codebasics Machine Learning Tutorial Python: study plan and how long it takes
The codebasics playlist is 42 videos and 12.0 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.
42 videos · 12.0 hours · codebasics
codebasics' Machine Learning Tutorial Python series is a practical, scikit-learn-based introduction: linear and logistic regression, saving models, decision trees, SVM and random forests, k-fold cross-validation, hyperparameter tuning, bias vs variance, then end-to-end projects including a real estate price prediction website, and outlier detection.
Each tutorial ends with an exercise, which makes it very hands-on.
Who it's for: Python programmers who want a practical, code-first introduction to machine learning.
How long does it take?
The full playlist is 12.0 hours across 42 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:
| Daily time | At 1x | At 1.5x | At 2x |
|---|---|---|---|
| 30 min a day | 25 days | 17 days | 13 days |
| 1 h a day | 13 days | 9 days | 7 days |
| 2 h a day | 7 days | 5 days | 4 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.
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 3 weeks in total. For a different pace, start date or speed, use the planner above.
Week 1Videos 1–20 · 5h 39m
Day 1 · 36m
- 1Machine Learning Tutorial Python -1: What is Machine Learning?7m
- 2Machine Learning Tutorial Python - 2: Linear Regression Single Variable15m
- 3Machine Learning Tutorial Python - 3: Linear Regression Multiple Variables14m
Day 2 · 58m
- 4Machine Learning Tutorial Python - 4: Gradient Descent and Cost Function28m
- 5Machine Learning Tutorial Python - 5: Save Model Using Joblib And Pickle8m
- 6Machine Learning Tutorial Python - 6: Dummy Variables & One Hot Encoding22m
Day 3 · 56m
- 7Machine Learning Tutorial Python - 7: Training and Testing Data7m
- 8Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification)19m
- 9Machine Learning Tutorial Python - 8 Logistic Regression (Multiclass Classification)16m
- 10Machine Learning Tutorial Python - 9 Decision Tree15m
Day 4 · 36m
- 11Machine Learning Tutorial Python - 10 Support Vector Machine (SVM)23m
- 12Machine Learning Tutorial Python - 11 Random Forest13m
Day 5 · 51m
- 13Machine Learning Tutorial Python 12 - K Fold Cross Validation25m
- 14Machine Learning Tutorial Python - 13: K Means Clustering Algorithm25m
Day 6 · 42m
- 15Machine Learning Tutorial Python - 14: Naive Bayes Classifier Algorithm Part 114m
- 16Machine Learning Tutorial Python - 15: Naive Bayes Classifier Algorithm Part 211m
- 17Machine Learning Tutorial Python - 16: Hyper parameter Tuning (GridSearchCV)17m
Day 7 · 59m
- 18Machine Learning Tutorial Python - 17: L1 and L2 Regularization | Lasso, Ridge Regression19m
- 19Machine Learning Tutorial Python - 18: K nearest neighbors classification with python code16m
- 20Machine Learning Tutorial Python - 19: Principal Component Analysis (PCA) with Python Code24m
Week 2Videos 21–39 · 5h 37m
Day 8 · 52m
- 21Machine Learning Tutorial Python - 20: Bias vs Variance In Machine Learning11m
- 22Machine Learning Tutorial Python - 21: Ensemble Learning - Bagging24m
- 23Machine Learning & Data Science Project - 1 : Introduction (Real Estate Price Prediction Project)2m
- 24Machine Learning & Data Science Project - 2 : Data Cleaning (Real Estate Price Prediction Project)15m
Day 9 · 47m
- 25Machine Learning & Data Science Project - 3 : Feature Engineering (Real Estate Price Prediction)8m
- 26Machine Learning & Data Science Project - 4 : Outlier Removal (Real Estate Price Prediction Project)20m
- 27Machine Learning & Data Science Project - 5 : Model Building (Real Estate Price Prediction Project)19m
Day 10 · 35m
- 28Machine Learning & Data Science Project - 6 : Python Flask Server (Real Estate Price Prediction)21m
- 29Machine Learning & Data Science Project - 7 : Website or UI (Real Estate Price Prediction Project)13m
Day 11 · 39m
- 30Deploy machine learning model to production AWS (Amazon EC2 instance)29m
- 31Data Science & Machine Learning Project - Part 1 Introduction | Image Classification5m
- 32Data Science & Machine Learning Project - Part 2 Data Collection | Image Classification6m
Day 12 · 59m
- 33Data Science & Machine Learning Project - Part 3 Data Cleaning | Image Classification41m
- 34Data Science & Machine Learning Project - Part 4 Feature Engineering | Image Classification18m
Day 13 · 56m
- 35Data Science & Machine Learning Project - Part 5 Training a Model | Image Classification20m
- 36Data Science & Machine Learning Project - Part 6 Flask Server | Image Classification36m
Day 14 · 49m
- 37Data Science & Machine Learning Project - Part 7 Build Website | Image Classification38m
- 38Data Science & Machine Learning Project - Part 8 Deployment & Exercise | Image Classification5m
- 39What is feature engineering | Feature Engineering Tutorial Python # 15m
Week 3Videos 40–42 · 45m
Day 15 · 45m
- 40Outlier detection and removal using percentile | Feature engineering tutorial python # 217m
- 41Outlier detection and removal: z score, standard deviation | Feature engineering tutorial python # 320m
- 42Outlier detection and removal using IQR | Feature engineering tutorial python # 48m
Tips for finishing
- Do every exercise at the end of each video before checking the solution in the linked GitHub repo.
- Build the end-to-end project yourself; it covers data cleaning, modelling and deployment.