Introduction to Machine Learning

The course introduces the concepts of Machine Learning.  It covers the regression, both linear and logistic. Decision trees, Data preprocessing etc., to explore the machine learning in brief with plenty of examples. it also discusses the concept of state space, bias, etc. in Machine learning. Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. This course covers the following aspects of machine learning:

1.Basic Definitions 2.Types of Learning

3.Hypothesis space and Inductive Bias

4.Evaluation

5.Cross-Validation

6.Linear Regression

7.Decision Trees 8.Overfitting

The course is designed after studying the syllabus of various technological universities. Machine learning is basically of three types: The main goal in supervised learning is to learn a model from labeled training data that allows us to make predictions about unseen or future data. Supervised refers to a set of samples where the desired output signals (labels) are already known.

Unsupervised learning deals with unlabeled data or data of unknown structure. Using unsupervised learning techniques explores the structure of our data to extract meaningful information without the guidance of a known outcome variable or reward function.

In reinforcement learning, the goal is to develop a system (agent) that improves its performance based on interactions (reward signals) with the environment.