Machine Learning

Machine Learning — Course #406

Teach Computers

to Learn 🤖

Machine Learning is the engine powering every modern AI system. Master regression, classification, clustering, ensemble methods, neural networks, and deployment pipelines — the core skills driving careers at Google, Amazon, and thousands of tech companies globally.

Supervised Learning Regression Classification Clustering Random Forest XGBoost Neural Networks Scikit-learn Feature Engineering MLOps

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ML — Model Training & Evaluation

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
model = GradientBoostingClassifier(
  n_estimators=200,
  learning_rate=0.05,
  max_depth=4,
  subsample=0.8
)
scores = cross_val_score(model, X, y, cv=5)
print(f"CV Accuracy: {scores.mean():.3f}")

40K+

Jobs

ML roles

48 hrs

Duration

Video content

Modules

Full syllabus

Cert

Included

Industry recognised

24/7

Support

Expert guidance

What You Master

The Complete ML Skill Set

From supervised and unsupervised learning to deep learning, NLP, RL, and production deployment.

⚙️

Supervised & Unsupervised Learning

Master the full ML algorithm spectrum — regression (Linear, Ridge, Lasso), classification (Logistic, SVM, KNN, Decision Trees, Random Forest, XGBoost, LightGBM), and clustering (K-Means, DBSCAN, Hierarchical). Tune models with Grid Search and Bayesian Optimization for peak performance.

Linear Regression Random Forest XGBoost SVM K-Means DBSCAN GridSearch Cross-Val

🧠

Neural Networks & Deep Learning

CNNs, RNNs, LSTMs — build deep learning models with TensorFlow and PyTorch for image and sequence tasks.

🔧

Feature Engineering & Optimization

Feature selection, PCA, t-SNE, hyperparameter tuning, imbalanced datasets, and cross-validation best practices.

🚀

MLOps & Model Deployment

Build end-to-end ML pipelines. Deploy models with Flask/FastAPI on AWS, GCP, or Azure with CI/CD monitoring.

Flask/FastAPI MLflow AWS/GCP

Syllabus

Course Curriculum

10 modules — from ML fundamentals to deep learning, NLP, RL, and production deployment.

What is ML? Supervised, Unsupervised, Reinforcement Learning
Applications in Healthcare, Finance, E-commerce, Autonomous Vehicles
Python Libraries Overview — NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch

Features, Labels, Training & Testing Sets
Handling Missing Values and Outliers
Feature Scaling — Normalization & Standardization
Encoding Categorical Variables
Data Visualization & Dimensionality Reduction (PCA, t-SNE, LDA)

Linear, Multiple, Polynomial, Ridge & Lasso Regression
Evaluation Metrics — RMSE, MAE, R² Score
Regularization Techniques
Implementing Regression Models with Scikit-learn

Logistic Regression, KNN, SVM, Decision Trees
Random Forest, Naïve Bayes, Gradient Boosting
Model Evaluation — Accuracy, Precision, Recall, F1, ROC-AUC
Confusion Matrix & Classification Reports

K-Means, Hierarchical Clustering, DBSCAN
Association Rule Learning — Apriori, FP-Growth
Anomaly Detection Techniques
Principal Component Analysis (PCA)

Feature Selection Techniques — Filter, Wrapper, Embedded
Hyperparameter Tuning — Grid Search, Random Search, Bayesian
Cross-Validation — K-Fold, Stratified
Handling Imbalanced Datasets — SMOTE, Class Weights

Bagging, Boosting, and Stacking
XGBoost, LightGBM, CatBoost in depth
Model Interpretability — SHAP, LIME
AutoML with Auto-sklearn and H2O

Introduction to Neural Networks & Backpropagation
Activation Functions & Optimization
Introduction to TensorFlow, Keras, PyTorch
CNNs for Image Classification, RNNs/LSTMs for Time Series & NLP

Text Preprocessing — Tokenization, Lemmatization, Stopwords
Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe)
Sentiment Analysis & Named Entity Recognition
Transformer Models — BERT, GPT

Model Deployment with Flask and FastAPI
Cloud Deployment — AWS, Google Cloud, Azure
Building Scalable ML Pipelines
Introduction to MLOps — MLflow, DVC, CI/CD

Career Outcomes

ML Careers in 2026

ML engineers are among the most sought-after professionals globally. From startups to FAANG companies, every organization building intelligent products needs ML expertise at the core of their team.

₹12–38L

India Avg LPA

$110K–$190K

USA Avg

40K+

Active Jobs

42% Growth

Year-on-Year

Machine Learning Engineer

Design, train, and deploy ML models at scale for production AI products and recommendation systems

₹14–38 LPA · $115K–$185K USA

Data Scientist

Build predictive models, run A/B experiments, and generate business insights using ML techniques

₹12–32 LPA · $105K–$165K USA

AI Engineer

Develop AI-powered features — NLP, CV, recommendation — integrating ML into production applications

₹15–40 LPA · $120K–$190K USA

Quantitative Analyst (Quant)

Apply ML to financial modeling, algorithmic trading, and risk assessment in banks and hedge funds

₹18–50 LPA · $130K–$250K USA

Teach Computers

to Learn 🤖

The Complete ML Skill Set

Course Curriculum

ML Careers in 2026

Ready to Master Machine Learning?