Machine Learning for Job Interviews Hindi : Zero to Hero

Want to Master Machine Learning but Struggling with Complex English Content?

Welcome to India's most comprehensive Machine Learning Bootcamp - 142+ lectures, 60+ hours of in-depth content, entirely in Hindi! From absolute zero to industry-ready ML engineer, this course covers everything you need.

Course Features:

• Zero Prerequisites Required

• 60+ Hours of Content

• Project Based Learning

• 100% Hindi/Hinglish Explanations

• Clear ML/DS interviews with confidence

• Q&A support in discussion section

• Regular content updates

• 30-Day Money-Back Guarantee

  
  

What Makes This Course Different?

Unlike other courses that rush through concepts, every single algorithm and technique includes both mathematical intuition AND practical implementation. You'll understand the "why" behind every decision, not just the "how."

Complete Course Curriculum:

SECTION 1: Python Programming Foundation (4 Hours)

• Complete Python from scratch for absolute beginners

• No prior coding experience needed

• Hands-on exercises and practice problems

SECTION 2: Object-Oriented Programming (5 Hours)

• Complete OOPs concepts explained in Hindi

• Real-world examples and applications

• Build strong programming foundation

SECTION 3: Essential Data Science Libraries

• NumPy (1 Hour): Arrays, matrices, numerical operations

• Pandas (2 Hours): Data manipulation and analysis mastery

• Streamlit (1 Hour): Build interactive web applications

• Complete practical implementations included

SECTION 4: Exploratory Data Analysis (EDA)

• Matplotlib and Seaborn complete course (1.5 Hours)

• 2 Complete EDA Projects (Minor + Major)

• Learn to extract insights from raw data

• Industry-standard visualization techniques

SECTION 5: Feature Engineering - The Game Changer (25+ Hours)

This is where you'll learn what separates good ML engineers from great ones:

Encoding Techniques:

• One Hot Encoding

• Label Encoding

• Ordinal Encoding

• Mathematical + Practical explanation for each

Feature Scaling:

• Standardization

• Normalization

• When to use which technique

Outlier Handling:

• Z-Score Method

• IQR Method

• Percentile Method

• Winsorization

• Complete mathematical intuition

Mathematical Transformations:

• Log Transformation

• Square Root Transformation

• Reciprocal Transformation

• Box-Cox Transformation

• Yeo-Johnson Transformation

Imbalanced Data Handling:

• Random Oversampling

• SMOTE (Synthetic Minority Over-sampling)

• Under-Sampling

• Hybrid Sampling techniques

Advanced Feature Engineering:

• Reverse Encoding (Binning techniques)

• Date/Time Feature Engineering

• Mixed Feature Handling

• Feature Construction

• Feature Extraction (PCA, UMAP)

• Curse of Dimensionality solutions

Missing Values Handling (20+ Techniques):

• Univariate Imputation (Mean, Median, Mode, Custom, Forward/Backward Fill, Interpolation, Moving Average, End of Distribution)

• Categorical Imputation (Hot Deck, Proxy Variable)

• Multivariate Imputation (MICE, KNN, Regression, Random Forest)

• When to use which technique - complete decision framework

Feature Selection:

• Correlation Coefficient

• Chi-Square Test

• ANOVA Test

• Mutual Information

• Variance Threshold

• Forward Selection

• RFE (Recursive Feature Elimination)

• BORUTA Algorithm

Feature Engineering Project: Build your own Automated Feature Engineering Tool - a portfolio project that showcases advanced skills!

SECTION 6: Complete Machine Learning Algorithms

Every algorithm includes:

- Mathematical Intuition (How it works internally)

- Practical Implementation (Code from scratch)

- Hyperparameters Explanation

- When to Use & When NOT to Use

- Advantages & Disadvantages

Regression Algorithms:

• Linear Regression (Simple & Multiple)

  • Assumptions with practical proofs

  • Complete mathematical derivation

• Lasso Regression

  • Bias-Variance Tradeoff

  • Proof of Sparsity

  • Why preferred over Linear Regression

• Ridge Regression

• ElasticNet Regression

• Comparison: Lasso vs Ridge vs ElasticNet

Classification Algorithms:

• Logistic Regression (Complete mathematical working)

• Naive Bayes (All types explained)

• K-Nearest Neighbors (KNN) for Classification & Regression

• Support Vector Machine (SVM)

  • Deep mathematical intuition

  • Kernel tricks explained

• Decision Trees (Classification & Regression)

  • Gini Index & Entropy explained

  • Tree pruning techniques

Ensemble Learning:

• Bagging vs Boosting (Complete comparison)

• Random Forest (In-depth coverage)

• Gradient Boosting (Mathematical working)

• XGBoost (How it's different from Gradient Boost)

• AdaBoost (Complete algorithm breakdown)

• CatBoost (Handling categorical features)

• LightGBM (LGBM) - Fastest boosting algorithm

Clustering Algorithms:

• K-Means Clustering

  • ELBOW Method for optimal K

  • Mathematical working

• DBSCAN (Density-based clustering)

• Hierarchical Clustering

• Complete comparison of all clustering types

Model Evaluation & Optimization:

• Complete Supervised ML Metrics

  • Classification Metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC)

  • Regression Metrics (MAE, MSE, RMSE, R², Adjusted R²)

  • Mathematical understanding of each metric

• Unsupervised ML Metrics

  • Inertia

  • Silhouette Score

• Hyperparameter Tuning (Grid Search, Random Search)

Mathematics for Machine Learning:

• Complete Calculus for Data Science:

  • Differentiation fundamentals

  • Chain Rule, Product Rule, Quotient Rule

  • Partial Derivatives

  • Second Derivatives

  • Real-world use cases in ML

• Gradient Descent Optimizer:

  • Mathematical intuition

  • How ML models learn

  • Advantages & Disadvantages

5 Projects with Deployment:

Project 1: EDA Minor Project

Project 2: EDA Major Project
Project 3: Automated Feature Engineering Tool (Portfolio Highlight)

Project 4: E-Commerce ML Project with Streamlit Deployment

Project 5: Airlines Customer Satisfaction Prediction with AWS Deployment

( MORE PROJECTS COMING )

Perfect For:

• Complete beginners with zero programming experience

• Students wanting to break into AI/ML field

• Working professionals planning career transition

• Data Analysts upgrading to ML Engineer roles

• Anyone struggling with English ML content

• Engineers preparing for data science interviews

  
  

Requirements:

• A computer (Windows/Mac/Linux)

• Internet connection

• Zero prior experience needed

• We'll install everything together