Machine Learning, Data Science and Deep Learning with Python

Getting Started 9 Topics

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Introduction

[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]

Python Basics, Part 2 [Optional]

Python Basics, Part 3 [Optional]

Python Basics, Part 4 [Optional]

Introducing the Pandas Library [Optional]

Statistics and Probability Refresher, Python Practice 13 Topics

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Types of Data (Numerical, Categorical, Ordinal)

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 (Normal, Binomial, Poisson, etc)

[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 Model 4 Topics

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[Activity] Linear Regression

[Activity] Polynomial Regression

[Activity] Multiple Regression, and Predicting Car Prices

Multi-Level Models

Machine Learning with Python 16 Topics

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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- Mean 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

[Activity] XG Boost

Support Vector Machines (SVM) Overview

[Activity] Using SVM to cluster people using scikit-learn

Recommender System 6 Topics

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User-Based Collaborative Filtering

Item-Based Collaborative Filtering

[Activity] Finding Movie Similarities using Cosine Similarity

[Activity] Improving the Results of Movie Similarities

[Activity] Making Movie Recommendations with Item-Based Collaborative Filtering

[Exercise] Improve the recommender’s results

More Data Mining and Machine Learning Techniques 12 Topics

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K-Nearest-Neighbors: Concepts

[Activity] Using KNN to predict a rating for a movie

Dimensionality Reduction; Principal Component Analysis (PCA)

[Activity] PCA Example with the Iris data set

Data Warehousing Overview: ETL and ELT

Reinforcement Learning

Cat and Mouse Example

Pac-Man Example

Python Markov Decision Process Toolbox

[Activity] Reinforcement Learning & Q-Learning with Gym

Understanding a Confusion Matrix

Measuring Classifiers (Precision, Recall, F1, ROC, AUC)

Dealing with Real-World Data 10 Topics

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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

Feature Engineering and the Curse of Dimensionality

Imputation Techniques for Missing Data

Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE

Binning, Transforming, Encoding, Scaling, and Shuffling

Apache Spark: Machine Learning on Big Data 12 Topics

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Warning about Java 11 and Spark 3!

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 Tress 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 6 Topics

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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 Gotchas

Deep Learning and Neural Networks 19 Topics

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Deep Learning Pre-Requisites

The History of Artificial Neural Networks

[Activity] Deep Learning in the Tensorflow Playground

Deep Learning Details

Introducing Tensorflow

Important note about Tensorflow 2

[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)

[Activity] Using CNN’s for handwriting recognition

Recurrent Neural Networks (RNN’s)

[Activity] Using a RNN for sentiment analysis

[Activity] Transfer Learning

Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters

Deep Learning Regularization with Dropout and Early Stopping

The Ethics of Deep Learning

Learning More about Deep Learning

Final Project 2 Topics

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Your final project assignment: Mammogram Classification

Final project review

Machine Learning,Data Science and Deep Learning With Python

Course Content

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