Global Leader in Emerging Tech Education

Machine Learning:

Understanding Solutions and Developing Applications

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!


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

Duration: 20 Hours

Course Access: 1 Year

Format: Self-Paced Learning

  • Machine Learning On Demand

Price: CHF 225.00

  • Join over 100,000 students learning with BTA

  • One year Access to Learning Content

  • 20 Hours of Learning Content


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