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Data & AI

100 Days of Machine Learning in Hindi (CampusX)

Sikhami InstituteHindi

Free

Join for free
  • 134 video lectures (62h 50m)
  • Certificate of completion
  • Ask the teacher your doubts
  • Watch on the web and in the Android app

About this course

Nitish Singh's in-depth machine learning course in Hindi, explaining both the intuition and the maths, with code for every algorithm. Who it's for: students and professionals who want a thorough foundation in machine learning, in Hindi. Taught in Hindi. These are the free, official videos of the "100 Days of Machine Learning" playlist from the CampusX YouTube channel (youtube.com/@campusx-official). All videos belong to CampusX; Sikhami is not affiliated with CampusX and simply arranges the public playlist into a course, so you can learn one lesson at a time and keep track of your progress.

What you will learn

• What machine learning is and the end-to-end project workflow • Getting data from CSV, JSON, SQL and APIs, and exploratory data analysis • Feature engineering: scaling, encoding, imputation and outliers • PCA and dimensionality reduction • Linear, polynomial, ridge, lasso and logistic regression, and gradient descent • Naive Bayes, SVM, decision trees and ensembles (random forest, AdaBoost, gradient boosting, XGBoost) • Clustering and hyperparameter tuning, including Optuna

Course content

1 section · 134 lectures · 62h 50m

  1. 1. 100 Days of Machine Learning | CampusX

    • What is Machine Learning? | 100 Days of Machine Learning20m
    • AI Vs ML Vs DL for Beginners in Hindi16m
    • Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth27m
    • Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types11m
    • Online Machine Learning | Online Learning | Online Vs Offline Machine Learning19m
    • Instance-Based Vs Model-Based Learning | Types of Machine Learning16m
    • Challenges in Machine Learning | Problems in Machine Learning23m
    • Application of Machine Learning | Real Life Machine Learning Applications29m
    • Machine Learning Development Life Cycle | MLDLC in Data Science25m
    • Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles26m
    • What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning41m
    • Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML37m
    • End to End Toy Project | Day 13 | 100 Days of Machine Learning30m
    • How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively22m
    • Working with CSV files | Day 15 | 100 Days of Machine Learning36m
    • Working with JSON/SQL | Day 16 | 100 Days of Machine Learning17m
    • Fetching Data From an API | Day 17 | 100 Days of Machine Learning22m
    • Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning37m
    • Understanding Your Data | Day 19 | 100 Days of Machine Learning15m
    • EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning30m
    • EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning38m
    • Pandas Profiling | Day 22 | 100 Days of Machine Learning13m
    • What is Feature Engineering | Day 23 | 100 Days of Machine Learning24m
    • Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning32m
    • Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling23m
    • Encoding Categorical Data | Ordinal Encoding | Label Encoding19m
    • One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning30m
    • Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn15m
    • Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning45m
    • Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform32m
    • Power Transformer | Box - Cox Transform | Yeo - Johnson Transform21m
    • Binning and Binarization | Discretization | Quantile Binning | KMeans Binning38m
    • Handling Mixed Variables | Feature Engineering12m
    • Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning14m
    • Handling Missing Data | Part 1 | Complete Case Analysis24m
    • Handling missing data | Numerical Data | Simple Imputer31m
    • Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp13m
    • Missing Indicator | Random Sample Imputation | Handling Missing Data Part 437m
    • KNN Imputer | Multivariate Imputation | Handling Missing Data Part 524m
    • Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer18m
    • What are Outliers | Outliers in Machine Learning17m
    • Outlier Detection and Removal using Z-score Method | Handling Outliers Part 217m
    • Outlier Detection and Removal using the IQR Method | Handing Outliers Part 314m
    • Outlier Detection using the Percentile Method | Winsorization Technique16m
    • Feature Construction | Feature Splitting12m
    • Curse of Dimensionality15m
    • Principle Component Analysis (PCA) | Part 1 | Geometric Intuition33m
    • Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution56m
    • Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization43m
    • Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi33m
    • Simple Linear Regression | Mathematical Formulation | Coding from Scratch53m
    • Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score43m
    • Multiple Linear Regression | Geometric Intuition & Code20m
    • Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch48m
    • Multiple Linear Regression | Part 3 | Code From Scratch16m
    • What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression17m
    • Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation1h 57m
    • Batch Gradient Descent with Code Demo | Simple Explanation in Hindi1h 4m
    • Stochastic Gradient Descent49m
    • Mini-Batch Gradient Descent22m
    • Polynomial Regression | Machine Learning26m
    • Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning8m
    • Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models19m
    • Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models43m
    • Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models18m
    • 5 Key Points - Ridge Regression | Part 4 | Regularized Linear Models30m
    • Lasso Regression | Intuition and Code Sample | Regularized Linear Models28m
    • Why Lasso Regression creates sparsity?24m
    • ElasticNet Regression | Intuition and Code Example | Regularized Linear Models11m
    • Logistic Regression Part 1 | Perceptron Trick47m
    • Logistic Regression Part 2 | Perceptron Trick Code17m
    • Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML40m
    • Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy29m
    • Derivative of Sigmoid Function5m
    • Logistic Regression Part 5 | Gradient Descent & Code From Scratch36m
    • Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 134m
    • Precision, Recall and F1 Score | Classification Metrics Part 242m
    • ROC Curve in Machine Learning | ROC-AUC in Machine Learning Simplified | CampusX1h 11m
    • Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 638m
    • Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 79m
    • Logistic Regression Hyperparameters || Logistic Regression Part 813m
    • Naive Bayes Classifier | Part 1 | Conditional Probability9m
    • Naive Bayes Classifier | Part 2 | Independent Events in Probability7m
    • Naive Bayes Classifier | Part 3 | Mutually Exclusive Events1m
    • Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability4m
    • Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem9m
    • Naive Bayes Classifier | Part 6 | Intuition14m
    • Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm19m
    • Naive Bayes Classifier | Part 8 | Simple Example Code16m
    • Naive Bayes Part 9 | Handling Numerical Data8m
    • What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX52m
    • Support Vector Machines | Geometric Intuition11m
    • Mathematics of SVM | Support Vector Machines | Hard margin SVM34m
    • Mathematics of Support Vector Machine | Soft Margin SVM14m
    • Kernel Trick in SVM | Geometric Intuition6m
    • Kernel Trick in SVM | Code Example14m
    • Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain58m
    • Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees27m
    • Regression Trees | Decision Trees Part 335m
    • Awesome Decision Tree Visualization using dtreeviz library18m
    • Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning37m
    • Voting Ensemble | Introduction and Core Idea | Part 116m
    • Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 223m
    • Voting Ensemble | Regression | Part 310m
    • Bagging | Introduction | Part 131m
    • Bagging Ensemble | Part 2 | Bagging Classifiers22m
    • Bagging Ensemble | Part 3 | Bagging Regressor10m
    • Introduction to Random Forest | Intuition behind the Algorithm33m
    • How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest12m
    • Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important12m
    • Random Forest Hyper-parameters15m
    • Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example11m
    • OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning6m
    • Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated27m
    • How Adaboost Classifier Works? | Geometric Intuition17m
    • AdaBoost - A Step by Step Explanation19m
    • AdaBoost Algorithm | Code from Scratch16m
    • AdaBoost Hyperparameters | GridSearchCV in Adaboost11m
    • Bagging Vs Boosting | What is the difference between Bagging and Boosting6m
    • Gradient Boosting Explained | How Gradient Boosting Works?32m
    • Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting56m
    • Gradient Boosting for Classification | Geometric Intuition | CampusX1h 4m
    • Introduction to XGBOOST | Machine Learning | CampusX1h 19m
    • XGBoost for Regression | XGBoost Part 2 | CampusX47m
    • XGBoost For Classification | How XGBoost works on Classification Problems | CampusX39m
    • The Maths Behind XGBoost | Machine Learning | CampusX1h 57m
    • Stacking and Blending Ensembles35m
    • K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning23m
    • K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn10m
    • K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch33m
    • Agglomerative Hierarchical Clustering | Python Code Example37m
    • DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX34m
    • Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE57m
    • Hyperparameter Tuning using Optuna | Bayesian Optimization using Optuna59m