Introduction to Machine Learning
Welcome to Introduction to Machine Learning Course!
Machine learning is the application of artificial intelligence (AI) that provides machines with the ability to automatically learn and improve without being explicitly programmed for the task. The main focus of Machine Learning is to provide algorithms to build and train such systems and use them to solve a determined problem. Therefore, it is very important to understand what is machine learning and how to apply it on your work.
- Machine Learning is closely related to the field of computational statistics as well as mathematical optimization. It contains multiple algorithms such as Supervised Learning, Unsupervised Learning, Semi-supervised Learning and Reinforcement Learning, which all differ in used cases and algorithms.
- Supervised Learning maps an input to an output based on example input-output pairs also known as labeled data. Supervised Learning has two sub classes: Classification and Regression. A task is considered a classification task if the output is categorical and a regression task if the output is a continuous value.
- Unsupervised Learning deals with unlabeled data (data that doesn’t have correct result values, which are known as labels). Unsupervised Learning is mostly used for finding relationships in data sets, reducing dimensionality or identifying anomalies.
- Semi-supervised learning is a mixture of supervised and unsupervised learning. It typically works with a small amount of labeled data and a large amount of unlabeled data. These addition of a little bit of labeled data to an unlabeled dataset has been shown to sometimes significantly improve their results.
- Reinforcement Learning deals with how an agent takes actions in an environment to maximize a reward. Reinforcement Learning saw huge successes in the last few years, especially in the context of game theory with an example being the Dota Bot from OpenAI.
In this course, we'll discuss the different learning methods used in machine learning, such as supervised, unsupervised, and semi-supervised types, along with some of the most common algorithms necessary for your projects.
To begin, learners are recommended to take a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments/projects/quizzes you’ll need to complete to pass the course.
Main concepts are delivered through videos, demos and hands-on exercises.
After taking this course, students should all be able to::
- Understand the basics of Machine Learning concepts
- Understand the basic concepts of Linear Algebra, descriptive statistics and probability
- Comprehend and practice basic Python programming, data structures in Python, working with Pandas and Numpy, Classes and Inheritance
- Comprehend and Practice with tool for Machine Learning
- Outline the basics of Supervised and Unsupervised Learning in Machine Learning with case studies
Module 1 - Machine Learning Overview
- Lesson 1: Welcome to Machine Learning
Module 2 - Python for Machine Learning
- Lesson 2: Python Basics with Data Structures
- Lesson 3: Python Advance with OOP and API
- Lesson 4: Numpy in Python
- Lesson 5: Working with Data and Pandas
- Lesson 6: Data Visualization with Matplotlib
Final Project - Test Grade Calculator