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CampusX 100 Days of Machine Learning: study plan and how long it takes

The CampusX playlist is 134 videos and 62.8 hours long. Here's how long it takes at different paces, a week-by-week schedule built from the real videos, and a planner that turns it into your own daily plan.

134 videos · 62.8 hours · CampusX

Nitish Singh's 100 Days of Machine Learning on CampusX is a complete ML course in Hindi: working with data (CSV, JSON, APIs, scraping), EDA, feature engineering and pipelines, outlier handling, then algorithms from scratch: linear and logistic regression, gradient descent, Naive Bayes, decision and regression trees, ensembles (bagging, random forests, boosting, stacking) and hyperparameter tuning.

It's known for implementing algorithms from scratch before using scikit-learn.

Who it's for: Hindi-speaking learners who know Python and want a thorough, practical ML course.

How long does it take?

The full playlist is 62.8 hours across 134 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:

Days to finish the playlist by daily watch time and playback speed
Daily timeAt 1xAt 1.5xAt 2x
30 min a day126 days84 days63 days
1 h a day63 days42 days32 days
2 h a day32 days21 days16 days

Make your own schedule

The playlist link is already filled in. Press Plan, set how much time you have each day and your speed, and you'll get a day-by-day schedule with a finish date and a calendar file. It reads the live playlist, so new videos are included.

Open on YouTube Track watched videos

Week-by-week schedule

A sample plan at 2 hours a day and normal speed, using whole videos so none is split across days. That's 6 weeks in total. For a different pace, start date or speed, use the planner above.

Week 1Videos 1–28 · 11h 45m

  1. Day 1 · 1h 51m

    • 1What is Machine Learning? | 100 Days of Machine Learning20m
    • 2AI Vs ML Vs DL for Beginners in Hindi16m
    • 3Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth28m
    • 4Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types11m
    • 5Online Machine Learning | Online Learning | Online Vs Offline Machine Learning19m
    • 6Instance-Based Vs Model-Based Learning | Types of Machine Learning17m

  2. Day 2 · 1h 44m

    • 7Challenges in Machine Learning | Problems in Machine Learning24m
    • 8Application of Machine Learning | Real Life Machine Learning Applications29m
    • 9Machine Learning Development Life Cycle | MLDLC in Data Science25m
    • 10Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles26m

  3. Day 3 · 1h 49m

    • 11What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning41m
    • 12Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML37m
    • 13End to End Toy Project | Day 13 | 100 Days of Machine Learning31m

  4. Day 4 · 1h 39m

    • 14How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively22m
    • 15Working with CSV files | Day 15 | 100 Days of Machine Learning37m
    • 16Working with JSON/SQL | Day 16 | 100 Days of Machine Learning17m
    • 17Fetching Data From an API | Day 17 | 100 Days of Machine Learning23m

  5. Day 5 · 1h 24m

    • 18Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning38m
    • 19Understanding Your Data | Day 19 | 100 Days of Machine Learning15m
    • 20EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning31m

  6. Day 6 · 1h 49m

    • 21EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning38m
    • 22Pandas Profiling | Day 22 | 100 Days of Machine Learning13m
    • 23What is Feature Engineering | Day 23 | 100 Days of Machine Learning25m
    • 24Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning33m

  7. Day 7 · 1h 29m

    • 25Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling24m
    • 26Encoding Categorical Data | Ordinal Encoding | Label Encoding20m
    • 27One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning30m
    • 28Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn16m
Week 2Videos 29–53 · 11h 33m

  1. Day 8 · 1h 39m

    • 29Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning46m
    • 30Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform32m
    • 31Power Transformer | Box - Cox Transform | Yeo - Johnson Transform21m

  2. Day 9 · 1h 30m

    • 32Binning and Binarization | Discretization | Quantile Binning | KMeans Binning38m
    • 33Handling Mixed Variables | Feature Engineering12m
    • 34Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning14m
    • 35Handling Missing Data | Part 1 | Complete Case Analysis25m

  3. Day 10 · 1h 46m

    • 36Handling missing data | Numerical Data | Simple Imputer31m
    • 37Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp14m
    • 38Missing Indicator | Random Sample Imputation | Handling Missing Data Part 437m
    • 39KNN Imputer | Multivariate Imputation | Handling Missing Data Part 524m

  4. Day 11 · 1h 52m

    • 40Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer19m
    • 41What are Outliers | Outliers in Machine Learning17m
    • 42Outlier Detection and Removal using Z-score Method | Handling Outliers Part 218m
    • 43Outlier Detection and Removal using the IQR Method | Handing Outliers Part 314m
    • 44Outlier Detection using the Percentile Method | Winsorization Technique16m
    • 45Feature Construction | Feature Splitting12m
    • 46Curse of Dimensionality15m

  5. Day 12 · 1h 30m

    • 47Principle Component Analysis (PCA) | Part 1 | Geometric Intuition34m
    • 48Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution56m

  6. Day 13 · 1h 17m

    • 49Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization43m
    • 50Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi34m

  7. Day 14 · 1h 58m

    • 51Simple Linear Regression | Mathematical Formulation | Coding from Scratch54m
    • 52Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score44m
    • 53Multiple Linear Regression | Geometric Intuition & Code21m
Week 3Videos 54–74 · 11h 29m

  1. Day 15 · 1h 22m

    • 54Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch48m
    • 55Multiple Linear Regression | Part 3 | Code From Scratch16m
    • 56What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression18m

  2. Day 16 · 1h 58m

    • 57Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation1h 58m

  3. Day 17 · 1h 54m

    • 58Batch Gradient Descent with Code Demo | Simple Explanation in Hindi1h 5m
    • 59Stochastic Gradient Descent50m

  4. Day 18 · 1h 17m

    • 60Mini-Batch Gradient Descent22m
    • 61Polynomial Regression | Machine Learning27m
    • 62Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning8m
    • 63Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models20m

  5. Day 19 · 1h 33m

    • 64Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models44m
    • 65Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models19m
    • 665 Key Points - Ridge Regression | Part 4 | Regularized Linear Models30m

  6. Day 20 · 1h 52m

    • 67Lasso Regression | Intuition and Code Sample | Regularized Linear Models29m
    • 68Why Lasso Regression creates sparsity?25m
    • 69ElasticNet Regression | Intuition and Code Example | Regularized Linear Models12m
    • 70Logistic Regression Part 1 | Perceptron Trick47m

  7. Day 21 · 1h 33m

    • 71Logistic Regression Part 2 | Perceptron Trick Code17m
    • 72Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML41m
    • 73Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy29m
    • 74Derivative of Sigmoid Function6m
Week 4Videos 75–107 · 12h 44m

  1. Day 22 · 1h 54m

    • 75Logistic Regression Part 5 | Gradient Descent & Code From Scratch37m
    • 76Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 134m
    • 77Precision, Recall and F1 Score | Classification Metrics Part 243m

  2. Day 23 · 1h 59m

    • 78ROC Curve in Machine Learning | ROC-AUC in Machine Learning Simplified | CampusX1h 11m
    • 79Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 638m
    • 80Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 79m

  3. Day 24 · 1h 45m

    • 81Logistic Regression Hyperparameters || Logistic Regression Part 813m
    • 82Naive Bayes Classifier | Part 1 | Conditional Probability9m
    • 83Naive Bayes Classifier | Part 2 | Independent Events in Probability8m
    • 84Naive Bayes Classifier | Part 3 | Mutually Exclusive Events2m
    • 85Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability4m
    • 86Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem9m
    • 87Naive Bayes Classifier | Part 6 | Intuition15m
    • 88Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm19m
    • 89Naive Bayes Classifier | Part 8 | Simple Example Code16m
    • 90Naive Bayes Part 9 | Handling Numerical Data9m

  4. Day 25 · 2h

    • 91What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX52m
    • 92Support Vector Machines | Geometric Intuition12m
    • 93Mathematics of SVM | Support Vector Machines | Hard margin SVM35m
    • 94Mathematics of Support Vector Machine | Soft Margin SVM15m
    • 95Kernel Trick in SVM | Geometric Intuition6m

  5. Day 26 · 1h 40m

    • 96Kernel Trick in SVM | Code Example14m
    • 97Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain58m
    • 98Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees27m

  6. Day 27 · 1h 48m

    • 99Regression Trees | Decision Trees Part 335m
    • 100Awesome Decision Tree Visualization using dtreeviz library19m
    • 101Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning38m
    • 102Voting Ensemble | Introduction and Core Idea | Part 117m

  7. Day 28 · 1h 39m

    • 103Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 224m
    • 104Voting Ensemble | Regression | Part 311m
    • 105Bagging | Introduction | Part 131m
    • 106Bagging Ensemble | Part 2 | Bagging Classifiers23m
    • 107Bagging Ensemble | Part 3 | Bagging Regressor11m
Week 5Videos 108–126 · 10h 28m

  1. Day 29 · 2h

    • 108Introduction to Random Forest | Intuition behind the Algorithm34m
    • 109How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest13m
    • 110Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important12m
    • 111Random Forest Hyper-parameters15m
    • 112Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example12m
    • 113OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning7m
    • 114Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated27m

  2. Day 30 · 1h 43m

    • 115How Adaboost Classifier Works? | Geometric Intuition17m
    • 116AdaBoost - A Step by Step Explanation19m
    • 117AdaBoost Algorithm | Code from Scratch16m
    • 118AdaBoost Hyperparameters | GridSearchCV in Adaboost11m
    • 119Bagging Vs Boosting | What is the difference between Bagging and Boosting6m
    • 120Gradient Boosting Explained | How Gradient Boosting Works?33m

  3. Day 31 · 57m

    • 121Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting57m

  4. Day 32 · 1h 5m

    • 122Gradient Boosting for Classification | Geometric Intuition | CampusX1h 5m

  5. Day 33 · 1h 20m

    • 123Introduction to XGBOOST | Machine Learning | CampusX1h 20m

  6. Day 34 · 1h 26m

    • 124XGBoost for Regression | XGBoost Part 2 | CampusX47m
    • 125XGBoost For Classification | How XGBoost works on Classification Problems | CampusX39m

  7. Day 35 · 1h 57m

    • 126The Maths Behind XGBoost | Machine Learning | CampusX1h 57m
Week 6Videos 127–134 · 4h 52m

  1. Day 36 · 1h 43m

    • 127Stacking and Blending Ensembles35m
    • 128K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning24m
    • 129K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn10m
    • 130K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch34m

  2. Day 37 · 1h 12m

    • 131Agglomerative Hierarchical Clustering | Python Code Example37m
    • 132DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX34m

  3. Day 38 · 1h 57m

    • 133Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE57m
    • 134Hyperparameter Tuning using Optuna | Bayesian Optimization using Optuna59m

Tips for finishing

  • Despite the name, most people need more than 100 days. Plan one video a day and adjust in the planner as you go.
  • Code the from-scratch implementations yourself; they're what make the scikit-learn versions make sense.

Frequently asked questions

How long does it take to finish CampusX 100 Days of Machine Learning?

The playlist has 134 videos and 62.8 hours of content. At 1 hour a day that's about 63 days at normal speed, or 42 days at 1.5x. At 2 hours a day it's about 32 days.

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