Machine LearningMay 25, 2026 at 1:30 PM7 min read
LinkedIn Auto Connect Bot
Build a LinkedIn automation bot in Python using Selenium and BeautifulSoup that sends personalized connection requests to suggested profiles automatically.
Category Profile
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)
Browse, search, and work through all available articles for this category.
Python Projects
3 articlesMachine Learning
Machine LearningMay 22, 2026 at 10:30 AM122 min read
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.
Machine LearningMay 21, 2026 at 1:30 PM10 min read
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.
Dimensionality Reduction
2 articles
Machine LearningMay 25, 2026 at 12:00 PM11 min read
Dimensionality Reduction with LDA and PCA in Python
Learn how to reduce high-dimensional feature spaces using LDA and PCA with scikit-learn. Applied to the Santander customer dataset with accuracy and speed comparisons.
Machine LearningMay 21, 2026 at 5:15 PM12 min read
PCA with Python: Principal Component Analysis
Learn how Principal Component Analysis works, then implement it in Python with scikit-learn to reduce 30 breast-cancer features down to 2 components while retaining maximum variance.
Feature Selection
9 articles
Machine LearningMay 25, 2026 at 11:15 AM12 min read
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.
Machine LearningMay 24, 2026 at 10:00 PM15 min read
Recursive Feature Elimination with Tree-Based and Gradient-Based Estimators
Learn how to apply Recursive Feature Elimination (RFE) in Python using Random Forest and Gradient Boosting estimators to select the most predictive features from the breast cancer dataset.
Machine LearningMay 23, 2026 at 4:30 PM9 min read
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.
Machine LearningMay 22, 2026 at 8:15 AM12 min read
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.
Machine LearningMay 21, 2026 at 4:30 PM14 min read
Feature Selection with ROC-AUC and MSE
Learn how to select features using ROC-AUC for classification and Mean Squared Error for regression. Score every feature individually, rank them, and keep only the most predictive ones.
Machine LearningMay 20, 2026 at 10:00 PM19 min read
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.
Machine LearningMay 20, 2026 at 9:45 AM12 min read
Feature Selection: Univariate ANOVA Test for Classification
Learn how to use univariate ANOVA F-tests to rank and select the most informative features for classification problems using scikit-learn's f_classif and SelectKBest.
Machine LearningMay 18, 2026 at 11:15 AM22 min read
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.
Machine LearningMay 17, 2026 at 5:15 PM13 min read
Regression Coefficients for Feature Selection with Lasso and Ridge
Learn how to use linear and logistic regression coefficients with Lasso (L1) and Ridge (L2) regularization to select the most informative features in Python.
Data Analysis & EDA
5 articles
Machine LearningMay 24, 2026 at 8:15 PM18 min read
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.
Machine LearningMay 24, 2026 at 9:00 AM19 min read
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.
Machine LearningMay 23, 2026 at 3:45 PM14 min read
Pandas Crash Course
Learn the fundamentals of pandas DataFrames, loading CSVs, column operations, handling missing values, mean imputation, and correlation analysis.
Machine LearningMay 20, 2026 at 10:30 AM8 min read
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.
Machine LearningMay 20, 2026 at 9:00 AM17 min read
Data Variable Types Every Data Scientist Needs
A practical guide to the four types of variables in any dataset — numeric, categorical, date-time, and mixed — with histogram and distribution examples using a real loan dataset.
ML Algorithms
9 articles
Machine LearningMay 24, 2026 at 9:45 AM12 min read
SVM with Python: Support Vector Machines
Learn how Support Vector Machines work — from hyperplanes and margin maximization to kernel tricks — and train SVM classifiers on the breast cancer dataset using scikit-learn.
Machine LearningMay 23, 2026 at 6:00 PM6 min read
Speed Up Training with Bagging
Learn how bagging reduces machine learning training time by splitting data across parallel estimators. You will implement a BaggingClassifier with SVM on the Iris dataset and compare training time against a single estimator.
Machine LearningMay 23, 2026 at 5:15 PM12 min read
Ensemble Learning in Python
Learn how ensemble learning combines multiple models using bagging, boosting, and voting to improve prediction accuracy. Train Random Forest, AdaBoost, Gradient Boosting, and XGBoost classifiers on the breast cancer dataset using scikit-learn.
Machine LearningMay 22, 2026 at 11:15 AM10 min read
Decision Tree in Python
Train decision tree classifiers and regressors in Python with scikit-learn. Covers splitting criteria, key hyperparameters, pruning, and model evaluation.
Machine LearningMay 17, 2026 at 9:00 PM20 min read
Linear Regression with Python
Learn how linear regression works and how to implement it with scikit-learn on the Boston housing dataset. Covers simple and multiple regression, feature selection by correlation, and model evaluation with R², MAE, and MSE.
Machine LearningMay 17, 2026 at 8:15 PM11 min read
K-Means Clustering in Python
Learn how K-Means clustering works and implement it in Python with scikit-learn. Covers centroid initialization, the elbow method for choosing K, inertia, and cluster visualization.
Machine LearningMay 17, 2026 at 6:00 PM11 min read
KNN Algorithm in Python
Learn how the K-Nearest Neighbors algorithm works and implement a tuned KNN classifier in Python with scikit-learn, including feature standardization and cross-validation to find the optimal K.
Machine LearningMay 17, 2026 at 3:00 PM19 min read
Logistic Regression with Python
Learn how logistic regression works — from the sigmoid function to the cost function — and build a Titanic survival classifier in Python using scikit-learn, recursive feature elimination, and ROC-AUC evaluation.
Machine LearningMay 16, 2026 at 2:15 PM11 min read
Random Forest Classifier and Regressor with Python
Learn how the Random Forest algorithm combines multiple decision trees through bagging to build robust classifiers and regressors. You will train both a regressor on the Diabetes dataset and a classifier on the Iris dataset, extract feature importances, and evaluate results.
Text & Sentiment
5 articles
Machine LearningMay 23, 2026 at 1:30 PM16 min read
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.
Machine LearningMay 22, 2026 at 9:45 AM8 min read
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.
Machine LearningMay 20, 2026 at 8:15 AM12 min read
Multi-Label Text Classification on Stack Overflow Tag Prediction
Build a multi-label text classifier that predicts Stack Overflow tags from question text using TF-IDF vectorization and the OneVsRest strategy, then evaluate it with Hamming loss and Jaccard score.
Machine LearningMay 19, 2026 at 6:00 PM9 min read
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.
Machine LearningMay 18, 2026 at 8:15 AM11 min read
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.
Feature Engineering
4 articles
Machine LearningMay 21, 2026 at 3:45 PM11 min read
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.
Machine LearningMay 19, 2026 at 5:15 PM13 min read
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.
Machine LearningMay 19, 2026 at 3:45 PM15 min read
Feature Engineering: Outlier Detection
Learn how to detect and handle outliers in machine learning using Python. Covers IQR and Z-score methods, visualization with boxplots and Q-Q plots, and practical boundary calculations on real datasets.
Machine LearningMay 16, 2026 at 12:00 PM23 min read
Feature Engineering: Linear Model Assumptions
Learn how to detect and fix violations of linear model assumptions — linearity, normality, homoscedasticity, and multicollinearity — using Python, scatter plots, Q-Q plots, and log transformations on the Boston housing dataset.
Data Preprocessing
2 articles
Machine LearningMay 21, 2026 at 3:00 PM12 min read
Cardinality in Machine Learning
Understand cardinality in categorical variables and its effect on model performance. Learn to handle high-cardinality features using Python techniques.
Machine LearningMay 19, 2026 at 7:30 PM6 min read
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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