Getting Started
  • Introduction
  • Udemy 101: Getting the Most From This Course
  • Installation: Getting Started
  • [Activity] WINDOWS: Installing and Using Anaconda & Course Materials
  • [Activity] MAC: Installing and Using Anaconda & Course Materials
  • [Activity] LINUX: Installing and Using Anaconda & Course Materials
  • Python Basics, Part 1 [Optional]
  • [Activity] Python Basics, Part 2 [Optional]
  • [Activity] Python Basics, Part 3 [Optional]
  • [Activity] Python Basics, Part 4 [Optional]
  • Introducing the Pandas Library [Optional]
Statistics and Probability Refresher, and Python Practise
  • Types of Data
  • Mean, Median, Mode
  • [Activity] Using mean, median, and mode in Python
  • [Activity] Variation and Standard Deviation
  • Probability Density Function; Probability Mass Function
  • Common Data Distributions
  • [Activity] Percentiles and Moments
  • [Activity] A Crash Course in matplotlib
  • [Activity] Advanced Visualization with Seaborn
  • [Activity] Covariance and Correlation
  • [Exercise] Conditional Probability
  • Exercise Solution: Conditional Probability of Purchase by Age
  • Bayes' Theorem
Predictive Models
  • [Activity] Linear Regression
  • [Activity] Polynomial Regression
  • [Activity] Multiple Regression, and Predicting Car Prices
  • Multi-Level Models
Machine Learning with Python
  • Supervised vs. Unsupervised Learning, and Train/Test
  • [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression
  • Bayesian Methods: Concepts
  • [Activity] Implementing a Spam Classifier with Naive Bayes
  • K-Means Clustering
  • [Activity] Clustering people based on income and age
  • Measuring Entropy
  • [Activity] WINDOWS: Installing Graphviz
  • [Activity] MAC: Installing Graphviz
  • [Activity] LINUX: Installing Graphviz
  • Decision Trees: Concepts
  • [Activity] Decision Trees: Predicting Hiring Decisions
  • Ensemble Learning
  • Support Vector Machines (SVM) Overview
  • [Activity] Using SVM to cluster people using scikit-learn
Recommender Systems
  • User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • [Activity] Finding Movie Similarities
  • [Activity] Improving the Results of Movie Similarities
  • [Activity] Making Movie Recommendations to People
  • [Exercise] Improve the recommender's results
More Data Mining and Machine Learning Techniques
  • K-Nearest-Neighbors: Concepts
  • [Activity] Using KNN to predict a rating for a movie
  • Dimensionality Reduction; Principal Component Analysis
  • [Activity] PCA Example with the Iris data set
  • Data Warehousing Overview: ETL and ELT
  • Reinforcement Learning
  • [Activity] Reinforcement Learning & Q-Learning with Gym
Dealing with Real-World Data
  • Bias/Variance Tradeoff
  • [Activity] K-Fold Cross-Validation to avoid overfitting
  • Data Cleaning and Normalization
  • [Activity] Cleaning web log data
  • Normalizing numerical data
  • [Activity] Detecting outliers
Apache Spark: Machine Learning on Big Data
  • Warning about Java 11 and Spark 2.4!
  • Spark installation notes for MacOS and Linux users
  • [Activity] Installing Spark - Part 1
  • [Activity] Installing Spark - Part 2
  • Spark Introduction
  • Spark and the Resilient Distributed Dataset (RDD)
  • Introducing MLLib
  • Introduction to Decision Trees in Spark
  • [Activity] K-Means Clustering in Spark
  • TF / IDF
  • [Activity] Searching Wikipedia with Spark
  • [Activity] Using the Spark 2.0 DataFrame API for MLLib
Experimental Design / ML in the Real World
  • Deploying Models to Real-Time Systems
  • A/B Testing Concepts
  • T-Tests and P-Values
  • [Activity] Hands-on With T-Tests
  • Determining How Long to Run an Experiment
  • A/B Test Gotchas
Deep Learning and Neural Networks
  • Deep Learning Pre-Requisites
  • The History of Artificial Neural Networks
  • [Activity] Deep Learning in the Tensorflow Playground
  • Deep Learning Details
  • Introducing Tensorflow
  • [Activity] Using Tensorflow, Part 1
  • [Activity] Using Tensorflow, Part 2
  • [Activity] Introducing Keras
  • [Activity] Using Keras to Predict Political Affiliations
  • Convolutional Neural Networks (CNN's)