Machine Learning Essentials with Python

Outline

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

Getting Started

• Installation: Getting Started and Overview
• LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container)
• Python Refresher
• Introducing the Pandas, NumPy and Scikit-Learn 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; Naive Bayes

• Common Data Distributions
• Percentiles and Moments
• A Crash Course in matplotlib
• Advanced Visualization with Seaborn
• Covariance and Correlation
• Conditional Probability
• Naive Bayes: Concepts
• Bayes’ Theorem
• Naive Bayes
• Spam Classifier with Naive Bayes

Predictive Models

• Linear Regression 
• Polynomial Regression
• Multiple Regression, and Predicting Car Prices 
• Logistic Regression
• Logistic Regression
• LDA : Linear Discriminant Analysis

Machine Learning with Python

• Supervised vs. Unsupervised Learning, and Train/Test
• Using Train/Test to Prevent Overfitting
• Understanding a Confusion Matrix
• Measuring Classifiers (Precision, Recall, F1, AUC, ROC)
• K-Means Clustering
• K-Means: Clustering People Based on Age and Income
• Measuring Entropy
• LINUX: Installing GraphViz
• Decision Trees: Concepts
• Decision Trees: Predicting Hiring Decisions
• Ensemble Learning
• Support Vector Machines (SVM) Overview
• Using SVM to Cluster People using scikit-learn

Recommender Systems

• User-Based Collaborative Filtering
• Item-Based Collaborative Filtering
• Finding Similar Movie
• Better Accuracy for Similar Movies
• Recommending movies to People
• Improving your recommendations

KNN and PCA

• K-Nearest-Neighbors: Concepts
• Using KNN to Predict a Rating for a Movie
• Dimensionality Reduction; Principal Component Analysis (PCA)
• PCA with the Iris Data Set

Reinforcement Learning

• Reinforcement Learning with Q-Learning and Gym

Dealing with Real-World Data

• 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

Experimental Design / ML in the Real World

• 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

Capstone Project

• Group Project & Presentation or Review

Optional: Time Permitting

Deep Learning and Neural Networks

• Deep Learning Prerequisites
• The History of Artificial Neural Networks
• Deep Learning in the TensorFlow Playground
• Deep Learning Details
• Introducing TensorFlow
• Using TensorFlow
• Introducing Keras
• Using Keras to Predict Political Affiliations
• Convolutional Neural Networks (CNN’s)
• Using CNN’s for Handwriting Recognition
• Recurrent Neural Networks (RNN’s)
• Using an RNN for Sentiment Analysis
• 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