                    import pandas as pd
                
                    from sklearn.ensemble import RandomForestClassifier
                
                    model = RandomForestClassifier(n_estimators=100)
                
                    model.fit(X_train, y_train)
                
                    predictions = model.predict(X_test)

AI/ML Specialization

Applied Machine Learning with Python

Master practical machine learning techniques using Python. Learn to build, deploy, and scale ML models for real-world applications from prediction to automation.

4 Months Duration

Live Coding Sessions

15+ Industry Projects

ML Engineer Certificate

₹12,000 ₹48,000 75% OFF

Enroll Now View Curriculum

Model Accuracy

Feature Importance

Age: 0.35
Income: 0.28
Education: 0.22
Location: 0.15

#1 Most In-Demand Tech Skill

Curriculum

Applied ML Curriculum

From Python basics to production ML systems

Module 1: Python for Data Science

3 Weeks • 12 Hours

Python Programming Fundamentals
NumPy for Numerical Computing
Pandas for Data Manipulation
Matplotlib & Seaborn Visualization
Data Cleaning & Wrangling
Exploratory Data Analysis
Project: Customer Analytics Dashboard

Module 2: Statistical Foundations

2 Weeks • 8 Hours

Probability Theory
Descriptive Statistics
Hypothesis Testing
Correlation & Regression
Statistical Inference
A/B Testing Design
Project: Marketing Campaign Analysis

Module 3: Supervised Learning

4 Weeks • 16 Hours

Linear & Logistic Regression
Decision Trees & Random Forests
SVM & Kernel Methods
k-Nearest Neighbors
Model Evaluation Metrics
Cross-Validation Techniques
Project: Credit Risk Prediction

Module 4: Advanced ML Algorithms

3 Weeks • 12 Hours

Gradient Boosting (XGBoost, LightGBM)
Neural Networks Basics
Clustering Algorithms (k-Means, DBSCAN)
Dimensionality Reduction (PCA, t-SNE)
Recommendation Systems
Time Series Forecasting
Project: Customer Segmentation

Module 5: ML Engineering

3 Weeks • 12 Hours

Feature Engineering Pipeline
Hyperparameter Tuning
Model Deployment with Flask/FastAPI
MLflow for Experiment Tracking
Model Monitoring & Maintenance
Production Best Practices
Project: End-to-End ML Pipeline

Module 6: Capstone & Deployment

3 Weeks • 12 Hours

End-to-End Project Development
Cloud Deployment (AWS SageMaker, GCP AI)
Containerization with Docker
CI/CD for ML
Ethical AI Considerations
Portfolio Building
Capstone: Complete ML Application

Datasets

Real-World Datasets

Work with industry-standard datasets

House Price Prediction

Predict house prices based on features like location, size, and amenities.

20K Records

Medical Diagnosis

Predict disease presence based on patient symptoms and test results.

50K Patients

E-commerce Analytics

Customer purchase history for recommendation systems and churn prediction.

100K Transactions

Autonomous Driving

Sensor data for object detection and autonomous vehicle decision making.

10K Driving Hours

Applications

ML Application Domains

Transform industries with intelligent systems

Finance & Banking

Fraud detection, credit scoring, algorithmic trading, and risk assessment.

Fraud Detection Risk Analysis Algorithmic Trading

E-commerce & Retail

Recommendation engines, demand forecasting, customer segmentation, and pricing optimization.

Recommendations Forecasting Customer Analytics

Healthcare

Disease prediction, medical imaging analysis, drug discovery, and personalized medicine.

Medical Imaging Drug Discovery Patient Monitoring

Manufacturing

Predictive maintenance, quality control, supply chain optimization, and process automation.

Predictive Maintenance Quality Control Supply Chain

Algorithms

Key ML Algorithms

Master the most important machine learning algorithms

Random Forest

Ensemble learning method for classification and regression with high accuracy.

Ensemble • Supervised

XGBoost

Gradient boosting framework that's won numerous Kaggle competitions.

Boosting • Supervised

Neural Networks

Deep learning models for complex pattern recognition and prediction tasks.

Deep Learning • Supervised

k-Means Clustering

Unsupervised learning algorithm for customer segmentation and data grouping.

Clustering • Unsupervised

                import pandas as pd

                from sklearn.model_selection import train_test_split

                from sklearn.ensemble import
                RandomForestClassifier

                # Load data

                data = pd.read_csv('customer_data.csv')

                X = data.drop('churn', axis=1)

                y = data['churn']

                # Split data

                X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

                # Train model

                model = RandomForestClassifier(n_estimators=100)

                model.fit(X_train, y_train)

                # Make predictions

                predictions = model.predict(X_test)

Projects

Real-World ML Projects

Build portfolio-worthy projects during the course

Customer Churn Prediction

Build an ML system to predict which customers are likely to churn and recommend retention strategies.

XGBoost Feature Engineering Flask API Dashboard

Image Classification System

Develop a CNN-based system for classifying images into multiple categories with high accuracy.

TensorFlow CNN Transfer Learning Streamlit

Sales Forecasting Engine

Create a time series forecasting model to predict future sales and optimize inventory management.

Prophet ARIMA LSTM Dashboard

Ecosystem

ML Ecosystem

Comprehensive Python libraries for machine learning

Scikit-learn

Classical ML algorithms for classification, regression, clustering, and preprocessing.

TensorFlow/Keras

Deep learning frameworks for building and training neural networks.

Pandas/NumPy

Data manipulation and numerical computing libraries for data preparation.

MLflow

Platform for managing the ML lifecycle, including experimentation and deployment.

Comparison

ML Framework Comparison

Choose the right tools for your ML projects

Framework Learning Curve Use Case Production Ready Community Scikit-learn Easy Classical ML Excellent Very Large TensorFlow Moderate Deep Learning Industry Standard Very Large PyTorch Moderate Research & DL Good Growing Rapidly XGBoost Easy Tabular Data Excellent Large LightGBM Easy Large Datasets Excellent Large

Careers

ML Career Pathways

High-demand roles in the AI/ML industry

ML Engineer

₹10-25 LPA

Build and deploy machine learning models in production

Data Scientist

₹8-20 LPA

Analyze data and build predictive models for business insights

MLOps Engineer

₹12-30 LPA

Manage ML lifecycle and deployment pipelines

Research Scientist

₹15-40 LPA

Conduct cutting-edge ML research and develop new algorithms

Requirements

Prerequisites & Certification

What you need and what you'll achieve

Basic Programming

Basic understanding of programming concepts (Python basics covered)

High School Math

Basic mathematics understanding (we cover required statistics)

Problem Solving

Logical thinking and problem-solving mindset

Certification

Receive Applied ML Engineer Professional certificate upon completion

Industry Recognized

Applied Machine Learning with Python

#1

75%

4.8/5

₹12L

Machine Learning Pipeline

Applied ML Curriculum

Module 1: Python for Data Science

Module 2: Statistical Foundations

Module 3: Supervised Learning

Module 4: Advanced ML Algorithms

Module 5: ML Engineering

Module 6: Capstone & Deployment

Real-World Datasets

House Price Prediction

Medical Diagnosis

E-commerce Analytics

Autonomous Driving

ML Application Domains

Finance & Banking

E-commerce & Retail

Healthcare

Manufacturing

Key ML Algorithms

Random Forest

XGBoost

Neural Networks

k-Means Clustering

Real-World ML Projects

Customer Churn Prediction

Image Classification System

Sales Forecasting Engine

ML Ecosystem

Scikit-learn

TensorFlow/Keras

Pandas/NumPy

MLflow

ML Framework Comparison

ML Career Pathways

ML Engineer

Data Scientist

MLOps Engineer

Research Scientist

Prerequisites & Certification

Basic Programming

High School Math

Problem Solving

Certification

Ready to Master Machine Learning?