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Introduction to Machine Learning
Rating: 3.9 out of 5(52 ratings)
2,513 students

Introduction to Machine Learning

Concepts of Regression, Decision Trees, etc...
Created byAniruddha Rumale
Last updated 3/2023
English
English [Auto],

What you'll learn

  • Theoretical concept of Machine learning
  • Concepts of Regression
  • Concepts of data preprocessing
  • Overall Machine Learning theory and its algorithms

Course content

1 section • 6 lectures • 1h 35m total length

  • Basic Definition of Machine Learning9:13

    Define machine learning as a field that enables computers to learn from experience without explicit programming, tracing its origins from 1930s Turing to 1950s AI breakthroughs.

  • Types of machine Learning5:54

    Identify and categorize data using supervised learning with labeled training data; discover patterns with unsupervised learning on unlabeled data; and train autonomous agents through reinforcement learning with reward signals.

  • Hypothesis Space and bias24:24

    Identify how the hypothesis space, or state space, frames learning as a search through possible solutions. Explore how data, prior knowledge, and inductive bias shape model selection, training, and validation.

  • Evaluation and Cross-validation14:41

    Explore evaluation functions, and how linear regression uses least mean squares, uncovering overfitting and variance, and applying k-fold cross-validation to ensure model generalization.

  • Linear Regression7:19

    Learn linear regression as modeling relation between dependent and independent variables with y = mx + c or y = w0 + w1 x, and fit by least mean square.

  • Decision Trees and Questions-Answers34:23

    Learn how decision trees and regression techniques—linear, logistic, and exponential—fit data, minimize error with least squares, and manage bias, variance, and hypothesis spaces for classification and regression.

Requirements

  • None. You will learn everything from its basic

Description

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.

Who this course is for:

  • The course is intended for the students of BCA, BE, BTech and anybody interested in Machine Learning

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