Mathew K AnalyticsData Mining
A free 31-lesson course. Every lesson has a video walkthrough and a Jupyter notebook you can download or open in Google Colab. Tools covered: pandas, NumPy, Matplotlib, scikit-learn, seaborn, mlxtend.
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
13 minLesson 5
Fundamentals of Exploratory Data Analysis and Visualization for Biblical Data Insights
17 minLesson 6
Data Cleaning Fundamentals: Managing Missing Values, Noise, and Outliers in Datasets
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
13 minLesson 10
Essential Data Preprocessing Techniques Using Titanic and Iris Datasets in Python
25 minLesson 11
Foundations of Exploratory Data Mining in Descriptive Analytics
16 minLesson 12
Understanding Summarization and Descriptive Statistics in Data Mining
16 minLesson 13
Understanding Histograms, Scatterplots, and Heatmaps for Data Visualization
17 minLesson 14
Understanding Association Rule Mining with the Apriori Algorithm: A Step-by-Step Guide
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
12 minLesson 38
ARIMA Modeling in Python: Classical Forecasting Techniques Explained
21 minLesson 44
Sentiment Analysis with Text Mining: A Practical Data Science Capstone Project
26 minLesson 45