Machine Learning Essentials with Python

Course Objectives

This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below

• Getting Started & Optional Python Quick Refresher
• Statistics and Probability Refresher and Python Practice
• Probability Density Function; Probability Mass Function; Naive Bayes
• Predictive Models
• Machine Learning with Python
• Recommender Systems
• KNN and PCA
• Reinforcement Learning
• Dealing with Real-World Data
• Experimental Design / ML in the Real World
• Time Permitting: Deep Learning and Neural Networks

NOTE: Students who want a more math-centric, deeper dive into detailed statistics and algorithm review might consider this course as an alternative: TTML5504 Machine Learning Foundation: Working with Statistics, Algorithms and Neural Networks

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be easily adjusted to accommodate.  We offer additional related Machine Learning, AI, Deep Learning, data science, programming (Python, R, Java, Scala etc.) and development courses which may be blended with this course for a track that best suits your learning objectives. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts.

 Students should have attended or have incoming skills equivalent to those in this course:

• Basic Python Skills.  Attendees without Python background may view labs as follow along exercises or team with others to complete them. (NOTE: This course is also offered in R or Scala – please inquire for details)
• Good foundational mathematics skills in Linear Algebra and Probability, to start learning about and using basic machine learning algorithms and concepts
• Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.

Course Agenda

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

Course Materials

Student Materials: Each student will receive a Student Guide with course notes, code samples, setp-by-step written lab instructions, software tutorials, diagrams and related reference materials and links (as applicable). Students will also receive related (as applicable) project files, code files, data sets and solutions required for any hands-on work.

Lab Setup Made Simple.   All course labs and solutions, data sets, software, detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants.