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Krish Naik's Complete Machine Learning Playlist: study plan and how long it takes

The Krish Naik playlist is 153 videos and 36.9 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.

153 videos · 36.9 hours · Krish Naik

Krish Naik's Complete Machine Learning playlist starts with Python, then covers statistics, regression including ridge and lasso, classification algorithms, decision trees and Gini impurity, SVMs and kernels, ensembles, performance metrics, PCA, and end-to-end projects with deployment using Flask.

It's a large collection built up over years, so it's best followed in topic blocks.

Who it's for: Aspiring data scientists who want broad ML coverage with interview preparation and end-to-end projects.

How long does it take?

The full playlist is 36.9 hours across 153 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 day74 days50 days37 days
1 h a day37 days25 days19 days
2 h a day19 days13 days10 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 1 hour 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 1–23 · 6h 7m

  1. Day 1 · 48m

    • 1Complete Road Map To Be Expert In Python- Follow My Way29m
    • 2Complete Roadmap To Follow To Prepare Machine Learning With All Videos And Materials19m

  2. Day 2 · 46m

    • 3Tutorial 1- Anaconda Installation and Python Basics19m
    • 4Why Python is the Best Programming Language For Machine Learning?5m
    • 5Tutorial 2 - Python List and Boolean Variables22m

  3. Day 3 · 1h

    • 6Tutorial 3- Python Sets, Dictionaries and Tuples16m
    • 7Tutorial 4 - Numpy and Inbuilt Functions Tutorial27m
    • 8Tutorial 5- Pandas, Data Frame and Data Series Part-117m

  4. Day 4 · 49m

    • 9Tutorial 6- Pandas,Reading CSV files With Various Parameters- Part 230m
    • 10Tutorial 7- Pandas-Reading JSON,Reading HTML, Read PICKLE, Read EXCEL Files- Part 320m

  5. Day 5 · 59m

    • 11Tutorial 8- Matplotlib (Simple Visualization Library)26m
    • 12Tutorial 9- Seaborn Tutorial- Distplot, Joinplot, Pairplot Part 122m
    • 13Tutorial 10- Seaborn- Countplot(), Violinplot(), Boxplot()- Part211m

  6. Day 6 · 55m

    • 14How To Become Expertise in Exploratory Data Analysis10m
    • 15Tutorial 11-Exploratory Data Analysis(EDA) of Titanic dataset32m
    • 16Tutorial 12- Python Functions, Positional and Keywords Arguments14m

  7. Day 7 · 50m

    • 17Tutorial 15- Map Functions using Python5m
    • 18Tutorial 13- Python Lambda Functions6m
    • 19Tutorial 16- Filter Functions In Python4m
    • 20Tutorial 17- Python List Comprehension8m
    • 21Tutorial 18- Python Advanced String Formatting10m
    • 22Tutorial 19- Python Iterables vs Iterators12m
    • 23Tutorial 20- How To Import All Important Python Data Science Libraries Using Pyforest5m
Week 2Videos 24–47 · 5h 55m

  1. Day 8 · 59m

    • 24Tutorial 21- Python OOPS Tutorial- Classes, Variables, Methods and Objects14m
    • 25Advanced Python- Exception Handling Detailed Explanation In Python20m
    • 26Advanced Python Series- Custom Exception Handling In Python9m
    • 27Advance Python Series- Public Private And Protected Access Modifiers15m

  2. Day 9 · 57m

    • 28Advance Python Series- Inheritance In Python12m
    • 29Tutorial 22-Univariate, Bivariate and Multivariate Analysis- Part1 (EDA)-Data Science13m
    • 30Tutorial 23-Univariate, Bivariate and Multivariate Analysis- Part2 (EDA)-Data Science16m
    • 31Tutorial 24- Histogram in EDA- Data Science5m
    • 32Tutorial 24-Z Score Statistics Data Science12m

  3. Day 10 · 52m

    • 33Tutorial 25- Probability Density function and CDF- EDA-Data Science8m
    • 34Tutorial 26- Linear Regression Indepth Maths Intuition- Data Science24m
    • 35Tutorial 27- Ridge and Lasso Regression Indepth Intuition- Data Science20m

  4. Day 11 · 46m

    • 36Tutorial 28- Ridge and Lasso Regression using Python and Sklearn10m
    • 37Multiple Linear Regression using python and sklearn20m
    • 38Tutorial 28-MultiCollinearity In Linear Regression- Part 216m

  5. Day 12 · 57m

    • 39Machine Learning-Bias And Variance In Depth Intuition| Overfitting Underfitting17m
    • 40Tutorial 29-R square and Adjusted R square Clearly Explained| Machine Learning17m
    • 41Tutorial 31- Hypothesis Test, Type 1 Error, Type 2 Error12m
    • 42What Is P Value In Statistics In Simple Language?11m

  6. Day 13 · 46m

    • 43Tutorial 32- All About P Value,T test,Chi Square Test, Anova Test and When to Use What?12m
    • 44Tutorial 33- P Value,T test, Correlation Implementation with Python- Hypothesis Testing20m
    • 45Tutorial 33- Chi Square Test Implementation with Python- Hypothesis Testing- Part 214m

  7. Day 14 · 37m

    • 46Tutorial 34- Performance Metrics For Classification Problem In Machine Learning- Part124m
    • 47Tutorial 35- Logistic Regression Indepth Intuition- Part 1| Data Science13m
Week 3Videos 48–75 · 6h 10m

  1. Day 15 · 57m

    • 48Tutorial 36- Logistic Regression Indepth Intuition- Part 2| Data Science28m
    • 49Tutorial 36- Logistic Regression Mutliclass Classification(OneVsRest)- Part 3| Data Science7m
    • 50Tutorial 37: Entropy In Decision Tree Intuition9m
    • 51Tutorial 38- Decision Tree Information Gain13m

  2. Day 16 · 41m

    • 52Tutorial 39- Gini Impurity Intuition In Depth In Decision Tree11m
    • 53Tutorial 40- Decision Tree Split For Numerical Feature6m
    • 54Advance House Price Prediction- Exploratory Data Analysis- Part 123m

  3. Day 17 · 56m

    • 55Advance House Price Prediction- Exploratory Data Analysis- Part 220m
    • 56Advance House Price Prediction-Feature Engineering Part 114m
    • 57Advance House Price Prediction-Feature Engineering Part 214m
    • 58Advance House Price Prediction-Feature Selection8m

  4. Day 18 · 54m

    • 59Tutorial 41-Performance Metrics(ROC,AUC Curve) For Classification Problem In Machine Learning Part 210m
    • 60Performance Metrics On MultiClass Classification Problems6m
    • 61K Nearest Neighbor classification with Intuition and practical solution20m
    • 62K Nearest Neighbour Easily Explained with Implementation18m

  5. Day 19 · 56m

    • 63Tutorial 42 - Ensemble: What is Bagging (Bootstrap Aggregation)?6m
    • 64Tutorial 43-Random Forest Classifier and Regressor10m
    • 65Tutorial 45-Handling imbalanced Dataset using python- Part 113m
    • 66Tutorial 46-Handling imbalanced Dataset using python- Part 211m
    • 67Hyperparameter Optimization for Xgboost15m

  6. Day 20 · 55m

    • 68What is AdaBoost (BOOSTING TECHNIQUES)14m
    • 69Visibility Climate Prediction- You Can Add This In Your Resume17m
    • 70Euclidean Distance and Manhattan Distance9m
    • 71K Means Clustering Intuition15m

  7. Day 21 · 52m

    • 72Hierarchical Clustering intuition6m
    • 73DBSCAN Clustering Easily Explained with Implementation19m
    • 74Silhouette (clustering)- Validating Clustering Models- Unsupervised Machine Learning20m
    • 75Curse of Dimensionality Easily explained| Machine Learning8m
Week 4Videos 76–99 · 6h 24m

  1. Day 22 · 50m

    • 76Dimensional Reduction| Principal Component Analysis19m
    • 77Principle Component Analysis (PCA) using sklearn and python13m
    • 78What is Cross Validation and its types?18m

  2. Day 23 · 55m

    • 79Tutorial 42-How To Find Optimal Threshold For Binary Classification - Data Science15m
    • 80Tutorial 47- Bayes' Theorem| Conditional Probability- Machine Learning11m
    • 81Tutorial 48- Naive Bayes' Classifier Indepth Intuition- Machine Learning16m
    • 82Tutorial 49- How To Apply Naive Bayes' Classifier On Text Data (NLP)- Machine Learning13m

  3. Day 24 · 57m

    • 83Support Vector Machine (SVM) Basic Intuition- Part 1| Machine Learning13m
    • 84Maths Intuition Behind Support Vector Machine Part 2 | Machine Learning Data Science23m
    • 85SVM Kernels In-depth Intuition- Polynomial Kernels Part 3 | Machine Learning Data Science21m

  4. Day 25 · 44m

    • 86SVM Kernal- Polynomial And RBF Implementation Using Sklearn- Machine Learning15m
    • 87Gradient Boosting In Depth Intuition- Part 1 Machine Learning11m
    • 88Gradient Boosting Complete Maths Indepth Intuiton Explained| Machine Learning- Part218m

  5. Day 26 · 59m

    • 89Xgboost Classification Indepth Maths Intuition- Machine Learning Algorithms🔥🔥🔥🔥24m
    • 90Xgboost Regression In-Depth Intuition Explained- Machine Learning Algorithms 🔥🔥🔥🔥20m
    • 91Data Science In Medical-Live Tracking Of CO--VID Cases In India using Python16m

  6. Day 27 · 1h

    • 92Perform EDA In Seconds With Visualization Using SweetViz Library17m
    • 934 End To End Projects Till Deployment For Beginners In Data Science| All You Have To Do Is Learn11m
    • 94Deploy Machine Learning Models Using StreamLit Library- Data Science13m
    • 95Perform Exploratory Data Analysis In Minutes- Data Science| Machine Learning19m

  7. Day 28 · 59m

    • 96Pandas Visual Analysis- Perform Exploratory Data Analysis In A Single Line Of Code🔥🔥🔥🔥13m
    • 97How To Read And Process Huge Datasets in Seconds Using Vaex Library| Data Science| Machine Learning20m
    • 98D-Tale The Best Library To Perform Exploratory Data Analysis Using Single Line Of Code🔥🔥🔥🔥13m
    • 99Interview Prep Day3-How To Prepare Support Vector Machines Important Questions In Interviews🔥🔥14m
Week 5Videos 100–133 · 6h 41m

  1. Day 29 · 57m

    • 100Google Datasets Search Engine- Search All Datasets From One Place For Data Science,Machine Learning12m
    • 101How To Run Flask In Google Colab8m
    • 102Time Series Forecasting Using Facebook FbProphet17m
    • 103Performance Metrics Interview Questions- Data Science5m
    • 104How To Perform Post Pruning In Decision Tree? Prevent Overfitting- Data Science16m

  2. Day 30 · 56m

    • 105How To Train Machine Learning Model Using CPU Multi Cores15m
    • 106Step By Step Process To Learn Machine Learning Algorithm Efficiently14m
    • 107Data Science Is Just Not About Model Building8m
    • 108How To Interpret The ML Model? Is Your Model Black Box? Lime Library11m
    • 1096 Healthcare End To End Machine Learning Projects- Credits Devansh and Bedanta8m

  3. Day 31 · 1h

    • 110Overfitting, Underfitting And Data Leakage Explanation With Simple Example14m
    • 111What Is API? Application Programming Interface And Why It Is Important-Data Science8m
    • 112500+ Machine Learning And Deep Learning Projects All At One Place8m
    • 113Google Colab Pro Vs Colab Free- Benefits Of Using Colab Pro- How To Access From India13m
    • 114Advance Python Series-Magic Methods In Classes10m
    • 115Advanced Python Series- Assert Statement In Python6m

  4. Day 32 · 58m

    • 116How To Speed Up Pandas By 4X Times- Modin Pandas Library12m
    • 117TextBlob Library In Python For Natural Language Processing9m
    • 1183000+ Research Datasets For Machine Learning Researchers By Papers With Code7m
    • 119Introduction To MLflow-An Open Source Platform for the Machine Learning Lifecycle12m
    • 120Amazing Data Science End To End Project From Starters In ML and Deep Learning- Agriculture Domain8m
    • 121Lux - Python Library for Intelligent Visual Discovery10m

  5. Day 33 · 58m

    • 122Texthero-Text Preprocessing, Representation And Visualization From Zero to Hero.16m
    • 123Colab Pro Now Available In India, Brazil, France, Thailand,Japan,UK- BOON FOR Data Science Aspirants6m
    • 124Rainfall Prediction- Converting A Kaggle Project to End To End Machine Learning Project7m
    • 125PyWebIO- Creating WebAPP Using Python Without Using HTML And JS17m
    • 126Creating BMI Calculator Web APP Using Python And PyWebIO12m

  6. Day 34 · 55m

    • 127Deployment Of ML Models Using PyWebIO And Flask12m
    • 128Shapash- Python Library To Make Machine Learning Interpretable16m
    • 129Difference Between fit(), transform(), fit_transform() and predict() methods in Scikit-Learn26m

  7. Day 35 · 57m

    • 130EvalML AutoML Library To Automate Feature Engineering, Feature Selection,Model Creation And Tuning23m
    • 131Lazy Predict Python- Understanding Which Models Works Well Without Any Tuning9m
    • 132How To Automate NLP Tasks Using EvalML Library15m
    • 133Gradio Library-Interfaces for your Machine Learning Models9m
Week 6Videos 134–153 · 5h 37m

  1. Day 36 · 59m

    • 134Comparing Transfer Learning Models Using Gradio8m
    • 135Introduction To Machine Learning And Deep Learning For Starters50m

  2. Day 37 · 51m

    • 136Numba Library- Let's Make Python Faster7m
    • 137Deployment Of ML Models Using PyWebIO And Flask In Heroku8m
    • 138All Automated EDA Libraries All At One Place15m
    • 139Discussing All The Types Of Feature Transformation In Machine Learning22m

  3. Day 38 · 57m

    • 140Automating Web Scrapping Using AutoScraper Library16m
    • 141Automating WebScraping Amazon Ecommerce Website Using AutoScrapper14m
    • 142AutoScraper and Flask: Create an API From Amazon Website in Less Than 10 Minutes14m
    • 143Autoviz-Automatically Visualize Any Dataset With Single Line Of Code6m
    • 144AutoScraper- Scrap Images From Amazon Ecommerce- End To End Web Scraping Application7m

  4. Day 39 · 54m

    • 145All Type Of Cross Validation With Python All In 1 Video15m
    • 146DataPrep Library- Perform Faster EDA Within No Time9m
    • 147Time Series Forecasting Made Easy Using Dart Library - Perform Multivariate Forecasting In No Time12m
    • 148FLAML - Fast and Lightweight AutoML Library By Microsoft8m
    • 149Tutorial on Automated Machine Learning using MLBox11m

  5. Day 40 · 27m

    • 150Definition Of Bias And Variance In Machine Learning- Interview Question8m
    • 151Elasticnet Regression Machine Learning Algorithm Explained In Depth11m
    • 152Out Of Bag Evaluation(OOB) And OOB Score Or Error In Random Forest7m

  6. Day 41 · 1h 29m

    • 153PCA Indepth Geometric And Mathematical InDepth Intuition ML Algorithms1h 29m

Tips for finishing

  • Skip the Python section if you already know it by setting the planner's From field.
  • Do at least one of the end-to-end projects yourself; they're the most portfolio-ready part.

Frequently asked questions

How long does it take to finish Krish Naik's Complete Machine Learning Playlist?

The playlist has 153 videos and 36.9 hours of content. At 1 hour a day that's about 37 days at normal speed, or 25 days at 1.5x. At 2 hours a day it's about 19 days.

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