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Machine Learning Tutorial in Python (codebasics)

By codebasics

Course audio
Course audio: English
Level
Beginner
Length
42 lessons · 12 hours

What you'll be able to do

  • Train common scikit-learn models with short exercises
  • Tune models with cross-validation and regularisation
  • Save and deploy a trained model
  • Engineer features and remove outliers

About this course

Codebasics' practical machine learning series in Python: 42 lessons, about 12 hours. It covers linear regression with one and many variables, gradient descent and the cost function, saving models with joblib and pickle, one-hot encoding, train-test split, logistic regression, decision trees, SVM, random forest, k-fold cross-validation, K-means, Naive Bayes, hyperparameter tuning, L1 and L2 regularisation and more, each with a short exercise. It then walks through an end-to-end data science project with model deployment and finishes with feature engineering and outlier removal. A friendly, code-first course for beginners and freelancers.

Before you start: Basic Python and pandas

Syllabus

42 lessons · 12:00:44 in total

  1. 1Machine Learning Tutorial Python -1: What is Machine Learning?6:51
  2. 2Machine Learning Tutorial Python - 2: Linear Regression Single Variable15:14
  3. 3Machine Learning Tutorial Python - 3: Linear Regression Multiple Variables14:08
  4. 4Machine Learning Tutorial Python - 4: Gradient Descent and Cost Function28:26
  5. 5Machine Learning Tutorial Python - 5: Save Model Using Joblib And Pickle8:21
  6. 6Machine Learning Tutorial Python - 6: Dummy Variables & One Hot Encoding21:35
  7. 7Machine Learning Tutorial Python - 7: Training and Testing Data6:34
  8. 8Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification)19:19
  9. 9Machine Learning Tutorial Python - 8 Logistic Regression (Multiclass Classification)15:43
  10. 10Machine Learning Tutorial Python - 9 Decision Tree14:46
  11. 11Machine Learning Tutorial Python - 10 Support Vector Machine (SVM)23:22
  12. 12Machine Learning Tutorial Python - 11 Random Forest12:48
  13. 13Machine Learning Tutorial Python 12 - K Fold Cross Validation25:20
  14. 14Machine Learning Tutorial Python - 13: K Means Clustering Algorithm25:15
  15. 15Machine Learning Tutorial Python - 14: Naive Bayes Classifier Algorithm Part 113:38
  16. 16Machine Learning Tutorial Python - 15: Naive Bayes Classifier Algorithm Part 211:28
  17. 17Machine Learning Tutorial Python - 16: Hyper parameter Tuning (GridSearchCV)16:30
  18. 18Machine Learning Tutorial Python - 17: L1 and L2 Regularization | Lasso, Ridge Regression19:21
  19. 19Machine Learning Tutorial Python - 18: K nearest neighbors classification with python code15:42
  20. 20Machine Learning Tutorial Python - 19: Principal Component Analysis (PCA) with Python Code24:09
  21. 21Machine Learning Tutorial Python - 20: Bias vs Variance In Machine Learning10:50
  22. 22Machine Learning Tutorial Python - 21: Ensemble Learning - Bagging23:37
  23. 23Machine Learning & Data Science Project - 1 : Introduction (Real Estate Price Prediction Project)2:12
  24. 24Machine Learning & Data Science Project - 2 : Data Cleaning (Real Estate Price Prediction Project)15:21
  25. 25Machine Learning & Data Science Project - 3 : Feature Engineering (Real Estate Price Prediction)8:26
  26. 26Machine Learning & Data Science Project - 4 : Outlier Removal (Real Estate Price Prediction Project)19:31
  27. 27Machine Learning & Data Science Project - 5 : Model Building (Real Estate Price Prediction Project)19:12
  28. 28Machine Learning & Data Science Project - 6 : Python Flask Server (Real Estate Price Prediction)21:13
  29. 29Machine Learning & Data Science Project - 7 : Website or UI (Real Estate Price Prediction Project)13:21
  30. 30Deploy machine learning model to production AWS (Amazon EC2 instance)28:39
Show all 42 lessons
  1. 31Data Science & Machine Learning Project - Part 1 Introduction | Image Classification4:42
  2. 32Data Science & Machine Learning Project - Part 2 Data Collection | Image Classification5:37
  3. 33Data Science & Machine Learning Project - Part 3 Data Cleaning | Image Classification40:45
  4. 34Data Science & Machine Learning Project - Part 4 Feature Engineering | Image Classification18:23
  5. 35Data Science & Machine Learning Project - Part 5 Training a Model | Image Classification20:01
  6. 36Data Science & Machine Learning Project - Part 6 Flask Server | Image Classification36:16
  7. 37Data Science & Machine Learning Project - Part 7 Build Website | Image Classification38:14
  8. 38Data Science & Machine Learning Project - Part 8 Deployment & Exercise | Image Classification5:17
  9. 39What is feature engineering | Feature Engineering Tutorial Python # 15:12
  10. 40Outlier detection and removal using percentile | Feature engineering tutorial python # 217:18
  11. 41Outlier detection and removal: z score, standard deviation | Feature engineering tutorial python # 320:05
  12. 42Outlier detection and removal using IQR | Feature engineering tutorial python # 48:02

What to take next

Videos by codebasics, played from their official YouTube channel. Subscribe on YouTube

Official upload by the teacher or institution on their own channel, shown with YouTube's embedded player.

Lessons listed from the official YouTube playlist. Full playlist on YouTube

Lessons last checked Oct 2026. Course summary and lesson structure by Sabaq.

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