Introduction: Why Machine Learning? - Problems Machine Learning Can Solve - Knowing Your Task and Knowing Your Data - Why Python? - scikit – learn - Essential Libraries and Tools- Python 2 Versus Python 3.

Classification and Regression - Generalization, Over fitting, and Under fitting- Relation of Model Complexity to Dataset Size- Supervised Machine Learning Algorithms - Some Sample Datasets - k-Nearest Neighbors - Linear Models- Naive Bayes Classifiers - Decision Trees- Ensembles of Decision Trees- Kernelized Support Vector Machines - Neural Networks (Deep Learning) - Uncertainty Estimates from Classifiers - The Decision Function - Predicting Probabilities- Uncertainty in Multiclass Classification.

: Types of Unsupervised Learning - Challenges in Unsupervised Learning - Preprocessing and Scaling - Different Kinds of Preprocessing - Applying Data Transformations - Scaling Training and Test Data the Same Way - The Effect of Preprocessing on Supervised Learning - Clustering - k-Means Clustering - Agglomerative Clustering – DBSCAN - Comparing and Evaluating Clustering Algorithms - Summary of Clustering Methods

Categorical Variables - One-Hot-Encoding (Dummy Variables) - Numbers Can Encode Categoricals - Automatic Feature Selection - Univariate Statistics - Model-Based Feature Selection - Iterative Feature Selection - Utilizing Expert Knowledge

: Types of Data Represented as Strings - Example Application: Sentiment Analysis of Movie Reviews - Representing Text Data as a Bag of Words - Applying Bag-of-Words to a Toy Dataset - Bag-of-Words for Movie Reviews – Advanced Tokenization, Stemming, and Lemmatization - Topic Modeling and Document Clustering - Latent Dirichlet Allocation