Course Overview

Course Description:Develop skills in model tuning, feature engineering, and using tools like scikit-learn and TensorFlow for predictive analytics.

Topics Covered:

• Fundamentals of machine learning
• Supervised and unsupervised learning techniques
• Introduction to data preprocessing and feature engineering
• Basic algorithms: Linear Regression, Decision Trees, K-Means
• Model evaluation and performance metrics

Syllabus

Module 1: Introduction to Machine Learning

• What is ML? Types (Supervised, Unsupervised, Reinforcement)
• ML vs AI vs Deep Learning
• Real-world applications
• ML workflow & pipeline

LAB 1

• Install Python & Jupyter/Colab
• Run a basic ML pipeline on Iris dataset in Scikit-learn

Module 2: Python for Machine Learning

• Python essentials (functions, OOP, file handling)
• NumPy, Pandas for data manipulation
• Matplotlib, Seaborn for visualization

LAB 2

• Load CSV dataset in Pandas
• Perform summary statistics
• Plot graphs using Matplotlib/Seaborn

Module 3: Data Preprocessing & Feature Engineering

• Data cleaning (missing values, outliers)
• Categorical encoding (One-hot, Label Encoding)
• Scaling (MinMax, StandardScaler)
• Feature selection & extraction

LAB 3

• Handle missing values in Titanic dataset
• Apply feature scaling on dataset in Scikit-learn

Module 4: Probability & Statistics for ML

• Probability basics, Bayes Theorem
• Distributions (Normal, Bernoulli, Binomial, Poisson)
• Hypothesis testing (t-test, chi-square)

LAB 4

• Simulate coin toss & dice using Python
• Test significance using SciPy

Learning Outcome

Understand core machine learning concepts, build basic models, and apply simple algorithms to analyze and predict data patterns.

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Course Overview

Course Description:Develop skills in model tuning, feature engineering, and using tools like scikit-learn and TensorFlow for predictive analytics.

Topics Covered:

• Advanced data preprocessing and feature selection
• Ensemble methods: Random Forest, Gradient Boosting
• Introduction to neural networks and deep learning
• Hyperparameter tuning and model optimization
• Working with large datasets and big data tools

Syllabus

Module 1: Introduction to Machine Learning

• What is ML? Types (Supervised, Unsupervised, Reinforcement)
• ML vs AI vs Deep Learning
• Real-world applications
• ML workflow & pipeline

LAB 1

• Install Python & Jupyter/Colab
• Run a basic ML pipeline on Iris dataset in Scikit-learn

Module 2: Python for Machine Learning

• Python essentials (functions, OOP, file handling)
• NumPy, Pandas for data manipulation
• Matplotlib, Seaborn for visualization

LAB 2

• Load CSV dataset in Pandas
• Perform summary statistics
• Plot graphs using Matplotlib/Seaborn

Module 3: Data Preprocessing & Feature Engineering

• Data cleaning (missing values, outliers)
• Categorical encoding (One-hot, Label Encoding)
• Scaling (MinMax, StandardScaler)
• Feature selection & extraction

LAB 3

• Handle missing values in Titanic dataset
• Apply feature scaling on dataset in Scikit-learn

Module 4: Probability & Statistics for ML

• Probability basics, Bayes Theorem
• Distributions (Normal, Bernoulli, Binomial, Poisson)
• Hypothesis testing (t-test, chi-square)

LAB 4

• Simulate coin toss & dice using Python
• Test significance using SciPy

Module 5: Supervised Learning – Regression Models

• Linear Regression, Multiple Regression
• Polynomial Regression
• Regularization (Lasso, Ridge)

LAB 5

• Predict house prices using Linear Regression (Kaggle dataset)
• Compare Ridge vs Lasso regression

Module 6: Supervised Learning – Classification Models

• Logistic Regression
• k-Nearest Neighbors (k-NN)
• Decision Trees
• Support Vector Machines (SVM)

LAB 6

• Predict Titanic survival (Logistic Regression)
• Build k-NN classifier on Iris dataset

Module 7: Ensemble Methods & Model Optimization

• Random Forest, Gradient Boosting, XGBoost
• Bagging vs Boosting vs Stacking
• Hyperparameter tuning (GridSearchCV, RandomSearchCV)

LAB 7

• Use Random Forest on loan prediction dataset
• Perform hyperparameter tuning with GridSearch

Module 8: Unsupervised Learning

• k-Means Clustering
• Hierarchical Clustering
• PCA & Dimensionality Reduction

LAB 8

• Perform customer segmentation using K-Means (Retail dataset)
• Apply PCA on Wine dataset

Learning Outcome

Develop more sophisticated machine learning models, optimize algorithms for better performance, and gain experience with neural networks and data management techniques.

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Course Overview

Course Description:Master deep learning, NLP, and scalable ML systems. Build real-world AI solutions for domains like healthcare, finance, and IoT.

Topics Covered:

• Deep learning architectures: CNNs, RNNs, GANs
• Natural language processing and computer vision
• Advanced model tuning and regularization techniques
• Deployment of machine learning models in production
• Ethics and bias in machine learning

Syllabus

Module 1: Introduction to Machine Learning

• What is ML? Types (Supervised, Unsupervised, Reinforcement)
• ML vs AI vs Deep Learning
• Real-world applications
• ML workflow & pipeline

LAB 1

• Install Python & Jupyter/Colab
• Run a basic ML pipeline on Iris dataset in Scikit-learn

Module 2: Python for Machine Learning

• Python essentials (functions, OOP, file handling)
• NumPy, Pandas for data manipulation
• Matplotlib, Seaborn for visualization

LAB 2

• Load CSV dataset in Pandas
• Perform summary statistics
• Plot graphs using Matplotlib/Seaborn

Module 3: Data Preprocessing & Feature Engineering

• Data cleaning (missing values, outliers)
• Categorical encoding (One-hot, Label Encoding)
• Scaling (MinMax, StandardScaler)
• Feature selection & extraction

LAB 3

• Handle missing values in Titanic dataset
• Apply feature scaling on dataset in Scikit-learn

Module 4: Probability & Statistics for ML

• Probability basics, Bayes Theorem
• Distributions (Normal, Bernoulli, Binomial, Poisson)
• Hypothesis testing (t-test, chi-square)

LAB 4

• Simulate coin toss & dice using Python
• Test significance using SciPy

Module 5: Supervised Learning – Regression Models

• Linear Regression, Multiple Regression
• Polynomial Regression
• Regularization (Lasso, Ridge)

LAB 5

• Predict house prices using Linear Regression (Kaggle dataset)
• Compare Ridge vs Lasso regression

Module 6: Supervised Learning – Classification Models

• Logistic Regression
• k-Nearest Neighbors (k-NN)
• Decision Trees
• Support Vector Machines (SVM)

LAB 6

• Predict Titanic survival (Logistic Regression)
• Build k-NN classifier on Iris dataset

Module 7: Ensemble Methods & Model Optimization

• Random Forest, Gradient Boosting, XGBoost
• Bagging vs Boosting vs Stacking
• Hyperparameter tuning (GridSearchCV, RandomSearchCV)

LAB 7

• Use Random Forest on loan prediction dataset
• Perform hyperparameter tuning with GridSearch

Module 8: Unsupervised Learning

• k-Means Clustering
• Hierarchical Clustering
• PCA & Dimensionality Reduction

LAB 8

• Perform customer segmentation using K-Means (Retail dataset)
• Apply PCA on Wine dataset

Module 9: Neural Networks & Deep Learning

• Basics of Neural Networks
• Forward & Backward Propagation
• Activation functions
• Introduction to TensorFlow & PyTorch

LAB 9

• Build a simple ANN in TensorFlow/Keras
• Classify MNIST digits

Module 10: Advanced Deep Learning (CNNs & RNNs)

• Convolutional Neural Networks (CNNs) for image recognition
• Recurrent Neural Networks (RNNs), LSTMs for sequences
• Transfer Learning basics

LAB 10

• Build CNN for CIFAR-10 image classification
• Train RNN for text prediction

Module 11: Natural Language Processing (NLP)

• Text preprocessing (tokenization, stemming, lemmatization)
• Word embeddings (Word2Vec, GloVe, BERT basics)
• Sentiment analysis

LAB 11

• Build sentiment analysis model using NLTK & Scikit-learn
• Use pre-trained BERT for text classification in HuggingFace

Module 12: Model Deployment & Capstone Project

• ML model deployment techniques
• Flask & Streamlit for deployment
• ML on Cloud (Google Colab, Streamlit Cloud, HuggingFace Spaces)

LAB 12

• Deploy a classification model on Streamlit Cloud
• End-to-End project: Choose dataset → Clean → Train → Deploy

Learning Outcome

Master advanced machine learning techniques, build complex models for NLP and computer vision, and deploy them in real-world applications while considering ethical issues.

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