Introduction To Machine Learning by Dr.Padmapriya G

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
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