Machine Learning, Data Science and Deep Learning with Python

  • Course provided by Udemy
  • Study type: Online
  • Starts: Anytime
  • Price: See latest price on Udemy
Udemy

Course Description

New! Updated for 2021 with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's)

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!

If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including:

  • Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras

  • Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's)

  • Data Visualization in Python with MatPlotLib and Seaborn

  • Transfer Learning

  • Sentiment analysis

  • Image recognition and classification

  • Regression analysis

  • K-Means Clustering

  • Principal Component Analysis

  • Train/Test and cross validation

  • Bayesian Methods

  • Decision Trees and Random Forests

  • Multiple Regression

  • Multi-Level Models

  • Support Vector Machines

  • Reinforcement Learning

  • Collaborative Filtering

  • K-Nearest Neighbor

  • Bias/Variance Tradeoff

  • Ensemble Learning

  • Term Frequency / Inverse Document Frequency

  • Experimental Design and A/B Tests

  • Feature Engineering

  • Hyperparameter Tuning


...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster.

If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs.

If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!


  • "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD


Who this course is for:

  • Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.
  • Technologists curious about how deep learning really works
  • Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.
  • If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.

Instructors

Founder, Sundog Education. Machine Learning Pro
  • 4.6 Instructor Rating
  • 107,459 Reviews
  • 504,784 Students
  • 23 Courses

Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford.

Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Founder, Sundog Education
  • 4.6 Instructor Rating
  • 103,208 Reviews
  • 459,249 Students
  • 14 Courses

Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.

Sundog Education Team
ST
  • 4.5 Instructor Rating
  • 102,162 Reviews
  • 452,418 Students
  • 15 Courses

Our mission is to make highly valuable skills in machine learning, big data, AI, and data science accessible at prices anyone in the world can afford. Our current online courses have reached over 500,000 students worldwide.

Expected Outcomes

  1. Build artificial neural networks with Tensorflow and Keras Classify images, data, and sentiments using deep learning Make predictions using linear regression, polynomial regression, and multivariate regression Data Visualization with MatPlotLib and Seaborn Implement machine learning at massive scale with Apache Spark's MLLib Understand reinforcement learning - and how to build a Pac-Man bot Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA Use train/test and K-Fold cross validation to choose and tune your models Build a movie recommender system using item-based and user-based collaborative filtering Clean your input data to remove outliers Design and evaluate A/B tests using T-Tests and P-Values Show more Show less Curated for the Udemy Business collection Course content 13 sections • 116 lectures • 15h 36m total length Expand all sections Getting Started 11 lectures • 1hr 6min Introduction Preview 02:41 Udemy 101: Getting the Most From This Course Preview 02:10 Installation: Getting Started 00:39 [Activity] WINDOWS: Installing and Using Anaconda & Course Materials 12:37 [Activity] MAC: Installing and Using Anaconda & Course Materials 10:02 [Activity] LINUX: Installing and Using Anaconda & Course Materials 10:57 Python Basics, Part 1 [Optional] 04:59 [Activity] Python Basics, Part 2 [Optional] Preview 05:17 [Activity] Python Basics, Part 3 [Optional] 02:46 [Activity] Python Basics, Part 4 [Optional] 04:02 Introducing the Pandas Library [Optional] 10:08 Statistics and Probability Refresher, and Python Practice 13 lectures • 2hr 2min Types of Data (Numerical, Categorical, Ordinal) Preview 06:58 Mean, Median, Mode 05:26 [Activity] Using mean, median, and mode in Python 08:21 [Activity] Variation and Standard Deviation Preview 11:12 Probability Density Function; Probability Mass Function 03:27 Common Data Distributions (Normal, Binomial, Poisson, etc) 07:45 [Activity] Percentiles and Moments 12:33 [Activity] A Crash Course in matplotlib 13:46 [Activity] Advanced Visualization with Seaborn 17:30 [Activity] Covariance and Correlation 11:31 [Exercise] Conditional Probability 16:04 Exercise Solution: Conditional Probability of Purchase by Age 02:20 Bayes' Theorem Preview 05:23 Predictive Models 4 lectures • 40min [Activity] Linear Regression Preview 11:01 [Activity] Polynomial Regression Preview 08:04 [Activity] Multiple Regression, and Predicting Car Prices 16:26 Multi-Level Models 04:36 Machine Learning with Python 16 lectures • 1hr 39min Supervised vs. Unsupervised Learning, and Train/Test 08:57 [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression 05:47 Bayesian Methods: Concepts 03:59 [Activity] Implementing a Spam Classifier with Naive Bayes Preview 08:05 K-Means Clustering 07:23 [Activity] Clustering people based on income and age 05:14 Measuring Entropy 03:09 [Activity] WINDOWS: Installing Graphviz 00:22 [Activity] MAC: Installing Graphviz 01:16 [Activity] LINUX: Installing Graphviz 00:54 Decision Trees: Concepts Preview 08:43 [Activity] Decision Trees: Predicting Hiring Decisions 09:47 Ensemble Learning 05:59 [Activity] XGBoost 15:29 Support Vector Machines (SVM) Overview 04:27 [Activity] Using SVM to cluster people using scikit-learn 09:29 Recommender Systems 6 lectures • 49min User-Based Collaborative Filtering Preview 07:57 Item-Based Collaborative Filtering 08:15 [Activity] Finding Movie Similarities using Cosine Similarity 09:08 [Activity] Improving the Results of Movie Similarities 07:59 [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering Preview 10:22 [Exercise] Improve the recommender's results 05:29 More Data Mining and Machine Learning Techniques 9 lectures • 1hr 18min K-Nearest-Neighbors: Concepts 03:44 [Activity] Using KNN to predict a rating for a movie 12:29 Dimensionality Reduction; Principal Component Analysis (PCA) 05:44 [Activity] PCA Example with the Iris data set 09:05 Data Warehousing Overview: ETL and ELT 09:05 Reinforcement Learning Preview 12:44 [Activity] Reinforcement Learning & Q-Learning with Gym 12:56 Understanding a Confusion Matrix 05:17 Measuring Classifiers (Precision, Recall, F1, ROC, AUC) 06:35 Dealing with Real-World Data 10 lectures • 1hr 12min Bias/Variance Tradeoff 06:15 [Activity] K-Fold Cross-Validation to avoid overfitting 10:26 Data Cleaning and Normalization Preview 07:10 [Activity] Cleaning web log data 10:56 Normalizing numerical data 03:22 [Activity] Detecting outliers 06:21 Feature Engineering and the Curse of Dimensionality 06:03 Imputation Techniques for Missing Data 07:48 Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE 05:35 Binning, Transforming, Encoding, Scaling, and Shuffling 07:51 Apache Spark: Machine Learning on Big Data 12 lectures • 1hr 33min Warning about Java 11 and Spark 3! 00:21 Spark installation notes for MacOS and Linux users 01:28 [Activity] Installing Spark - Part 1 06:59 [Activity] Installing Spark - Part 2 07:20 Spark Introduction 09:10 Spark and the Resilient Distributed Dataset (RDD) 11:42 Introducing MLLib 05:09 Introduction to Decision Trees in Spark Preview 16:15 [Activity] K-Means Clustering in Spark 11:23 TF / IDF Preview 06:44 [Activity] Searching Wikipedia with Spark 08:21 [Activity] Using the Spark 2.0 DataFrame API for MLLib 08:07 Experimental Design / ML in the Real World 6 lectures • 42min Deploying Models to Real-Time Systems 08:42 A/B Testing Concepts 08:23 T-Tests and P-Values 05:59 [Activity] Hands-on With T-Tests 06:04 Determining How Long to Run an Experiment 03:24 A/B Test Gotchas Preview 09:26 Deep Learning and Neural Networks 18 lectures • 3hr 2min Deep Learning Pre-Requisites 11:43 The History of Artificial Neural Networks Preview 11:14 [Activity] Deep Learning in the Tensorflow Playground 12:00 Deep Learning Details 09:29 Introducing Tensorflow 11:29 Important note about Tensorflow 2 00:23 [Activity] Using Tensorflow, Part 1 13:11 [Activity] Using Tensorflow, Part 2 12:03 [Activity] Introducing Keras 13:33 [Activity] Using Keras to Predict Political Affiliations 12:05 Convolutional Neural Networks (CNN's) 11:28 [Activity] Using CNN's for handwriting recognition 08:02 Recurrent Neural Networks (RNN's) 11:02 [Activity] Using a RNN for sentiment analysis 09:37 [Activity] Transfer Learning 12:14 Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters 04:39 Deep Learning Regularization with Dropout and Early Stopping 06:21 The Ethics of Deep Learning Preview 11:02 3 more sections Requirements You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software. Some prior coding or scripting experience is required. At least high school level math skills will be required. Description New! Updated for 2021 with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's) Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video , and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's) Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning Sentiment analysis Image recognition and classification Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multiple Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests Feature Engineering Hyperparameter Tuning ...and much more! There's also an entire section on machine learning with Apache Spark , which lets you scale up these techniques to "big data" analyzed on a computing cluster. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs. If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now! "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD Who this course is for: Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course. Technologists curious about how deep learning really works Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful. If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first. Show more Show less Featured review Mangesh Jagannath Thorat 5 courses 4 reviews Rating: 4.5 out of 5 a year ago Excellent course. Precise and well organized presentation. Complete course is filled with lot of learning not only theoretical but also practical examples. Mr.Frank is kind enough to share his practical experiences and actual problems faced by data scientist/ML engineer. The topic on"The ethics of deep learning" is really gold nugget that everyone must follow. Thank you Mr. Frank Kane and Udemy for this wonderful course. Show more Show less Instructors Sundog Education by Frank Kane Founder, Sundog Education. Machine Learning Pro 4.6 Instructor Rating 107,459 Reviews 504,784 Students 23 Courses Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb , developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing , data mining , and machine learning . In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. Show more Show less Frank Kane Founder, Sundog Education 4.6 Instructor Rating 103,208 Reviews 459,249 Students 14 Courses Frank spent 9 years at Amazon and IMDb , developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing , data mining , and machine learning . In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding. Show more Show less Sundog Education Team Sundog Education Team ST 4.5 Instructor Rating 102,162 Reviews 452,418 Students 15 Courses Our mission is to make highly valuable skills in machine learning, big data, AI, and data science accessible at prices anyone in the world can afford. Our current online courses have reached over 500,000 students worldwide. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6776ec3ead5f2d28',m:'9ff7b5f37f33b487c556bceef4cb0a74440d44ea-1627735517-1800-AToArRBxQOceZnAUiEUyXeuRZQqsOvmVqzf0W7X6CbBD7JJ1lzhbgQrtizMYhUXBDnMMvM6FP7Ss5bdzd5zOWUvRazNMK2VQsa0JHdhQt0wJ3+AU4oKzg7u5P+20Kc1Wi5MRNDjtSvNhclt+iVM81N5vF3GUr+uo0w1IXNJV/1I8sNvZkBAKI184cGARbkDv0w==',s:[0x1bfea6be2b,0xae75f88553],}})();
  2. Classify images, data, and sentiments using deep learning Make predictions using linear regression, polynomial regression, and multivariate regression Data Visualization with MatPlotLib and Seaborn Implement machine learning at massive scale with Apache Spark's MLLib Understand reinforcement learning - and how to build a Pac-Man bot Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA Use train/test and K-Fold cross validation to choose and tune your models Build a movie recommender system using item-based and user-based collaborative filtering Clean your input data to remove outliers Design and evaluate A/B tests using T-Tests and P-Values Show more Show less Curated for the Udemy Business collection Course content 13 sections • 116 lectures • 15h 36m total length Expand all sections Getting Started 11 lectures • 1hr 6min Introduction Preview 02:41 Udemy 101: Getting the Most From This Course Preview 02:10 Installation: Getting Started 00:39 [Activity] WINDOWS: Installing and Using Anaconda & Course Materials 12:37 [Activity] MAC: Installing and Using Anaconda & Course Materials 10:02 [Activity] LINUX: Installing and Using Anaconda & Course Materials 10:57 Python Basics, Part 1 [Optional] 04:59 [Activity] Python Basics, Part 2 [Optional] Preview 05:17 [Activity] Python Basics, Part 3 [Optional] 02:46 [Activity] Python Basics, Part 4 [Optional] 04:02 Introducing the Pandas Library [Optional] 10:08 Statistics and Probability Refresher, and Python Practice 13 lectures • 2hr 2min Types of Data (Numerical, Categorical, Ordinal) Preview 06:58 Mean, Median, Mode 05:26 [Activity] Using mean, median, and mode in Python 08:21 [Activity] Variation and Standard Deviation Preview 11:12 Probability Density Function; Probability Mass Function 03:27 Common Data Distributions (Normal, Binomial, Poisson, etc) 07:45 [Activity] Percentiles and Moments 12:33 [Activity] A Crash Course in matplotlib 13:46 [Activity] Advanced Visualization with Seaborn 17:30 [Activity] Covariance and Correlation 11:31 [Exercise] Conditional Probability 16:04 Exercise Solution: Conditional Probability of Purchase by Age 02:20 Bayes' Theorem Preview 05:23 Predictive Models 4 lectures • 40min [Activity] Linear Regression Preview 11:01 [Activity] Polynomial Regression Preview 08:04 [Activity] Multiple Regression, and Predicting Car Prices 16:26 Multi-Level Models 04:36 Machine Learning with Python 16 lectures • 1hr 39min Supervised vs. Unsupervised Learning, and Train/Test 08:57 [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression 05:47 Bayesian Methods: Concepts 03:59 [Activity] Implementing a Spam Classifier with Naive Bayes Preview 08:05 K-Means Clustering 07:23 [Activity] Clustering people based on income and age 05:14 Measuring Entropy 03:09 [Activity] WINDOWS: Installing Graphviz 00:22 [Activity] MAC: Installing Graphviz 01:16 [Activity] LINUX: Installing Graphviz 00:54 Decision Trees: Concepts Preview 08:43 [Activity] Decision Trees: Predicting Hiring Decisions 09:47 Ensemble Learning 05:59 [Activity] XGBoost 15:29 Support Vector Machines (SVM) Overview 04:27 [Activity] Using SVM to cluster people using scikit-learn 09:29 Recommender Systems 6 lectures • 49min User-Based Collaborative Filtering Preview 07:57 Item-Based Collaborative Filtering 08:15 [Activity] Finding Movie Similarities using Cosine Similarity 09:08 [Activity] Improving the Results of Movie Similarities 07:59 [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering Preview 10:22 [Exercise] Improve the recommender's results 05:29 More Data Mining and Machine Learning Techniques 9 lectures • 1hr 18min K-Nearest-Neighbors: Concepts 03:44 [Activity] Using KNN to predict a rating for a movie 12:29 Dimensionality Reduction; Principal Component Analysis (PCA) 05:44 [Activity] PCA Example with the Iris data set 09:05 Data Warehousing Overview: ETL and ELT 09:05 Reinforcement Learning Preview 12:44 [Activity] Reinforcement Learning & Q-Learning with Gym 12:56 Understanding a Confusion Matrix 05:17 Measuring Classifiers (Precision, Recall, F1, ROC, AUC) 06:35 Dealing with Real-World Data 10 lectures • 1hr 12min Bias/Variance Tradeoff 06:15 [Activity] K-Fold Cross-Validation to avoid overfitting 10:26 Data Cleaning and Normalization Preview 07:10 [Activity] Cleaning web log data 10:56 Normalizing numerical data 03:22 [Activity] Detecting outliers 06:21 Feature Engineering and the Curse of Dimensionality 06:03 Imputation Techniques for Missing Data 07:48 Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE 05:35 Binning, Transforming, Encoding, Scaling, and Shuffling 07:51 Apache Spark: Machine Learning on Big Data 12 lectures • 1hr 33min Warning about Java 11 and Spark 3! 00:21 Spark installation notes for MacOS and Linux users 01:28 [Activity] Installing Spark - Part 1 06:59 [Activity] Installing Spark - Part 2 07:20 Spark Introduction 09:10 Spark and the Resilient Distributed Dataset (RDD) 11:42 Introducing MLLib 05:09 Introduction to Decision Trees in Spark Preview 16:15 [Activity] K-Means Clustering in Spark 11:23 TF / IDF Preview 06:44 [Activity] Searching Wikipedia with Spark 08:21 [Activity] Using the Spark 2.0 DataFrame API for MLLib 08:07 Experimental Design / ML in the Real World 6 lectures • 42min Deploying Models to Real-Time Systems 08:42 A/B Testing Concepts 08:23 T-Tests and P-Values 05:59 [Activity] Hands-on With T-Tests 06:04 Determining How Long to Run an Experiment 03:24 A/B Test Gotchas Preview 09:26 Deep Learning and Neural Networks 18 lectures • 3hr 2min Deep Learning Pre-Requisites 11:43 The History of Artificial Neural Networks Preview 11:14 [Activity] Deep Learning in the Tensorflow Playground 12:00 Deep Learning Details 09:29 Introducing Tensorflow 11:29 Important note about Tensorflow 2 00:23 [Activity] Using Tensorflow, Part 1 13:11 [Activity] Using Tensorflow, Part 2 12:03 [Activity] Introducing Keras 13:33 [Activity] Using Keras to Predict Political Affiliations 12:05 Convolutional Neural Networks (CNN's) 11:28 [Activity] Using CNN's for handwriting recognition 08:02 Recurrent Neural Networks (RNN's) 11:02 [Activity] Using a RNN for sentiment analysis 09:37 [Activity] Transfer Learning 12:14 Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters 04:39 Deep Learning Regularization with Dropout and Early Stopping 06:21 The Ethics of Deep Learning Preview 11:02 3 more sections Requirements You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software. Some prior coding or scripting experience is required. At least high school level math skills will be required. Description New! Updated for 2021 with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's) Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video , and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's) Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning Sentiment analysis Image recognition and classification Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multiple Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests Feature Engineering Hyperparameter Tuning ...and much more! There's also an entire section on machine learning with Apache Spark , which lets you scale up these techniques to "big data" analyzed on a computing cluster. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs. If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now! "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD Who this course is for: Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course. Technologists curious about how deep learning really works Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful. If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first. Show more Show less Featured review Mangesh Jagannath Thorat 5 courses 4 reviews Rating: 4.5 out of 5 a year ago Excellent course. Precise and well organized presentation. Complete course is filled with lot of learning not only theoretical but also practical examples. Mr.Frank is kind enough to share his practical experiences and actual problems faced by data scientist/ML engineer. The topic on"The ethics of deep learning" is really gold nugget that everyone must follow. Thank you Mr. Frank Kane and Udemy for this wonderful course. Show more Show less Instructors Sundog Education by Frank Kane Founder, Sundog Education. Machine Learning Pro 4.6 Instructor Rating 107,459 Reviews 504,784 Students 23 Courses Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb , developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing , data mining , and machine learning . In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. Show more Show less Frank Kane Founder, Sundog Education 4.6 Instructor Rating 103,208 Reviews 459,249 Students 14 Courses Frank spent 9 years at Amazon and IMDb , developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing , data mining , and machine learning . In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding. Show more Show less Sundog Education Team Sundog Education Team ST 4.5 Instructor Rating 102,162 Reviews 452,418 Students 15 Courses Our mission is to make highly valuable skills in machine learning, big data, AI, and data science accessible at prices anyone in the world can afford. Our current online courses have reached over 500,000 students worldwide. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6776ec3ead5f2d28',m:'9ff7b5f37f33b487c556bceef4cb0a74440d44ea-1627735517-1800-AToArRBxQOceZnAUiEUyXeuRZQqsOvmVqzf0W7X6CbBD7JJ1lzhbgQrtizMYhUXBDnMMvM6FP7Ss5bdzd5zOWUvRazNMK2VQsa0JHdhQt0wJ3+AU4oKzg7u5P+20Kc1Wi5MRNDjtSvNhclt+iVM81N5vF3GUr+uo0w1IXNJV/1I8sNvZkBAKI184cGARbkDv0w==',s:[0x1bfea6be2b,0xae75f88553],}})();
  3. Make predictions using linear regression, polynomial regression, and multivariate regression Data Visualization with MatPlotLib and Seaborn Implement machine learning at massive scale with Apache Spark's MLLib Understand reinforcement learning - and how to build a Pac-Man bot Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA Use train/test and K-Fold cross validation to choose and tune your models Build a movie recommender system using item-based and user-based collaborative filtering Clean your input data to remove outliers Design and evaluate A/B tests using T-Tests and P-Values Show more Show less Curated for the Udemy Business collection Course content 13 sections • 116 lectures • 15h 36m total length Expand all sections Getting Started 11 lectures • 1hr 6min Introduction Preview 02:41 Udemy 101: Getting the Most From This Course Preview 02:10 Installation: Getting Started 00:39 [Activity] WINDOWS: Installing and Using Anaconda & Course Materials 12:37 [Activity] MAC: Installing and Using Anaconda & Course Materials 10:02 [Activity] LINUX: Installing and Using Anaconda & Course Materials 10:57 Python Basics, Part 1 [Optional] 04:59 [Activity] Python Basics, Part 2 [Optional] Preview 05:17 [Activity] Python Basics, Part 3 [Optional] 02:46 [Activity] Python Basics, Part 4 [Optional] 04:02 Introducing the Pandas Library [Optional] 10:08 Statistics and Probability Refresher, and Python Practice 13 lectures • 2hr 2min Types of Data (Numerical, Categorical, Ordinal) Preview 06:58 Mean, Median, Mode 05:26 [Activity] Using mean, median, and mode in Python 08:21 [Activity] Variation and Standard Deviation Preview 11:12 Probability Density Function; Probability Mass Function 03:27 Common Data Distributions (Normal, Binomial, Poisson, etc) 07:45 [Activity] Percentiles and Moments 12:33 [Activity] A Crash Course in matplotlib 13:46 [Activity] Advanced Visualization with Seaborn 17:30 [Activity] Covariance and Correlation 11:31 [Exercise] Conditional Probability 16:04 Exercise Solution: Conditional Probability of Purchase by Age 02:20 Bayes' Theorem Preview 05:23 Predictive Models 4 lectures • 40min [Activity] Linear Regression Preview 11:01 [Activity] Polynomial Regression Preview 08:04 [Activity] Multiple Regression, and Predicting Car Prices 16:26 Multi-Level Models 04:36 Machine Learning with Python 16 lectures • 1hr 39min Supervised vs. Unsupervised Learning, and Train/Test 08:57 [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression 05:47 Bayesian Methods: Concepts 03:59 [Activity] Implementing a Spam Classifier with Naive Bayes Preview 08:05 K-Means Clustering 07:23 [Activity] Clustering people based on income and age 05:14 Measuring Entropy 03:09 [Activity] WINDOWS: Installing Graphviz 00:22 [Activity] MAC: Installing Graphviz 01:16 [Activity] LINUX: Installing Graphviz 00:54 Decision Trees: Concepts Preview 08:43 [Activity] Decision Trees: Predicting Hiring Decisions 09:47 Ensemble Learning 05:59 [Activity] XGBoost 15:29 Support Vector Machines (SVM) Overview 04:27 [Activity] Using SVM to cluster people using scikit-learn 09:29 Recommender Systems 6 lectures • 49min User-Based Collaborative Filtering Preview 07:57 Item-Based Collaborative Filtering 08:15 [Activity] Finding Movie Similarities using Cosine Similarity 09:08 [Activity] Improving the Results of Movie Similarities 07:59 [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering Preview 10:22 [Exercise] Improve the recommender's results 05:29 More Data Mining and Machine Learning Techniques 9 lectures • 1hr 18min K-Nearest-Neighbors: Concepts 03:44 [Activity] Using KNN to predict a rating for a movie 12:29 Dimensionality Reduction; Principal Component Analysis (PCA) 05:44 [Activity] PCA Example with the Iris data set 09:05 Data Warehousing Overview: ETL and ELT 09:05 Reinforcement Learning Preview 12:44 [Activity] Reinforcement Learning & Q-Learning with Gym 12:56 Understanding a Confusion Matrix 05:17 Measuring Classifiers (Precision, Recall, F1, ROC, AUC) 06:35 Dealing with Real-World Data 10 lectures • 1hr 12min Bias/Variance Tradeoff 06:15 [Activity] K-Fold Cross-Validation to avoid overfitting 10:26 Data Cleaning and Normalization Preview 07:10 [Activity] Cleaning web log data 10:56 Normalizing numerical data 03:22 [Activity] Detecting outliers 06:21 Feature Engineering and the Curse of Dimensionality 06:03 Imputation Techniques for Missing Data 07:48 Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE 05:35 Binning, Transforming, Encoding, Scaling, and Shuffling 07:51 Apache Spark: Machine Learning on Big Data 12 lectures • 1hr 33min Warning about Java 11 and Spark 3! 00:21 Spark installation notes for MacOS and Linux users 01:28 [Activity] Installing Spark - Part 1 06:59 [Activity] Installing Spark - Part 2 07:20 Spark Introduction 09:10 Spark and the Resilient Distributed Dataset (RDD) 11:42 Introducing MLLib 05:09 Introduction to Decision Trees in Spark Preview 16:15 [Activity] K-Means Clustering in Spark 11:23 TF / IDF Preview 06:44 [Activity] Searching Wikipedia with Spark 08:21 [Activity] Using the Spark 2.0 DataFrame API for MLLib 08:07 Experimental Design / ML in the Real World 6 lectures • 42min Deploying Models to Real-Time Systems 08:42 A/B Testing Concepts 08:23 T-Tests and P-Values 05:59 [Activity] Hands-on With T-Tests 06:04 Determining How Long to Run an Experiment 03:24 A/B Test Gotchas Preview 09:26 Deep Learning and Neural Networks 18 lectures • 3hr 2min Deep Learning Pre-Requisites 11:43 The History of Artificial Neural Networks Preview 11:14 [Activity] Deep Learning in the Tensorflow Playground 12:00 Deep Learning Details 09:29 Introducing Tensorflow 11:29 Important note about Tensorflow 2 00:23 [Activity] Using Tensorflow, Part 1 13:11 [Activity] Using Tensorflow, Part 2 12:03 [Activity] Introducing Keras 13:33 [Activity] Using Keras to Predict Political Affiliations 12:05 Convolutional Neural Networks (CNN's) 11:28 [Activity] Using CNN's for handwriting recognition 08:02 Recurrent Neural Networks (RNN's) 11:02 [Activity] Using a RNN for sentiment analysis 09:37 [Activity] Transfer Learning 12:14 Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters 04:39 Deep Learning Regularization with Dropout and Early Stopping 06:21 The Ethics of Deep Learning Preview 11:02 3 more sections Requirements You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software. Some prior coding or scripting experience is required. At least high school level math skills will be required. Description New! Updated for 2021 with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's) Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video , and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's) Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning Sentiment analysis Image recognition and classification Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multiple Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests Feature Engineering Hyperparameter Tuning ...and much more! There's also an entire section on machine learning with Apache Spark , which lets you scale up these techniques to "big data" analyzed on a computing cluster. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs. If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now! "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD Who this course is for: Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course. Technologists curious about how deep learning really works Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful. If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first. Show more Show less Featured review Mangesh Jagannath Thorat 5 courses 4 reviews Rating: 4.5 out of 5 a year ago Excellent course. Precise and well organized presentation. Complete course is filled with lot of learning not only theoretical but also practical examples. Mr.Frank is kind enough to share his practical experiences and actual problems faced by data scientist/ML engineer. The topic on"The ethics of deep learning" is really gold nugget that everyone must follow. Thank you Mr. Frank Kane and Udemy for this wonderful course. Show more Show less Instructors Sundog Education by Frank Kane Founder, Sundog Education. Machine Learning Pro 4.6 Instructor Rating 107,459 Reviews 504,784 Students 23 Courses Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb , developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing , data mining , and machine learning . In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. Show more Show less Frank Kane Founder, Sundog Education 4.6 Instructor Rating 103,208 Reviews 459,249 Students 14 Courses Frank spent 9 years at Amazon and IMDb , developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing , data mining , and machine learning . In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding. Show more Show less Sundog Education Team Sundog Education Team ST 4.5 Instructor Rating 102,162 Reviews 452,418 Students 15 Courses Our mission is to make highly valuable skills in machine learning, big data, AI, and data science accessible at prices anyone in the world can afford. Our current online courses have reached over 500,000 students worldwide. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6776ec3ead5f2d28',m:'9ff7b5f37f33b487c556bceef4cb0a74440d44ea-1627735517-1800-AToArRBxQOceZnAUiEUyXeuRZQqsOvmVqzf0W7X6CbBD7JJ1lzhbgQrtizMYhUXBDnMMvM6FP7Ss5bdzd5zOWUvRazNMK2VQsa0JHdhQt0wJ3+AU4oKzg7u5P+20Kc1Wi5MRNDjtSvNhclt+iVM81N5vF3GUr+uo0w1IXNJV/1I8sNvZkBAKI184cGARbkDv0w==',s:[0x1bfea6be2b,0xae75f88553],}})();
  4. Data Visualization with MatPlotLib and Seaborn Implement machine learning at massive scale with Apache Spark's MLLib Understand reinforcement learning - and how to build a Pac-Man bot Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA Use train/test and K-Fold cross validation to choose and tune your models Build a movie recommender system using item-based and user-based collaborative filtering Clean your input data to remove outliers Design and evaluate A/B tests using T-Tests and P-Values Show more Show less Curated for the Udemy Business collection Course content 13 sections • 116 lectures • 15h 36m total length Expand all sections Getting Started 11 lectures • 1hr 6min Introduction Preview 02:41 Udemy 101: Getting the Most From This Course Preview 02:10 Installation: Getting Started 00:39 [Activity] WINDOWS: Installing and Using Anaconda & Course Materials 12:37 [Activity] MAC: Installing and Using Anaconda & Course Materials 10:02 [Activity] LINUX: Installing and Using Anaconda & Course Materials 10:57 Python Basics, Part 1 [Optional] 04:59 [Activity] Python Basics, Part 2 [Optional] Preview 05:17 [Activity] Python Basics, Part 3 [Optional] 02:46 [Activity] Python Basics, Part 4 [Optional] 04:02 Introducing the Pandas Library [Optional] 10:08 Statistics and Probability Refresher, and Python Practice 13 lectures • 2hr 2min Types of Data (Numerical, Categorical, Ordinal) Preview 06:58 Mean, Median, Mode 05:26 [Activity] Using mean, median, and mode in Python 08:21 [Activity] Variation and Standard Deviation Preview 11:12 Probability Density Function; Probability Mass Function 03:27 Common Data Distributions (Normal, Binomial, Poisson, etc) 07:45 [Activity] Percentiles and Moments 12:33 [Activity] A Crash Course in matplotlib 13:46 [Activity] Advanced Visualization with Seaborn 17:30 [Activity] Covariance and Correlation 11:31 [Exercise] Conditional Probability 16:04 Exercise Solution: Conditional Probability of Purchase by Age 02:20 Bayes' Theorem Preview 05:23 Predictive Models 4 lectures • 40min [Activity] Linear Regression Preview 11:01 [Activity] Polynomial Regression Preview 08:04 [Activity] Multiple Regression, and Predicting Car Prices 16:26 Multi-Level Models 04:36 Machine Learning with Python 16 lectures • 1hr 39min Supervised vs. Unsupervised Learning, and Train/Test 08:57 [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression 05:47 Bayesian Methods: Concepts 03:59 [Activity] Implementing a Spam Classifier with Naive Bayes Preview 08:05 K-Means Clustering 07:23 [Activity] Clustering people based on income and age 05:14 Measuring Entropy 03:09 [Activity] WINDOWS: Installing Graphviz 00:22 [Activity] MAC: Installing Graphviz 01:16 [Activity] LINUX: Installing Graphviz 00:54 Decision Trees: Concepts Preview 08:43 [Activity] Decision Trees: Predicting Hiring Decisions 09:47 Ensemble Learning 05:59 [Activity] XGBoost 15:29 Support Vector Machines (SVM) Overview 04:27 [Activity] Using SVM to cluster people using scikit-learn 09:29 Recommender Systems 6 lectures • 49min User-Based Collaborative Filtering Preview 07:57 Item-Based Collaborative Filtering 08:15 [Activity] Finding Movie Similarities using Cosine Similarity 09:08 [Activity] Improving the Results of Movie Similarities 07:59 [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering Preview 10:22 [Exercise] Improve the recommender's results 05:29 More Data Mining and Machine Learning Techniques 9 lectures • 1hr 18min K-Nearest-Neighbors: Concepts 03:44 [Activity] Using KNN to predict a rating for a movie 12:29 Dimensionality Reduction; Principal Component Analysis (PCA) 05:44 [Activity] PCA Example with the Iris data set 09:05 Data Warehousing Overview: ETL and ELT 09:05 Reinforcement Learning Preview 12:44 [Activity] Reinforcement Learning & Q-Learning with Gym 12:56 Understanding a Confusion Matrix 05:17 Measuring Classifiers (Precision, Recall, F1, ROC, AUC) 06:35 Dealing with Real-World Data 10 lectures • 1hr 12min Bias/Variance Tradeoff 06:15 [Activity] K-Fold Cross-Validation to avoid overfitting 10:26 Data Cleaning and Normalization Preview 07:10 [Activity] Cleaning web log data 10:56 Normalizing numerical data 03:22 [Activity] Detecting outliers 06:21 Feature Engineering and the Curse of Dimensionality 06:03 Imputation Techniques for Missing Data 07:48 Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE 05:35 Binning, Transforming, Encoding, Scaling, and Shuffling 07:51 Apache Spark: Machine Learning on Big Data 12 lectures • 1hr 33min Warning about Java 11 and Spark 3! 00:21 Spark installation notes for MacOS and Linux users 01:28 [Activity] Installing Spark - Part 1 06:59 [Activity] Installing Spark - Part 2 07:20 Spark Introduction 09:10 Spark and the Resilient Distributed Dataset (RDD) 11:42 Introducing MLLib 05:09 Introduction to Decision Trees in Spark Preview 16:15 [Activity] K-Means Clustering in Spark 11:23 TF / IDF Preview 06:44 [Activity] Searching Wikipedia with Spark 08:21 [Activity] Using the Spark 2.0 DataFrame API for MLLib 08:07 Experimental Design / ML in the Real World 6 lectures • 42min Deploying Models to Real-Time Systems 08:42 A/B Testing Concepts 08:23 T-Tests and P-Values 05:59 [Activity] Hands-on With T-Tests 06:04 Determining How Long to Run an Experiment 03:24 A/B Test Gotchas Preview 09:26 Deep Learning and Neural Networks 18 lectures • 3hr 2min Deep Learning Pre-Requisites 11:43 The History of Artificial Neural Networks Preview 11:14 [Activity] Deep Learning in the Tensorflow Playground 12:00 Deep Learning Details 09:29 Introducing Tensorflow 11:29 Important note about Tensorflow 2 00:23 [Activity] Using Tensorflow, Part 1 13:11 [Activity] Using Tensorflow, Part 2 12:03 [Activity] Introducing Keras 13:33 [Activity] Using Keras to Predict Political Affiliations 12:05 Convolutional Neural Networks (CNN's) 11:28 [Activity] Using CNN's for handwriting recognition 08:02 Recurrent Neural Networks (RNN's) 11:02 [Activity] Using a RNN for sentiment analysis 09:37 [Activity] Transfer Learning 12:14 Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters 04:39 Deep Learning Regularization with Dropout and Early Stopping 06:21 The Ethics of Deep Learning Preview 11:02 3 more sections Requirements You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software. Some prior coding or scripting experience is required. At least high school level math skills will be required. Description New! Updated for 2021 with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's) Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video , and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's) Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning Sentiment analysis Image recognition and classification Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multiple Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests Feature Engineering Hyperparameter Tuning ...and much more! There's also an entire section on machine learning with Apache Spark , which lets you scale up these techniques to "big data" analyzed on a computing cluster. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs. If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now! "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD Who this course is for: Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course. Technologists curious about how deep learning really works Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful. If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first. Show more Show less Featured review Mangesh Jagannath Thorat 5 courses 4 reviews Rating: 4.5 out of 5 a year ago Excellent course. Precise and well organized presentation. Complete course is filled with lot of learning not only theoretical but also practical examples. Mr.Frank is kind enough to share his practical experiences and actual problems faced by data scientist/ML engineer. The topic on"The ethics of deep learning" is really gold nugget that everyone must follow. Thank you Mr. Frank Kane and Udemy for this wonderful course. Show more Show less Instructors Sundog Education by Frank Kane Founder, Sundog Education. Machine Learning Pro 4.6 Instructor Rating 107,459 Reviews 504,784 Students 23 Courses Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb , developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing , data mining , and machine learning . In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. Show more Show less Frank Kane Founder, Sundog Education 4.6 Instructor Rating 103,208 Reviews 459,249 Students 14 Courses Frank spent 9 years at Amazon and IMDb , developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing , data mining , and machine learning . In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding. Show more Show less Sundog Education Team Sundog Education Team ST 4.5 Instructor Rating 102,162 Reviews 452,418 Students 15 Courses Our mission is to make highly valuable skills in machine learning, big data, AI, and data science accessible at prices anyone in the world can afford. Our current online courses have reached over 500,000 students worldwide. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6776ec3ead5f2d28',m:'9ff7b5f37f33b487c556bceef4cb0a74440d44ea-1627735517-1800-AToArRBxQOceZnAUiEUyXeuRZQqsOvmVqzf0W7X6CbBD7JJ1lzhbgQrtizMYhUXBDnMMvM6FP7Ss5bdzd5zOWUvRazNMK2VQsa0JHdhQt0wJ3+AU4oKzg7u5P+20Kc1Wi5MRNDjtSvNhclt+iVM81N5vF3GUr+uo0w1IXNJV/1I8sNvZkBAKI184cGARbkDv0w==',s:[0x1bfea6be2b,0xae75f88553],}})();
  5. Implement machine learning at massive scale with Apache Spark's MLLib Understand reinforcement learning - and how to build a Pac-Man bot Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA Use train/test and K-Fold cross validation to choose and tune your models Build a movie recommender system using item-based and user-based collaborative filtering Clean your input data to remove outliers Design and evaluate A/B tests using T-Tests and P-Values Show more Show less Curated for the Udemy Business collection Course content 13 sections • 116 lectures • 15h 36m total length Expand all sections Getting Started 11 lectures • 1hr 6min Introduction Preview 02:41 Udemy 101: Getting the Most From This Course Preview 02:10 Installation: Getting Started 00:39 [Activity] WINDOWS: Installing and Using Anaconda & Course Materials 12:37 [Activity] MAC: Installing and Using Anaconda & Course Materials 10:02 [Activity] LINUX: Installing and Using Anaconda & Course Materials 10:57 Python Basics, Part 1 [Optional] 04:59 [Activity] Python Basics, Part 2 [Optional] Preview 05:17 [Activity] Python Basics, Part 3 [Optional] 02:46 [Activity] Python Basics, Part 4 [Optional] 04:02 Introducing the Pandas Library [Optional] 10:08 Statistics and Probability Refresher, and Python Practice 13 lectures • 2hr 2min Types of Data (Numerical, Categorical, Ordinal) Preview 06:58 Mean, Median, Mode 05:26 [Activity] Using mean, median, and mode in Python 08:21 [Activity] Variation and Standard Deviation Preview 11:12 Probability Density Function; Probability Mass Function 03:27 Common Data Distributions (Normal, Binomial, Poisson, etc) 07:45 [Activity] Percentiles and Moments 12:33 [Activity] A Crash Course in matplotlib 13:46 [Activity] Advanced Visualization with Seaborn 17:30 [Activity] Covariance and Correlation 11:31 [Exercise] Conditional Probability 16:04 Exercise Solution: Conditional Probability of Purchase by Age 02:20 Bayes' Theorem Preview 05:23 Predictive Models 4 lectures • 40min [Activity] Linear Regression Preview 11:01 [Activity] Polynomial Regression Preview 08:04 [Activity] Multiple Regression, and Predicting Car Prices 16:26 Multi-Level Models 04:36 Machine Learning with Python 16 lectures • 1hr 39min Supervised vs. Unsupervised Learning, and Train/Test 08:57 [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression 05:47 Bayesian Methods: Concepts 03:59 [Activity] Implementing a Spam Classifier with Naive Bayes Preview 08:05 K-Means Clustering 07:23 [Activity] Clustering people based on income and age 05:14 Measuring Entropy 03:09 [Activity] WINDOWS: Installing Graphviz 00:22 [Activity] MAC: Installing Graphviz 01:16 [Activity] LINUX: Installing Graphviz 00:54 Decision Trees: Concepts Preview 08:43 [Activity] Decision Trees: Predicting Hiring Decisions 09:47 Ensemble Learning 05:59 [Activity] XGBoost 15:29 Support Vector Machines (SVM) Overview 04:27 [Activity] Using SVM to cluster people using scikit-learn 09:29 Recommender Systems 6 lectures • 49min User-Based Collaborative Filtering Preview 07:57 Item-Based Collaborative Filtering 08:15 [Activity] Finding Movie Similarities using Cosine Similarity 09:08 [Activity] Improving the Results of Movie Similarities 07:59 [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering Preview 10:22 [Exercise] Improve the recommender's results 05:29 More Data Mining and Machine Learning Techniques 9 lectures • 1hr 18min K-Nearest-Neighbors: Concepts 03:44 [Activity] Using KNN to predict a rating for a movie 12:29 Dimensionality Reduction; Principal Component Analysis (PCA) 05:44 [Activity] PCA Example with the Iris data se