Machine Learning

Course Overview

This course is expertly crafted to guide participants through the foundational concepts and advanced techniques in the field. Starting with an introduction to what machine learning is and its differentiation from data science and AI, the course moves into practical aspects such as project identification, formulating machine learning questions, and defining success metrics.

It covers essential project management strategies specific to machine learning, including DevOps integration, lifecycle examples, and team structuring. Participants will delve into basic algorithms like linear regression and cluster analysis, understand the pivotal role of statistics and probability, and engage in exploratory data analysis using visualisation techniques.

The course also addresses technical skills in linear algebra for large data manipulation, strategies for managing training vs. testing data, combating overfitting, and the intricacies of recommendation engines including hyper-parameter tuning.

Culminating in a full project example, this course is designed for those looking to deepen their understanding of machine learning applications and methodologies, enhancing their ability to develop and implement machine learning models effectively.

Learning Objectives

• Understand the fundamental concepts of machine learning, distinguishing it from data science and artificial intelligence, and recognize its basic applications in the real world.
• Develop the ability to formulate a machine learning question, identify relevant metrics for project success, and understand the steps involved in project identification.
• Acquire project management skills tailored to machine learning projects, including knowledge of DevOps integration, understanding the project lifecycle, and structuring a machine learning team.
• Gain proficiency in basic machine learning algorithms, including linear regression and cluster analysis, and understand the differences between supervised and unsupervised learning.
• Master the essentials of statistics and probability in the context of machine learning, including calculating probabilities, understanding chained probabilities, and recognizing sampling biases.
• Learn exploratory data analysis techniques for extracting insights from datasets, comprehend the importance of training vs. testing data to avoid overfitting, and explore the development and tuning of recommendation engines.