Mathew K AnalyticsLearn scikit-learn with free notebooks
150 free video lessons that use scikit-learn, each with a downloadable Jupyter notebook and, where needed, the dataset. Learn scikit-learn by working through real examples.
10 minLesson 25
Machine Learning for Beginners in Python | Data Analytics #25
11 minLesson 26
Feature Engineering & Model Evaluation | Data Analytics #26
15 minLesson 30
End-to-End Analytics Capstone Project | Data Analytics #30
23 minLesson 10
Intro to Machine Learning with scikit-learn
25 minLesson 1
Linear Regression from Scratch in Python | ML Algorithms #1
21 minLesson 2
Logistic Regression for Classification | ML Algorithms #2
20 minLesson 3
K-Nearest Neighbours (KNN) Explained | ML Algorithms #3
17 minLesson 4
Decision Trees: How They Really Work | ML Algorithms #4
17 minLesson 5
Random Forests Explained with Python | ML Algorithms #5
17 minLesson 6
Gradient Boosting Explained Step by Step | ML Algorithms #6
17 minLesson 7
Support Vector Machines Made Intuitive | ML Algorithms #7
16 minLesson 8
Naive Bayes Classifier Explained | ML Algorithms #8
19 minLesson 9
K-Means Clustering in Python | ML Algorithms #9
15 minLesson 10
Hierarchical Clustering & Dendrograms | ML Algorithms #10
15 minLesson 11
PCA & Dimensionality Reduction Explained | ML Algorithms #11
15 minLesson 12
Comparing Every ML Algorithm: Capstone Project | ML Algorithms #12
17 minLesson 1
Scikit-learn Tutorial #1: The Estimator API Explained
17 minLesson 2
Scikit-learn Tutorial #2: Preprocessing — Scalers
16 minLesson 3
Scikit-learn Tutorial #3: Preprocessing — Encoders
15 minLesson 4
Scikit-learn Tutorial #4: Handling Missing Data
16 minLesson 5
Scikit-learn Tutorial #5: Feature Engineering
15 minLesson 6
Scikit-learn Tutorial #6: Pipelines
15 minLesson 7
Scikit-learn Tutorial #7: ColumnTransformer
15 minLesson 8
Scikit-learn Tutorial #8: Splitting & Cross-Validation
15 minLesson 9
Scikit-learn Tutorial #9: Hyperparameter Tuning
15 minLesson 10
Scikit-learn Tutorial #10: Classification Metrics
14 minLesson 11
Scikit-learn Tutorial #11: Regression Metrics
14 minLesson 12
Scikit-learn Tutorial #12: Clustering Metrics
14 minLesson 13
Scikit-learn Tutorial #13: Feature Selection
14 minLesson 14
Scikit-learn Tutorial #14: Text Data & Vectorisation
14 minLesson 15
Scikit-learn Tutorial #15: Ensembling & Meta-Estimators
15 minLesson 16
Scikit-learn Tutorial #16: Custom Estimators
15 minLesson 17
Scikit-learn Tutorial #17: Model Persistence & Diagnostics
16 minLesson 18
Scikit-learn Tutorial #18: Capstone — End-to-End ML Pipeline
21 minLesson 30
Customer Clustering Using K-Means
28 minLesson 35
Correlation and Key Driver Analysis in Market Research
22 minLesson 45
Visualizing Text-Based Market Insights in Python for Market Research
24 minLesson 49
Retention and Churn Analysis Using Python for Market Research
27 minLesson 50
Introductory Market Trend Forecasting
27 minLesson 54
Best Practices for Market Research Visuals | Python Analytics Tutorial
21 minLesson 57
Customer Segmentation Case Study Using Python for Market Research Analytics
19 minLesson 53
Essential Data Preparation Techniques for Effective scikit-learn Machine Learning Models
26 minLesson 19
Encoding Categorical Retail Variables for Machine Learning in Python
21 minLesson 30
Customer Clustering Using K-Means in Python for Retail Analytics
27 minLesson 41
Introduction to Machine Learning in Retail: Practical Training for E-commerce Analytics
20 minLesson 43
Product Recommendation Systems Training for E-commerce Analytics with Python
23 minLesson 45
Demand Prediction Using Machine Learning for Retail E-Commerce Analytics
19 minLesson 57
Customer Segmentation Case Study: Python Training for Retail E-commerce Analytics
30 minLesson 51
Introduction to Machine Learning for Social Media
21 minLesson 54
Feature Importance for Engagement Prediction
17 minLesson 56
1 - Supervised vs Unsupervised Learning in Python
15 minLesson 57
2 - Train-Test Split in Python
14 minLesson 58
3 - Regression and Classification in Python
13 minLesson 60
5 - Feature Scaling in Python
11 minLesson 61
Understanding Decision Trees and Random Forests in Python for Classification
25 minLesson 2
Setting Up Python Jupyter Notebooks for Probability and Statistics Analysis
24 minLesson 9
Master Bayes Theorem: Real-Life Examples & Step-by-Step Probability Explained
17 minLesson 19
Understanding Monte Carlo Simulations in Python for Statistical Modeling and Analysis
29 minLesson 21
Practical Applications of Simulation Techniques in Statistical Analysis and Decision-Making
12 minLesson 23
Simple Linear Regression in Python: Model Fitting & Interpretation Guide
12 minLesson 25
Understanding Regression Diagnostics: Residuals and Key Assumptions Explained
10 minLesson 26
Practical Regression Project: Predicting Housing Prices with Python and Data Analysis
12 minLesson 28
Logistic Regression Explained: Master Coefficients & Odds Ratios Easily
14 minLesson 29
Master Accuracy, Precision & Recall for Evaluating Classification Models in Python
12 minLesson 30
Understanding ROC Curves and AUC for Evaluating Classification Models in Python
12 minLesson 40
Comprehensive Time Series Forecasting Project: From Data Preparation to Deployment
34 minLesson 41
Comprehensive Statistical Analysis: Step-by-Step Capstone Project Case Study
22 minLesson 15
Analyzing and Predicting Iris Flower Species Using Data Science Techniques
23 minLesson 17
Building a Car Price Prediction Model Using Python and Data Science Techniques
18 minLesson 26
Predicting Wine Quality with Machine Learning: A Step-by-Step Guide Using Python
25 minLesson 27
Building Accurate Disease Prediction Models with Machine Learning: A Step-by-Step Guide
23 minLesson 29
Predicting Heart Disease with Logistic Regression: A Step-by-Step Python Guide
15 minLesson 30
Building a House Price Prediction Model with Python: Data Science and Machine Learning
17 minLesson 31
Applying Linear Regression to the Boston Housing Dataset: A Step-by-Step Guide
24 minLesson 33
Classifying Cancer Cells with Scikit-learn: A Step-by-Step Guide for Beginners
24 minLesson 35
Predicting Box Office Revenue with Linear Regression: A Step-by-Step Guide
27 minLesson 36
Online Payment Fraud Detection Using Machine Learning in Python | Data Science Project
24 minLesson 38
Building and Training Handwritten Digit Recognition Models with Scikit Learn
26 minLesson 40
Building a Credit Card Fraud Detection System with Machine Learning Techniques
20 minLesson 41
Building a Recommendation System in Python: A Step-by-Step Guide for Beginners
23 minLesson 49
Deep Learning for Wine Type Classification: Step-by-Step Model Training Guide
22 minLesson 51
Building a Neural Network for Handwritten Digit Recognition: A Step-by-Step Guide
24 minLesson 53
Logistic Regression for Handwritten Digit Recognition Using PyTorch: Step-by-Step Tutorial
17 minLesson 73
Customer Churn Prediction: Step-by-Step Classification Models in Python
13 minLesson 76
4 - Loan Default Prediction - Risk Modeling
19 minLesson 77
Predicting House Prices with Regression Using the Ames Housing Dataset
17 minLesson 79
Applying Binary Classification for Accurate Diabetes Prediction in Healthcare
15 minLesson 83
Detecting Fake News with NLP and Transformer Models: A Biblical Perspective
22 minLesson 1
Understanding Data Mining and the Knowledge Discovery in Databases (KDD) Process
22 minLesson 2
Data Mining vs Data Science vs Machine Learning: Key Differences Explained
26 minLesson 4
Introduction to Python, Pandas, and scikit-learn for Data Analysis and Machine Learning
20 minLesson 7
Understanding Data Transformation: Normalization, Encoding, and Feature Scaling in Machine Learning
22 minLesson 8
Understanding Principal Component Analysis (PCA) for Dimensionality Reduction in Data
25 minLesson 11
Foundations of Exploratory Data Mining in Descriptive Analytics
26 minLesson 16
Understanding Decision Trees: ID3, C4.5, and CART Algorithms for Classification
29 minLesson 17
Mastering Naive Bayes Classifier: Key Concepts and Real-World Data Analysis Applications
15 minLesson 19
Master Logistic Regression for Binary Classification: Step-by-Step Practical Guide
20 minLesson 20
Understanding Classifier Evaluation: Accuracy, Precision, Recall, and F1-Score Explained
19 minLesson 21
Understanding ROC Curves, AUC, and Confusion Matrix for Classification Model Evaluation
23 minLesson 22
Customer Churn Prediction: Step-by-Step Python Tutorial Using Real Data
16 minLesson 24
Understanding the k-Means Clustering Algorithm: A Clear Step-by-Step Guide
21 minLesson 26
Understanding Density-Based Clustering with DBSCAN: Principles and Python Implementation
15 minLesson 27
Understanding 5 Key Cluster Evaluation Metrics for Effective Machine Learning Analysis
16 minLesson 28
Understanding Socio-Economic Segmentation Through Hands-On Clustering Techniques
22 minLesson 29
Predicting Diabetes Using Ensemble Machine Learning Models: A Step-by-Step Guide
23 minLesson 30
Understanding Bagging and Random Forests: Ensemble Methods in Machine Learning
17 minLesson 31
Understanding AdaBoost and XGBoost: Key Boosting Techniques in Machine Learning
23 minLesson 32
Support Vector Machines (SVM): Principles, Methods, and Practical Applications
19 minLesson 35
Foundations of Anomaly and Outlier Detection in Data Analysis Explained
19 minLesson 36
Understanding Statistical Outlier Detection: Methods and Practical Applications in Python
21 minLesson 44
Sentiment Analysis with Text Mining: A Practical Data Science Capstone Project
26 minLesson 45
Comprehensive Data Mining Capstone Project: Step-by-Step Case Study Walkthrough
15 minLesson 22
Understanding Autoregressive, Moving Average, and ARMA Models for Time Series Forecasting
17 minLesson 24
Seasonal ARIMA and SARIMAX in Python: A Guide to Time Series Forecasting
12 minLesson 28
Encoding Seasonality in Time Series Data Using Fourier Terms for Accurate Modeling
11 minLesson 29
Supervised Learning for Time Series in Python: Data Preparation and Model Setup
14 minLesson 30
Linear Regression and Regularization Techniques for Accurate Forecasting in Machine Learning
14 minLesson 31
Using Random Forest and XGBoost for Accurate Time Series Forecasting
15 minLesson 32
Walk-Forward Validation in Python: A Clear Guide for Time Series Model Evaluation
16 minLesson 33
Understanding 7 Key Forecast Accuracy Metrics for Evaluating Models in Python
10 minLesson 36
Understanding Convolutional Neural Networks for Time Series Analysis in Christian Data Studies
11 minLesson 38
Sequence-to-Sequence Forecasting in Python: Deep Learning for Time Series Prediction
10 minLesson 42
Using Multilayer Perceptrons for Time Series Forecasting in Christian Analytics
25 minLesson 29
Designing Effective Fraud Alert Thresholds for Banking Systems Using Python
11 minLesson 8
1 Introduction to Machine Learning in Python
15 minLesson 14
Understanding Accuracy, Precision, Recall, F1 Score, and ROC Curve for Model Evaluation in Python
12 minLesson 29
Understanding Simple and Multiple Linear Regression in Python for Bible Study Applications
13 minLesson 33
Understanding Ridge, Lasso, and ElasticNet Regularization in Python for Regression Analysis
16 minLesson 34
Predicting California Housing Prices with Python: A Step-by-Step Regression Guide
13 minLesson 44
Understanding K Nearest Neighbors (KNN) in Python: A Step-by-Step Guide for Classification
16 minLesson 46
Understanding Decision Trees and Random Forests in Python for Bible Teaching Applications
12 minLesson 61
Master Hyperparameter Tuning with GridSearchCV in Python for Optimal ML Models
13 minLesson 68
Understanding Principal Component Analysis (PCA) in Python: A Comprehensive Tutorial
15 minLesson 69
Customer Segmentation Using PCA and KMeans Clustering in Python: A Step-by-Step Guide
11 minLesson 75
Understanding Gradient Boosting and Implementing XGBoost in Python for Predictive Modeling
14 minLesson 1
1 Capstone ML Project in Python: Build and Evaluate a Machine Learning Model
21 minLesson 3
Comprehensive Guide to Training Machine Learning Models with Scikit-Learn in Python
12 minLesson 4
2 Train-Test Split in Python: Step-by-Step Guide for Machine Learning Beginners
10 minLesson 5
3 Cross-Validation in Python: Machine Learning Model Evaluation Techniques
21 minLesson 7
Simple Linear Regression Model in Python with scikit-learn
11 minLesson 8
Polynomial Regression in Python: Complete Guide for Machine Learning Beginners
11 minLesson 14
Titanic Survival Prediction with Python: Data Analysis & Machine Learning Basics
13 minLesson 15
Building a Heart Disease Classification Model in Python: Step-by-Step Machine Learning Guide
12 minLesson 16
Handling Missing Values and Data Imputation Techniques in Python for Machine Learning
12 minLesson 18
Feature Scaling and Pipeline Construction in Python for Effective Machine Learning
15 minLesson 22
Understanding KMeans Clustering in Python: A Guide to Unsupervised Machine Learning
12 minLesson 23
Master DBSCAN & Hierarchical Clustering in Python for Unsupervised Learning
12 minLesson 26
Master Support Vector Machines (SVM) in Python for Advanced Machine Learning
9 minLesson 28
Understanding Gradient Boosting with LightGBM in Python for Advanced Machine Learning
15 minLesson 29