Discrete vs Continuous Hyperparameters (Ch 2) | Hands-On Machine Learning

RandomizedSearchCV – Smarter Hyperparameter Search (Ch 2) | Hands-On Machine Learning

GridSearchCV – Line by Line (Ch 2) | Hands-On Machine Learning

Random Forest Regressor – Validation Error vs Training Error | Ch 2 | Hands-On Machine Learning

Better Evaluation Using Cross-Validation | Ch 2 | Hands-On Machine Learning

Decision Tree Regressor – Why RMSE = 0.0? (Overfitting Explained) | Ch 2 | Hands-On Machine Learning

Evaluate the Linear Regression Model (Predictions & RMSE) | Ch 2 | Hands-On Machine Learning

Select and Train the First Model (Linear Regression) | Ch 2 | Hands-On Machine Learning

Final Combined Preprocessing Pipeline + Feature Names (Ch 2) Hands-On Machine Learning

Geographical Feature Engineering with ClusterSimilarity (Ch 2) | Hands-On Machine Learning

Log Transformation Pipeline with FunctionTransformer (Ch 2) | Hands-On Machine Learning

Ratio-Based Feature Engineering with FunctionTransformer (Ch 2) | Hands-On Machine Learning

Numeric & Categorical Pipelines with ColumnTransformer (Ch 2) Hands-On Machine Learning

Getting Feature Names & DataFrame from Pipeline (Ch 2) Hands-On Machine Learning

Monkey Patch for get_feature_names_out (Ch 2) Hands-On Machine Learning

What is a Pipeline in Machine Learning? | Scikit-Learn Pipelines Explained | Learn AI with Rawal

How to Validate Feature Engineering Using Visualization (Cluster Similarity Explained)

ClusterSimilarity Explained (Turning Clusters into Powerful ML Features)

How to Build a Custom Transformer in Scikit-Learn (fit & transform Explained)

Using RBF Similarity for Geographic Features (FunctionTransformer Example)

FunctionTransformer Explained (Turning Math into ML Features)

TransformedTargetRegressor Code Explained (Safe Target Scaling in Scikit-Learn)

Manual Target Scaling Explained (StandardScaler + Inverse Transform in ML)

RBF as Feature Engineering Explained Creating Nonlinear Features for ML

Gamma Explained in RBF Kernel How Similarity Shape Changes in ML

When Percentiles Help ML Models (and When They Hurt)

Percentiles to Buckets Explained (np.percentile, pd.cut with ML Example)

Log Transform Explained from Scratch (log, log(1+x), and Why ML Uses It)

Heavy Tailed Data Explained Why Scaling Alone Fails in Machine Learning

OneHotEncoder Advanced Guide: Unknown Categories, Feature Names & Final Encoded DataFrame Explained

Why pd.get_dummies() Fails in ML Pipelines (OneHotEncoder Comparison Explained)

OneHotEncoder Explained: The Right Way to Encode Categories (Sparse vs Dense)

OrdinalEncoder Explained: Converting Categories into Numbers (Simple Hinglish Guide)

Detecting Outliers Using IsolationForest (Simple Hinglish Explanation)

SimpleImputer Explained: Fill Missing Values the Right Way (Median Strategy)

Handling Missing Values Using Pandas dropna, drop, fillna

Which Features Matter Most Correlation + Scatter Matrix Explained

Population + Price + Location in One Chart Powerful EDA

Mapping Housing Data Longitude–Latitude Scatter Explained

StratifiedShuffleSplit Explained Line-by-Line | Correct Train–Test Split

Turning Continuous Income Into Categories | pd.cut() Explained

Random Sampling vs Reality | Why ML Needs Stratified Sampling

train_test_split in Scikit-Learn | When to Use It (and When NOT To)

Choosing the Right ID for Stable Splitting | Why Index Fails but Lat–Long Works

Stable Train–Test Split Using Hashing CRC32 Explained

Why Random Splitting Is Dangerous

shuffle_and_split_data Explained Line-by-Line | Random Splitting in HOML

Random Train–Test Split Explained | Why This Is the First Approach Beginners Learn

What Is a Test Set? | Data Snooping Explained in Simple Words | HOML Chapter 2

How to Read Histograms Like a Data Scientist | Learn AI with Rawal (Hindi)

How to Explore Any ML Dataset Step-by-Step | Learn AI with Rawal (Hindi)

K Nearest Neighbors (KNN) Explained | Hands-On Machine Learning Chapter 1 | Learn AI with Rawal

Linear Regression Explained with Practical Demo | HOML Chapter 1 | Learn AI with Rawal