Data Science and Machine Learning with Python - Hands On!

About Data Science and Machine Learning with Python - Hands On!

Introduction

Windows Setup Instructions

Mac Setup Instructions

Linux Setup Instructions

Python Basics, Part 1

Python Basics, Part 2

Python Basics, Part 3

Python Basics, Part 4

Intro to Pandas

Types of Data

Mean, Median, Mode

Using mean, media, 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

Data Visualization with Seaborn

Covariance and Correlation

Exercise: Conditional Probability

Exercise Solution: Conditional Probability

Bayes' Theorem

Linear Regression

Polynomial Regression

Multiple Regression

Multi-Level Models

Supervised vs. Unsupervised Learning, Train / Test

Using Train/Test to Prevent Overfitting

Bayesian Methods: Concepts

Implementing a Spam Classifier with Naive Bayes

K-Means Clustering

Clustering People by Income and Age

Measuring Entropy

Windows: Installing Graphviz

Mac: Installing Graphviz

Linux: Installing Graphviz

Decision Trees: Concepts

Decision Trees: Predicting Hiring Decisions

Ensemble Learning

[Activity] XGBoost

Support Vector Machines (SVM) Overview

Using SVM to Cluster People

User-Based Collaborative Filtering

Item-Based Collaborative Filtering

Finding Movie Similarities

Improving the Results of Movie Similarities

Making Movie Recommendations to People

Improving the Recommender's Results

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; ETL and ELT

Reinforcement Learning

Hands-On with Q-Learning

Understanding a Confusion Matrix

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

Bias / Variance Tradeoff

K-Fold Cross Validation

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: Oversampling, Undersampling, and SMOTE

Binning, Transforming, Encoding, Scaling, and Shuffling

Important Spark Installation Notes

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 2 DataFrame API for MLLib

Deploying Models to Production

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