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Data Science, Deep Learning, and Machine Learning with Python - Hands On!
Getting Started
Introduction (2:41)
Installation: Getting Started
[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 (4:59)
Python Basics, Part 2 (5:17)
Python Basics, Part 3 (2:46)
Python Basics, Part 4 (4:02)
Introducing the Pandas Library (10:08)
Statistics and Probability Refresher, and Python Practice
Types Of Data (6:58)
Mean, Median, Mode (5:26)
Using mean, median, and mode in Python (8:20)
Variation and Standard Deviation (11:12)
Probability Density Function; Probability Mass Function (3:27)
Common Data Distributions (7:45)
Percentiles and Moments (12:33)
A Crash Course in matplotlib (13:46)
Advanced Visualization with Seaborn (17:30)
Covariance and Correlation (11:31)
Conditional Probability (16:04)
Exercise Solution: Conditional Probability of Purchase by Age (2:20)
Bayes' Theorem (5:23)
Predictive Models
Linear Regression (11:01)
Polynomial Regression (8:04)
Multiple Regression, and Predicting Car Prices (16:26)
Multi-Level Models (4:36)
Machine Learning with Python
Supervised vs. Unsupervised Learning, and Train/Test (8:57)
Using Train/Test to Prevent Overfitting a Polynomial Regression (5:47)
Bayesian Methods: Concepts (3:59)
Implementing a Spam Classifier with Naive Bayes (8:05)
K-Means Clustering (7:23)
Clustering people based on income and age (5:14)
Measuring Entropy (3:09)
WINDOWS: Installing GraphViz (0:22)
MAC: Installing GraphViz (1:16)
LINUX: Installing GraphViz (0:54)
Decision Trees: Concepts (8:43)
Decision Trees: Predicting Hiring Decisions (9:47)
Ensemble Learning (5:59)
XGBoost (15:29)
Support Vector Machines (SVM) Overview (4:27)
Using SVM to cluster people using scikit-learn (8:38)
Recommender System
User-Based Collaborative Filtering (7:57)
Item-Based Collaborative Filtering (8:15)
Finding Movie Similarities (9:08)
Improving the Results of Movie Similarities (7:59)
Making Movie Recommendations to People (10:22)
Improve the recommender's results (5:29)
More Data Mining and Machine Learning Techniques
K-Nearest-Neighbors: Concepts (3:44)
Using KNN to predict a rating for a movie (12:29)
Dimensionality Reduction; Principal Component Analysis (5:44)
PCA Example with the Iris data set (9:05)
Data Warehousing Overview: ETL and ELT (9:05)
Reinforcement Learning (12:44)
Reinforcement Learning & Q-Learning with Gym (12:56)
Understanding a Confusion Matrix (5:17)
Measuring Classifiers (Precision, Recall, etc.) (6:35)
Dealing with Real-World Data
Bias/Variance Tradeoff (6:15)
K-Fold Cross-Validation to avoid overfitting (10:26)
Data Cleaning and Normalization (7:10)
Cleaning web log data (10:56)
Normalizing numerical data (3:22)
Detecting outliers (6:21)
Feature Engineering and the Curse of Dimensionality (6:03)
Imputation Techniques for Missing Data (7:48)
Handling Unbalanced Data (5:35)
Binning, Transforming, Encoding, Scaling, and Shuffling (7:51)
Apache Spark: Machine Learning on Big Data
Installing Spark - Part 1 (6:59)
Installing Spark - Part 2 (7:20)
Spark Introduction (9:10)
Spark and the Resilient Distributed Dataset (RDD) (11:42)
Introducing MLLib (5:09)
Decision Trees in Spark (16:15)
K-Means Clustering in Spark (11:23)
TF / IDF (6:43)
Searching Wikipedia with Spark (8:21)
Using the Spark DataFrame API for MLLib (8:07)
Experimental Design
Deploying Models to Real-Time Systems (8:42)
A/B Testing Concepts (8:23)
T-Tests and P-Values (5:59)
Hands-on With T-Tests (6:04)
Determining How Long to Run an Experiment (3:24)
A/B Test Gotchas (9:26)
Deep Learning and Neural Networks
Deep Learning Pre-Requisites (11:43)
The History of Artificial Neural Networks (11:14)
Deep Learning in the Tensorflow Playground (12:00)
Deep Learning Details (9:29)
Introducing Tensorflow (11:29)
Using Tensorflow, Part 1 (13:10)
Using Tensorflow, Part 2 (12:03)
Introducing Keras (13:33)
Using Keras to Predict Political Parties (12:05)
Convolutional Neural Networks (CNN's) (11:27)
Using CNN's for handwriting recognition (8:02)
Recurrent Neural Networks (RNN's) (11:02)
Using a RNN for sentiment analysis (9:37)
Transfer Learning (12:14)
Tuning Neural Networks (4:39)
Deep Learning Regularization Techniques (6:21)
The Ethics of Deep Learning (11:02)
Generative Models
Variational Auto-Encoders (VAE's) - how they work (10:23)
Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST (26:31)
Generative Adversarial Networks (GAN's) - How they work (7:39)
Generative Adversarial Networks (GAN's) - Playing with some demos (11:22)
Generative Adversarial Networks (GAN's) - Hands-on with Fashion MNIST (15:20)
Learning More about Deep Learning (1:44)
Final Project
Your final project assignment (6:19)
Final project review (10:26)
You made it!
More to Explore (2:59)
Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST
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