Introduction To Machine Learning by Dr.Padmapriya G

Machine Learning, Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Markov Models, HMM

What you'll learn

Explore the fundamental mathematical concepts of machine learning algorithms

Apply linear machine learning model to perform regression and classification

Utilize mixture models to group similar data items

Develop macine learning models for time-series data prediction

Design ensemble learning model using various machine learning algorithms

Requirements

No programming experience is need

Description

Course Description:Unlock the power of machine learning with this comprehensive course designed for beginners and intermediate learners. You will be guided through the essential concepts, algorithms, and techniques driving machine learning today, building a solid understanding of how machines learn from data and solve real-world problems. This course is designed to help you grasp the theoretical underpinnings of machine learning while applying your knowledge through solved problems, making complex concepts more accessible.What You'll Learn:Core Principles of Machine Learning: Gain a deep understanding of how systems learn from data to make intelligent decisions.Supervised Learning: Explore predictive modeling using algorithms like Linear Regression, and Support Vector Machines (SVM).Unsupervised Learning: Master clustering techniques like k-Means and Hierarchical Clustering to discover patterns in data.Regression and Classification: Learn how to model continuous outcomes (regression) and classify data into distinct categories (classification).Clustering: Group similar data points to uncover hidden structures within large datasets.Markov Models & Hidden Markov Models (HMMs): Understand probabilistic models that predict future states and learn how they are used to model sequences and temporal data. Through solved problems, you'll explore how these models work in practice, gaining insights into the theoretical foundation and practical application of HMMs in time-series data and sequential decision-making processes.Machine learning is transforming industries by enabling systems to learn and make intelligent decisions from data. This course will equip you with a strong foundation in machine learning, focusing on problem-solving and theoretical understanding without the need for hands-on implementation.Practical Application Through Solved Problems:This course includes solved problems to illustrate how each algorithm and technique works in practice. These examples will help you apply theoretical concepts to real-world situations, deepening your understanding and preparing you to solve similar problems in your professional or academic career.Through detailed explanations of algorithms, real-world examples, and step-by-step breakdowns of machine learning processes, you'll develop a solid grasp of the models and techniques used across various industries. This course is perfect for learners who want to master the core concepts of machine learning and engage with practical applications without diving into programming or technical implementation.Course Highlights:No Programming Required: Focus on understanding the theory behind machine learning algorithms and models.Solve Real-World Problems: Work through practical examples to understand how to apply machine learning techniques to everyday challenges.Evaluate Model Performance: Learn to assess, interpret, and refine machine learning models effectively.Build a Strong Conceptual Foundation: Prepare for future practical applications in machine learning or data-driven fields.Who Should Take This Course:Students and Professionals: Ideal for those seeking an in-depth introduction to machine learning theory.Enthusiasts with Basic Knowledge of Math and Programming: Perfect for those interested in machine learning concepts through solved problems and real-world examples.

Overview

Section 1: Introduction

Lecture 1 Machine Learning What and Why?

Lecture 2 Supervised and Unsupervised Learning

Lecture 3 Polynomial Curve Fitting

Lecture 4 Probability Theory - Introduction and Fundamental Rules

Lecture 5 Probability - Bayes Rule and Independence and Conditional Independence

Lecture 6 Probability - Random Variables and Density Function

Lecture 7 Probability - Quantiles, Mean, Variance, Expectation and Covariance

Section 2: Linear Models for Regression

Lecture 8 Robust Linear Regression

Lecture 9 Ridge Linear Regression

Section 3: Mixture Models and EM

Lecture 10 K- Means Clustering

Lecture 11 K-Means Clustering Solved Problem

Lecture 12 PCA Solved Problem

Lecture 13 Hierarchical Clustering

Section 4: Hidden Markov Models

Lecture 14 Sequential Data and Markov Model

Beginners for Machine learning