Machine Learning
Explore data analysis, exploratory data analysis (EDA), feature engineering, and the implementation of classic machine learning models using Scikit-Learn, Pandas, and NumPy.
Machine Learning Tutorials (39)
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Feature Engineering: Variable Magnitude
Understand the impact of feature magnitude on ML algorithms, and learn scaling techniques in Python including Standard, MinMax, and Robust scaling.
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Feature Engineering: Outlier Detection
Detect and handle outliers in Python using IQR and Z-score methods, with boxplot and Q-Q plot visualization and practical boundary calculations.
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Feature Engineering: Linear Model Assumptions
Detect and fix violations of linear model assumptions: linearity, normality, homoscedasticity, and multicollinearity, with Q-Q plots and log transforms.
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Feature Engineering: Rare Labels
Learn what rare labels are in categorical variables, why they cause overfitting and train/test mismatches, and how to group them safely in Python.
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Cardinality in Machine Learning
Understand cardinality in categorical variables and its effect on model performance. Learn to handle high-cardinality features using Python techniques.
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Missing Values and Their Mechanisms
Understand MCAR, MAR, and MNAR missing data mechanisms and their impact on machine learning. Covers detection, analysis, and treatment strategies using Python.
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Data Variable Types Every Data Scientist Needs
A practical guide to the four variable types in any dataset: numeric, categorical, date-time, and mixed, with examples from a real loan dataset.
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Matplotlib Crash Course
A hands-on crash course covering matplotlib's pyplot API and object-oriented interface: line plots, scatter, bar, histograms, box plots, subplots, and axis controls.
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Data Visualization with Pandas
A hands-on guide to building line, bar, histogram, box, scatter, KDE, Andrews curve, and subplot visualizations directly from a pandas DataFrame or Series.
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Pandas Crash Course
Learn the fundamentals of pandas DataFrames, loading CSVs, column operations, handling missing values, mean imputation, and correlation analysis.
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Resume and CV Summarization
Build a resume parser using spaCy NER trained on 200 resumes. Extract names, skills, and experience fields automatically from new CV documents in Python.
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LinkedIn Profile Scraper in Python
Scrape public LinkedIn profile data using Selenium and BeautifulSoup in Python. Covers automated login, profile extraction, and exporting structured results.
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LinkedIn Auto Connect Bot
Build a LinkedIn automation bot in Python using Selenium and BeautifulSoup that sends personalized connection requests to suggested profiles automatically.
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Real-Time Phone Call Sentiment Analysis
Build a Python pipeline that transcribes live microphone audio and classifies sentiment polarity in real time using NLTK and TextBlob.
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Download HD Wallpapers from Unsplash API
Automate HD wallpaper downloads from Unsplash using Python and the Unsplash API. Covers API authentication, search parameters, and automatic image saving.
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Star Rating Prediction with SVM and TF-IDF
Predict Amazon product star ratings from review text using TF-IDF vectorization and a Support Vector Machine classifier in Python with scikit-learn.
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Complete Seaborn Tutorial in Python
A hands-on guide to seaborn covering relational, categorical, distribution, and regression plots with the tips, fmri, iris, and Titanic datasets.
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Sentiment Analysis with Scikit-learn
Build a binary sentiment classifier for IMDB movie reviews using TF-IDF text vectorization and a Linear Support Vector Machine in Python with scikit-learn.
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Multi-Label Text Classification on Stack Overflow Tag Prediction
Predict Stack Overflow tags with multi-label classification: TF-IDF vectorization, OneVsRest strategy, and Hamming loss and Jaccard score evaluation.
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Feature Selection with ROC-AUC and MSE
Select features with ROC-AUC for classification and MSE for regression: score every feature individually, rank them, and keep the most predictive.
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Feature Selection: Fisher Score & Chi2
Apply Fisher Score and Chi-squared tests for feature selection on the Titanic dataset in Python. Covers categorical feature scoring with scikit-learn chi2.
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Feature Selection: Univariate ANOVA Test for Classification
Use univariate ANOVA F-tests to rank and select the most informative classification features with f_classif and SelectKBest in scikit-learn.
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Feature Selection with Mutual Information
Learn how to use mutual information (entropy gain) to select the most predictive features for classification and regression in Python with scikit-learn.
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Feature Selection with Filter Method
Remove constant, quasi-constant, and duplicate features from ML datasets using Python. Covers VarianceThreshold and correlation-based duplicate feature removal.
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Dimensionality Reduction with LDA and PCA in Python
Reduce high-dimensional feature spaces with LDA and PCA in scikit-learn: applied to the Santander dataset with accuracy and speed comparisons.
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Lasso and Ridge Coefficients for Feature Selection
Learn how to use linear and logistic regression coefficients with Lasso (L1) and Ridge (L2) regularization to select the most informative features in Python.
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Recursive Feature Elimination (RFE) in Python
Apply Recursive Feature Elimination (RFE) with Random Forest and Gradient Boosting to select the most predictive breast cancer dataset features.
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Step Forward, Step Backward, and Exhaustive Feature Selection
Learn how to use wrapper-based feature selection: Sequential Forward, Backward, and Exhaustive Search with mlxtend and scikit-learn on the Wine dataset.
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Lasso and Ridge Regularization for Feature Selection
Learn how Lasso (L1) and Ridge (L2) regularization act as embedded feature selectors. Apply SelectFromModel and RidgeClassifierCV on the Titanic dataset in Python.
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Logistic Regression with Python
From sigmoid to cost function. Build a Titanic survival classifier with scikit-learn, recursive feature elimination, and ROC-AUC evaluation.
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PCA with Python: Principal Component Analysis
Learn how PCA works, then reduce 30 breast-cancer features to 2 components with scikit-learn while retaining maximum variance.
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KNN Algorithm in Python
Implement a tuned K-Nearest Neighbors classifier with scikit-learn, including feature standardization and cross-validation to find the optimal K.
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K-Means Clustering in Python
Learn how K-Means clustering works and implement it with scikit-learn: centroid initialization, the elbow method, inertia, and cluster visualization.
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Linear Regression with Python
Learn linear regression with scikit-learn on the Boston housing dataset: simple and multiple regression, feature selection, and R2, MAE, MSE evaluation.
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Random Forest Classifier and Regressor with Python
Learn how Random Forest combines decision trees through bagging, train a regressor and a classifier with scikit-learn and extract feature importances.
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Speed Up Training with Bagging
Cut training time by splitting data across parallel estimators. Implement a BaggingClassifier with SVM on Iris and benchmark against a single model.
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Ensemble Learning in Python
Learn how bagging, boosting, and voting combine models to boost accuracy. Train Random Forest, AdaBoost, Gradient Boosting, and XGBoost with scikit-learn.
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Decision Tree in Python
Train decision tree classifiers and regressors in Python with scikit-learn. Covers splitting criteria, key hyperparameters, pruning, and model evaluation.
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SVM with Python: Support Vector Machines
Learn how SVMs work: hyperplanes, margin maximization, and kernel tricks, and train classifiers on the breast cancer dataset with scikit-learn.
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