Machine Learning

This course provides an introduction to the fundamentals of Machine Learning. Students will learn the basics of supervised and unsupervised learning, as well as the principles of data mining and predictive analytics. Topics covered include linear and logistic regression, decision trees, support vector machines, neural networks, clustering, and ensemble methods. Students will also gain hands-on experience with popular machine learning libraries such as scikit-learn and TensorFlow.

Beginner 0(0 Ratings) 1 Students enrolled English
Created by Earl Jackson
Last updated Tue, 14-May-2024
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Course overview

This course provides an introduction to the fundamentals of Machine Learning. It covers the basic concepts, algorithms, and techniques used in Machine Learning, and provides an overview of the various applications of Machine Learning. The course will cover topics such as supervised and unsupervised learning, linear and non-linear models, decision trees, neural networks, and deep learning. It will also discuss the various evaluation metrics used to measure the performance of Machine Learning models. The course will also provide hands-on experience with popular Machine Learning libraries such as Scikit-Learn, TensorFlow, and Keras. By the end of the course, students will have a good understanding of the fundamentals of Machine Learning and be able to apply them to real-world problems.

What will i learn?

  • Understand the fundamentals of Machine Learning algorithms and their applications.
  • Develop an understanding of the various techniques used in Machine Learning.
  • Students should have a basic understanding of mathematics, statistics, and computer programming.
  • Course Materials: Textbook, lecture slides, and other course materials will be provided.
Curriculum for this course
10 Lessons 01:19:26 Hours
Introduction to course
5 Lessons 01:03:47 Hours
  • A case study in predicting house prices
  • Regression fundamentals: data & model
  • Regression fundamentals
  • Regression ML block diagram
  • The simple linear regression model
5 Lessons 00:15:39 Hours
  • The cost of using a given line
  • Using the fitted line
  • Interpreting the fitted line
  • Finding maxima or minima analytically
  • Finding the max via hill climbing

Frequently asked question

What is Machine Learning?
Machine Learning is a type of artificial intelligence (AI) that enables computers to learn from data and make predictions without being explicitly programmed. It uses algorithms to analyze data and identify patterns, allowing computers to make decisions and take actions based on the data.
What are the benefits of Machine Learning?
Machine Learning can be used to automate tasks, improve decision-making, and increase efficiency. It can also be used to identify patterns in data that would otherwise be difficult to detect, allowing for more accurate predictions and better insights.
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