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Machine Learning Engineering

Learn how to build intelligent systems using Python, Data Science tools and Machine Learning frameworks. (Beginner → Advanced)

Module 1: Foundations of Machine Learning

  • What is Machine Learning?
  • Difference between AI, Machine Learning, and Deep Learning
  • Machine Learning use cases in real industries
  • Types of ML: Supervised, Unsupervised, Reinforcement Learning
  • Setting up ML environment with Python, Jupyter Notebook
  • Introduction to NumPy and Pandas
  • Understanding datasets
Project: Build a **Student Score Prediction Model** that predicts exam performance based on study hours.

Module 2: Data Preparation & Feature Engineering

  • Data cleaning and preprocessing
  • Handling missing data
  • Feature engineering techniques
  • Encoding categorical variables
  • Data scaling and normalization
  • Exploratory Data Analysis (EDA)
  • Data visualization using Matplotlib and Seaborn
Project: Analyze and visualize **Global Technology Salary Data** and prepare it for machine learning.

Module 3: Supervised Learning Algorithms

  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • K-Nearest Neighbors (KNN)
  • Model training and evaluation
  • Accuracy, Precision, Recall, F1 Score
  • Confusion Matrix
Project: Build a **Loan Approval Prediction System** used by financial institutions.

Module 4: Unsupervised Learning

  • Clustering concepts
  • K-Means clustering
  • Hierarchical clustering
  • Customer segmentation
  • Dimensionality reduction
  • Principal Component Analysis (PCA)
Project: Build a **Customer Segmentation Model** for an online store.

Module 5: Deep Learning Fundamentals

  • Neural Networks basics
  • Activation functions
  • Forward and Backpropagation
  • TensorFlow and Keras
  • Building Deep Learning models
  • Overfitting and Regularization
Project: Build a **Handwritten Digit Recognition Model** using deep learning.

Module 6: Natural Language Processing (NLP)

  • Introduction to NLP
  • Text preprocessing
  • Tokenization and embeddings
  • Sentiment analysis
  • Chatbot basics
  • Using pre-trained NLP models
Project: Build a **Social Media Sentiment Analyzer** that detects positive or negative comments.

Module 7: Model Optimization & Deployment

  • Model tuning and hyperparameters
  • Cross validation
  • Improving model performance
  • Saving ML models
  • Building ML APIs with Flask or FastAPI
  • Deploying models to the web
  • Introduction to ML pipelines
Project: Deploy a **Machine Learning Prediction API** that predicts housing prices.

Module 8: Real World Industry Projects

  • Fraud detection systems
  • Recommendation systems
  • Stock price prediction
  • Image classification models
  • AI automation tools
  • ML model deployment
Final Capstone Project Options:
  • AI Powered Job Recommendation System
  • Medical Diagnosis Prediction System
  • AI Resume Screening System
  • Movie Recommendation Engine
  • Smart Chatbot using NLP

By the end of this program, learners will understand how to **build, train, optimize and deploy machine learning systems used in real companies.**