Cours Overview
Machine Learning is a discipline of Artificial Intelligence focused on teaching machines to gather and apply knowledge. We're already beginning to see the profound effects that educated intelligence systems are having on our world. The decade ahead promises to be one in which we will see an explosive growth in Machine Learning applications, techniques, solutions, and platforms.
In this training class we will focus on learning the core concepts of Machine Learning and getting hands-on with the latest technologies to learn how to create your own solutions!
Audience
This is a technical, hands-on course where students will work with the latest technologies in a series of labs to learn how to create their own solutions. To be successful in this course you need to be a seasoned software developer who is comfortable and fluent in one or more modern development languages (preferably R, Python, or Spark).
Students are also expected to have a strong knowledge of the mathematical and statistical concepts which underlie Machine Learning as this course will NOT cover these concepts in-depth!
Course Outline
Chapter 1: What is Machine Learning?
How does ML relate to AI?
How does DL relate to ML/AI?
ML landscape
ML applications
ML algorithms & models (supervised and unsupervised)
Chapter 2: Machine Learning Tools
Introduction to Jupyter notebooks / R-Studio
Lab: Getting familiar with ML environment
Chapter 3: Machine Learning Concepts
Statistics Primer
Covariance, Correlation, Covariance Matrix
Errors, Residuals
Overfitting / Underfitting
Cross validation, bootstrapping
Confusion Matrix
ROC curve, Area Under Curve (AUC)
Lab: Basic stats
Chapter 4: Feature Engineering
Preparing data for ML
Extracting features, enhancing data
Data cleanup
Visualizing Data
Lab: data cleanup
Lab: visualizing data
Chapter 5: Linear regression
Simple Linear Regression
Multiple Linear Regression
Running LR
Evaluating LR model performance
Lab
Use case: House price estimates
Chapter 6: Logistic Regression
Understanding Logistic Regression
Calculating Logistic Regression
Evaluating model performance
Lab
Use case: credit card application, college admissions
Chapter 7: SVM (Supervised Vector Machines)
SVM concepts and theory
SVM with kernel
Lab
Use case: Customer churn data
Chapter 8: Decision Trees & Random Forests
Theory behind trees
Classification and Regression Trees (CART)
Random Forest concepts
Labs
Use case: predicting loan defaults, estimating election contributions
Chapter 9: Naive Bayes
Theory behind Naive Bayes
Running NB algorithm
Evaluating NB model
Lab
Use case: spam filtering
Chapter 10: Clustering (K-Means)
Theory behind K-Means
Running K-Means algorithm
Estimating the performance
Lab
Use case: grouping cars data, grouping shopping data
Chapter 11: Principal Component Analysis (PCA)
Understanding PCA concepts
PCA applications
Running a PCA algorithm
Evaluating results
Lab
Use case: analyzing retail shopping data
Chapter 12: Recommendation (Collaborative filtering)
Recommender systems overview
Collaborative Filtering concepts
Lab
Use case: movie recommendations, music recommendations
