Python: Machine Learning and Data Science Video Course

Curriculum For This Course

• 1. Getting Started 6 Videos 00:48:12
  • [Activity] Getting What You Need 05:10
  • [Activity] Installing Enthought Canopy 15:58
  • Python Basics, Part 1 [Optional] 09:41
  • [Activity] Python Basics, Part 2 [Optional] 03:55
  • Running Python Scripts [Optional] 10:15
  • Introducing the Pandas Library [Optional] 10:14
• 2. Statistics and Probability Refresher, and Python Practise 12 Videos 01:39:05
  • Types of Data 06:58
  • Mean, Median, Mode 05:26
  • [Activity] Using mean, median, and mode in Python 08:30
  • [Activity] Variation and Standard Deviation 11:12
  • Probability Density Function; Probability Mass Function 03:27
  • Common Data Distributions 07:45
  • [Activity] Percentiles and Moments 12:33
  • [Activity] A Crash Course in matplotlib 13:46
  • [Activity] Covariance and Correlation 11:31
  • [Exercise] Conditional Probability 10:16
  • Exercise Solution: Conditional Probability of Purchase by Age 02:18
  • Bayes' Theorem 05:23
• 3. Predictive Models 4 Videos 00:33:33
  • [Activity] Linear Regression 11:01
  • [Activity] Polynomial Regression 08:04
  • [Activity] Multivariate Regression, and Predicting Car Prices 09:53
  • Multi-Level Models 04:36
• 4. Machine Learning with Python 12 Videos 01:17:06
  • 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 08:05
  • K-Means Clustering 07:23
  • [Activity] Clustering people based on income and age 05:14
  • Measuring Entropy 03:09
  • Decision Trees: Concepts 08:43
  • [Activity] Decision Trees: Predicting Hiring Decisions 09:47
  • Ensemble Learning 05:59
  • Support Vector Machines (SVM) Overview 04:27
  • [Activity] Using SVM to cluster people using scikit-learn 05:36
• 5. Recommender Systems 6 Videos 00:49:10
  • User-Based Collaborative Filtering 07:57
  • Item-Based Collaborative Filtering 08:15
  • [Activity] Finding Movie Similarities 09:08
  • [Activity] Improving the Results of Movie Similarities 07:59
  • [Activity] Making Movie Recommendations to People 10:22
  • [Exercise] Improve the recommender's results 05:29
• 6. More Data Mining and Machine Learning Techniques 6 Videos 00:52:51
  • K-Nearest-Neighbors: Concepts 03:44
  • [Activity] Using KNN to predict a rating for a movie 12:29
  • Dimensionality Reduction; Principal Component Analysis 05:44
  • [Activity] PCA Example with the Iris data set 09:05
  • Data Warehousing Overview: ETL and ELT 09:05
  • Reinforcement Learning 12:44
• 7. Dealing with Real-World Data 6 Videos 00:45:38
  • Bias/Variance Tradeoff 06:15
  • [Activity] K-Fold Cross-Validation to avoid overfitting 10:55
  • Data Cleaning and Normalization 07:10
  • [Activity] Cleaning web log data 10:56
  • Normalizing numerical data 03:22
  • [Activity] Detecting outliers 07:00
• 8. Apache Spark: Machine Learning on Big Data 10 Videos 01:36:31
  • [Activity] Installing Spark - Part 1 07:02
  • [Activity] Installing Spark - Part 2 13:29
  • Spark Introduction 09:10
  • Spark and the Resilient Distributed Dataset (RDD) 11:42
  • Introducing MLLib 05:09
  • [Activity] Decision Trees in Spark 16:00
  • [Activity] K-Means Clustering in Spark 11:07
  • TF / IDF 06:44
  • [Activity] Searching Wikipedia with Spark 08:11
  • [Activity] Using the Spark 2.0 DataFrame API for MLLib 07:57
• 9. Experimental Design 5 Videos 00:33:16
  • 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 09:26
• 10. Deep Learning and Neural Networks 15 Videos 02:39:12
  • Deep Learning Pre-Requisites 10:51
  • The History of Artificial Neural Networks 11:15
  • [Activity] Deep Learning in the Tensorflow Playground 12:00
  • Deep Learning Details 09:29
  • Introducing Tensorflow 12:39
  • [Activity] Using Tensorflow, Part 1 09:37
  • [Activity] Using Tensorflow, Part 2 13:27
  • [Activity] Introducing Keras 14:22
  • [Activity] Using Keras to Predict Political Affiliations 12:30
  • Convolutional Neural Networks (CNN's) 11:28
  • [Activity] Using CNN's for handwriting recognition 08:15
  • Recurrent Neural Networks (RNN's) 11:02
  • [Activity] Using a RNN for sentiment analysis 10:15
  • The Ethics of Deep Learning 11:02
  • Learning More about Deep Learning 01:45
• 11. Final Project 2 Videos 00:16:52
  • Your final project assignment 06:26
  • Final project review 08:59