Menu
Menu

    LIVE Classes

    VLSI Design Programme
    VLSI Design Programme New

    IIT Delhi certified

    AI & Machine Learning
    AI & Machine Learning New

    Intel & IIT-M Pravartak certified

    Data Science
    Data Science

    IIT-M Pravartak certified

    Full Stack Development
    Full Stack Development

    IIT-M Pravartak certified

    UI/UX Design
    UI/UX Design

    HCL GUVI certified

    AI DevOps Program
    AI DevOps Program

    HCL GUVI certified

    Courses

    Practice

    CodeKata

    CodeKata

    Sharpen your coding skills, prepare for interviews

    WebKata

    WebKata

    Build basic frontend & backend development skills

    SQLKata

    SQLKata

    Master SQL concepts in relational database management

    FixTheCode

    FixTheCode

    A series of programs curated by industry experts

    IDE - Online Compiler

    IDE - Online Compiler

    Run & test your code in any programming language

    Resources

    Free Resources

    Free Resources

    Referral

    Referral

    Success Stories

    Success Stories

    Blogs

    Blogs

    Learn Hub

    Learn Hub

    Get The App

    Get The App

    Rewards

    Rewards

    Become an Affiliate

    Become an Affiliate

    Leaderboard

    Leaderboard

    What's New?
    Resume Builder

    Resume Builder

    Community Hub

    Community Hub

    Our Products

    HackerKID

    HackerKID

    Coding classes platform for K-12 children

    GUVI For Corporates

    GUVI For Corporates

    Meet your Hiring & Training Needs

    Placement Preparation

    Placement Preparation

    Ace Up your Aptitude

    Play StoreApp Store
Still Confused?WhatsApp

Welcome to HCL GUVI

Hey there! Welcome to HCL GUVI—Grab Your Vernacular Imprint—where tech learning is easy, fun, and curated specially for you. Incubated by IIT Madras & IIM Ahmedabad in 2014 and now part of HCL Group, we're making quality tech education accessible to all.

Join 3M+ learners breaking barriers and upskilling for a brighter future. We're here to guide you every step of the way! 🚀

LIVE Classes

Zen Classes are HCL GUVI's most refined and flagship product—live, expert-led tech programs for beginners and pros. With IITM Pravartak affiliations, master Full-Stack, Data Science, DevOps, UI/UX, and more in multiple languages!

Explore More

Courses

Looking for flexibility? HCL GUVI's 200+ self-paced courses let you learn anytime, anywhere! From free lessons to IIT-M & Autodesk-certified programs, gain in-demand skills in your preferred language.

Explore More

Practice Platforms

Enhance your coding skills with HCL GUVI's Practice Platforms—interactive, structured, and designed to help you master programming effortlessly.

CodeKata:

A structured coding practice platform with 1500+ coding problems designed by industry experts. Ideal for beginners and professionals preparing for tech interviews with real-world coding challenges.

Try Now >

WebKata:

An interactive platform to master HTML, CSS, JavaScript, and Bootstrap with a live coding environment. Perfect for hands-on web development practice without any setup.

Try Now >

SQLKata:

A practice ground for mastering SQL queries used in real-world applications. Write, optimize, and refine your queries to build strong database skills.

Try Now >

FixTheCode:

Hone your bug-fixing skills with real-world debugging challenges in Python, C++, JavaScript, and Golang. More languages coming soon!

Try Now >

IDE:

A free online compiler supporting 20+ programming languages with auto-complete, debugging, and AI-powered code generation—all in the cloud!

Try Now >

Leaderboard

Climb the leaderboard as you earn Geekoins by learning and practicing! The top scorers get featured, making learning competitive and rewarding. Keep going—you could be next!

Explore More

Rewards

Earn Geekoins by watching videos and practicing problems, then redeem them for exciting rewards. The more you engage, the more you win!

Explore More

Referral

Love learning with HCL GUVI? Share it with friends! Invite them using your unique link or code and unlock exciting rewards—Amazon vouchers, iPhones, and more. A Win-Win.

Explore More

Profile

Your HCL GUVI profile is your digital portfolio! Track progress, showcase skills, add projects, and build a resume. Keep it updated—opportunities await!

Explore More

That's It! You Are Ready!

You're all set to dive into your learning journey with HCL GUVI. Explore, upskill, and make each step count—exciting possibilities awaits!

Thank you for Reaching us out

Our team will reach you out
within the next 24 hours.

Explore all Programs

Boosting and AdaBoost Explained

Boosting and AdaBoost Explained

Boosting

Boosting trains models sequentially, with each new model focusing on correcting the previous one's mistakes.

Example: A weak model misclassifies certain loan applicants, and the next model is trained to pay extra attention to exactly those cases.

AdaBoost

AdaBoost, short for Adaptive Boosting, assigns higher weight to misclassified data points, forcing the next model to focus more on those difficult cases.

Example: In spam detection, AdaBoost gradually improves accuracy by repeatedly emphasizing emails the earlier models got wrong.

Share

You can share this topic with your family and friends on Social Media.

Share in

Machine Learning Tutorial

AP

Abhishek Pati

Machine Learning Tutorial

MODULE 1 : Introduction to Machine Learning
lessonWhat is Machine Learning
lessonTypes of Machine Learning
lessonSupervised vs Unsupervised vs Reinforcement Learning
lessonApplications of Machine Learning in Real Life
lessonMachine Learning vs Artificial Intelligence vs Deep Learning
lessonMachine Learning Life Cycle Explained
lessonHow to Set Up Your Machine Learning Environment
MODULE 2 : Python for Machine Learning
lessonPython Basics for Machine Learning
lessonNumPy Basics for Machine Learning
lessonPandas Basics for Data Handling
lessonData Visualization with Matplotlib
lessonData Visualization with Seaborn
MODULE 3 : Data Preprocessing and Feature Engineering
lessonWhat is Data Preprocessing
lessonData Cleaning Techniques
lessonHandling Missing Values
lessonFeature Scaling Explained
lessonNormalization vs Standardization
lessonEncoding Categorical Data
lessonFeature Selection Techniques
lessonFeature Engineering Explained
lessonTrain-Test Split Explained
lessonCross-Validation Explained
MODULE 4 : Supervised Learning: Regression
lessonWhat is Regression in Machine Learning
lessonSimple Linear Regression Explained
lessonMultiple Linear Regression Explained
lessonRidge Regression Explained
lessonLasso Regression Explained
lessonBias and Variance Tradeoff Explained
lessonEvaluation Metrics for Regression Models
MODULE 5 : Supervised Learning: Classification
lessonWhat is Classification in Machine Learning
lessonLogistic Regression Explained
lessonK-Nearest Neighbours Explained
lessonDecision Tree Classifier Explained
lessonRandom Forest Classifier Explained
lessonSupport Vector Machine Explained
lessonModel Selection Techniques
lessonEvaluation Metrics for Classification Models
lessonConfusion Matrix Explained
lessonPrecision, Recall, F1 Score and ROC AUC Explained
MODULE 6 : Unsupervised Learning
lessonWhat is Unsupervised Learning
lessonK-Means Clustering Explained
lessonHierarchical Clustering Explained
lessonPrincipal Component Analysis Explained
lessonDimensionality Reduction Techniques
MODULE 7 : Ensemble Learning
lessonWhat is Ensemble Learning
lessonBagging Explained
lessonBoosting and AdaBoost Explained
lessonGradient Boosting Explained
lessonXGBoost Explained
MODULE 8 : Model Evaluation and Optimization
lessonOverfitting and Underfitting Explained
lessonRegularization Techniques Explained
lessonHyperparameter Tuning and Grid Search Explained
MODULE 9 : Neural Networks and Deep Learning
lessonWhat is a Neural Network
lessonActivation Functions Explained
lessonGradient Descent Explained
lessonWhat is Deep Learning
lessonTensorFlow and Keras Explained
MODULE 10 : Model Deployment
lessonWhat is Model Deployment
lessonSaving and Loading Machine Learning Models
MODULE 11 : Iris Flower Classification
lessonProject Overview and Problem Statement
lessonUnderstanding the Iris Dataset
lessonLoading the Dataset with Pandas
lessonExploratory Data Analysis on Iris Data
lessonSplitting Data into Train and Test Sets
lessonBuilding a K-Nearest Neighbours Model
lessonEvaluating Model Performance
lessonConclusion and Key Takeaways
MODULE 12 : Learning Resources and Next Step
lessonWhat to Learn After This Machine Learning Tutorial
lessonBest Machine Learning Courses

Learn via Video Course

Artificial Intelligence and Machine Learning Course Logo

Artificial Intelligence and Machine Learning Course

55 hrs

Get in Touch For Details

Machine Learning Tutorial

    MODULE 1 : Introduction to Machine Learning
    lessonWhat is Machine Learning
    lessonTypes of Machine Learning
    lessonSupervised vs Unsupervised vs Reinforcement Learning
    lessonApplications of Machine Learning in Real Life
    lessonMachine Learning vs Artificial Intelligence vs Deep Learning
    lessonMachine Learning Life Cycle Explained
    lessonHow to Set Up Your Machine Learning Environment
    MODULE 2 : Python for Machine Learning
    lessonPython Basics for Machine Learning
    lessonNumPy Basics for Machine Learning
    lessonPandas Basics for Data Handling
    lessonData Visualization with Matplotlib
    lessonData Visualization with Seaborn
    MODULE 3 : Data Preprocessing and Feature Engineering
    lessonWhat is Data Preprocessing
    lessonData Cleaning Techniques
    lessonHandling Missing Values
    lessonFeature Scaling Explained
    lessonNormalization vs Standardization
    lessonEncoding Categorical Data
    lessonFeature Selection Techniques
    lessonFeature Engineering Explained
    lessonTrain-Test Split Explained
    lessonCross-Validation Explained
    MODULE 4 : Supervised Learning: Regression
    lessonWhat is Regression in Machine Learning
    lessonSimple Linear Regression Explained
    lessonMultiple Linear Regression Explained
    lessonRidge Regression Explained
    lessonLasso Regression Explained
    lessonBias and Variance Tradeoff Explained
    lessonEvaluation Metrics for Regression Models
    MODULE 5 : Supervised Learning: Classification
    lessonWhat is Classification in Machine Learning
    lessonLogistic Regression Explained
    lessonK-Nearest Neighbours Explained
    lessonDecision Tree Classifier Explained
    lessonRandom Forest Classifier Explained
    lessonSupport Vector Machine Explained
    lessonModel Selection Techniques
    lessonEvaluation Metrics for Classification Models
    lessonConfusion Matrix Explained
    lessonPrecision, Recall, F1 Score and ROC AUC Explained
    MODULE 6 : Unsupervised Learning
    lessonWhat is Unsupervised Learning
    lessonK-Means Clustering Explained
    lessonHierarchical Clustering Explained
    lessonPrincipal Component Analysis Explained
    lessonDimensionality Reduction Techniques
    MODULE 7 : Ensemble Learning
    lessonWhat is Ensemble Learning
    lessonBagging Explained
    lessonBoosting and AdaBoost Explained
    lessonGradient Boosting Explained
    lessonXGBoost Explained
    MODULE 8 : Model Evaluation and Optimization
    lessonOverfitting and Underfitting Explained
    lessonRegularization Techniques Explained
    lessonHyperparameter Tuning and Grid Search Explained
    MODULE 9 : Neural Networks and Deep Learning
    lessonWhat is a Neural Network
    lessonActivation Functions Explained
    lessonGradient Descent Explained
    lessonWhat is Deep Learning
    lessonTensorFlow and Keras Explained
    MODULE 10 : Model Deployment
    lessonWhat is Model Deployment
    lessonSaving and Loading Machine Learning Models
    MODULE 11 : Iris Flower Classification
    lessonProject Overview and Problem Statement
    lessonUnderstanding the Iris Dataset
    lessonLoading the Dataset with Pandas
    lessonExploratory Data Analysis on Iris Data
    lessonSplitting Data into Train and Test Sets
    lessonBuilding a K-Nearest Neighbours Model
    lessonEvaluating Model Performance
    lessonConclusion and Key Takeaways
    MODULE 12 : Learning Resources and Next Step
    lessonWhat to Learn After This Machine Learning Tutorial
    lessonBest Machine Learning Courses

    ZEN CLASS

    LIVE CLASS

    Popular Courses

    Self-Paced Courses

    Practice Platforms

    Community

    Products

    Resources

    Company

    ZEN CLASS

    LIVE CLASS

    Popular Courses

    Self-Paced Courses

    Practice Platforms

    Community

    Products

    Resources

    Company

    app store
    play store
    Refer & Earn

    HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

    Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

    app storeplay store
    Refer & Earn

    HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

    Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

    app storeplay storeRefer & Earn
    Terms and ConditionsPrivacy Policy

    HCL GUVI Geek Network Pvt. Ltd.

    Terms and ConditionsPrivacy Policy

    HCL GUVI Geek Network Pvt. Ltd.