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Data Science, Deep Learning, and Machine Learning with Python - Hands On!
Getting Started
Introduction
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
Python Basics, Part 2
Python Basics, Part 3
Python Basics, Part 4
Introducing the Pandas Library
Statistics and Probability Refresher, and Python Practice
Types Of Data
Mean, Median, Mode
Using mean, median, and mode in Python
Variation and Standard Deviation
Probability Density Function; Probability Mass Function
Common Data Distributions
Percentiles and Moments
A Crash Course in matplotlib
Advanced Visualization with Seaborn
Covariance and Correlation
Conditional Probability
Exercise Solution: Conditional Probability of Purchase by Age
Bayes' Theorem
Predictive Models
Linear Regression
Polynomial Regression
Multiple Regression, and Predicting Car Prices
Multi-Level Models
Machine Learning with Python
Supervised vs. Unsupervised Learning, and Train/Test
Using Train/Test to Prevent Overfitting a Polynomial Regression
Bayesian Methods: Concepts
Implementing a Spam Classifier with Naive Bayes
K-Means Clustering
Clustering people based on income and age
Measuring Entropy
WINDOWS: Installing GraphViz
MAC: Installing GraphViz
LINUX: Installing GraphViz
Decision Trees: Concepts
Decision Trees: Predicting Hiring Decisions
Ensemble Learning
XGBoost
Support Vector Machines (SVM) Overview
Using SVM to cluster people using scikit-learn
Recommender System
User-Based Collaborative Filtering
Item-Based Collaborative Filtering
Finding Movie Similarities
Improving the Results of Movie Similarities
Making Movie Recommendations to People
Improve the recommender's results
More Data Mining and Machine Learning Techniques
K-Nearest-Neighbors: Concepts
Using KNN to predict a rating for a movie
Dimensionality Reduction; Principal Component Analysis
PCA Example with the Iris data set
Data Warehousing Overview: ETL and ELT
Reinforcement Learning
Reinforcement Learning & Q-Learning with Gym
Understanding a Confusion Matrix
Measuring Classifiers (Precision, Recall, etc.)
Dealing with Real-World Data
Bias/Variance Tradeoff
K-Fold Cross-Validation to avoid overfitting
Data Cleaning and Normalization
Cleaning web log data
Normalizing numerical data
Detecting outliers
Feature Engineering and the Curse of Dimensionality
Imputation Techniques for Missing Data
Handling Unbalanced Data
Binning, Transforming, Encoding, Scaling, and Shuffling
Apache Spark: Machine Learning on Big Data
Installing Spark - Part 1
Installing Spark - Part 2
Spark Introduction
Spark and the Resilient Distributed Dataset (RDD)
Introducing MLLib
Decision Trees in Spark
K-Means Clustering in Spark
TF / IDF
Searching Wikipedia with Spark
Using the Spark DataFrame API for MLLib
Experimental Design
Deploying Models to Real-Time Systems
A/B Testing Concepts
T-Tests and P-Values
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
Deep Learning in the Tensorflow Playground
Deep Learning Details
Introducing Tensorflow
Using Tensorflow, Part 1
Using Tensorflow, Part 2
Introducing Keras
Using Keras to Predict Political Parties
Convolutional Neural Networks (CNN's)
Using CNN's for handwriting recognition
Recurrent Neural Networks (RNN's)
Using a RNN for sentiment analysis
Transfer Learning
Tuning Neural Networks
Deep Learning Regularization Techniques
The Ethics of Deep Learning
Generative Models
Variational Auto-Encoders (VAE's) - how they work
Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST
Generative Adversarial Networks (GAN's) - How they work
Generative Adversarial Networks (GAN's) - Playing with some demos
Generative Adversarial Networks (GAN's) - Hands-on with Fashion MNIST
Learning More about Deep Learning
Final Project
Your final project assignment
Final project review
You made it!
More to Explore
Python Basics, Part 4
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