Machine Learning Essentials with Python (TTML5506-P)

3 Days

18 CPD hours

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

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

Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms

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

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

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

Additional course details:

Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.