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Introduction to Machine Learning

  • Course level: Beginner

Description

Introduction to Machine Learning

  • To introduce students to the basic concepts and techniques of Machine Learning.
  • To develop skills of using recent machine learning software for solving practical problems.
  • To gain experience of doing independent study and research.

What this course will offer

This course covers the basic concepts and techniques of Machine Learning from both theoretical and practical perspective. The material includes classical ML approaches such as Linear Regression and Decision Trees, more advanced approaches as Clustering and Association Rules as well as “hot” topics such as XGBoost.

View full course outline here

What Will I Learn?

  • Practical Approach to learning Machine Learning
  • Complete 360 degree view for Machine Learning
  • Supervised and Unsupervised Machine Learning
  • Commonly used Algorithms

Topics for this course

18 Lessons

Module 1 Introduction

What is Machine Learning? & Popular Applications16:27
Lifecycle of a ML Project16:27
Introduction to Supervised and Unsupervised Learning16:50

Module 2 Data Exploration

Module 3 Evaluation Metrics

Module 4 Linear Regression

Module 5 Classification

Module 6 Unsupervised Learning

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Supper Expelnetion

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Requirements

  • Basic coding experience in any programming language
  • Some exposure to Python and its libraries would help

Target Audience

  • People looking for a career shift in data science
  • Students wanting to advance their skills in Machine Learning and AI
  • Anyone looking for a practical approach to learning Machine Learning