Finest AI ML Course in Pune & Machine Learning Course in Pune

Module 1: AI ML

AI ML Intro and more

Narrow AI, General AI, & Super AI

Definition, types (Supervised, Unsupervised, Reinforcement), and examples

Install Python, set up a development environment

Module 2: Python Coding Language Basics

Why Python is used in AI and ML

Variables, data types & operators

If else statements & loops

Functions: Defining and calling functions, parameters

Hands-on: Write simple Python scripts (e.g., calculator, basic loops)

Module 3: Python for Data Handling

Lists, tuples, dictionaries, and sets: Creation and manipulation

File handling: Reading and writing text files

Libraries: Introduction to NumPy (arrays) and Pandas (dataframes)

Data cleaning: Handling missing values, duplicates

Hands-on: Load a dataset (e.g., CSV file) and perform basic operations

Module 4: Mathematics for AI and ML

Linear Algebra: Vectors, matrices, dot products

Statistics: Mean, median, standard deviation

Probability: Basics, distributions (normal, binomial)

Calculus: Derivatives, gradients (intro to optimization)

Hands-on: Calculate stats and plot distributions using Python

Module 5: Data Preprocessing Techniques

Importance of clean data in ML

Normalization and standardization of data

Encoding categorical variables (e.g., one-hot encoding)

Splitting data: Training, validation, and test sets

Hands-on: Preprocess a dataset (e.g., Titanic dataset)

Module 6: Introduction to Machine Learning

Definition and workflow of ML: Data, model, prediction

Supervised Learning including Regression & Classification

Unsupervised Learning including Clustering & Dimensionality Reduction

Key terms: Features, labels, overfitting

Hands-on: Explore a sample ML dataset

Module 7: Linear Regression

Concept: Predicting continuous values (e.g., house prices)

Equation: y = mx + b, understanding slope and intercept

Cost function: Mean Squared Error (MSE)

Optimization: Gradient Descent basics

Hands-on: Build a linear regression model in Python

Module 8: Logistic Regression

Concept: Predicting categories (e.g., spam vs. not spam)

Sigmoid function and decision boundaries

Loss function: Log loss (cross-entropy)

Evaluation: Accuracy, precision, recall

Hands-on: Implement logistic regression on a binary dataset

Module 9: Decision Trees

How decision trees work: Splitting data based on rules

Key concepts: Root, branches, leaves

Entropy and Information Gain for splitting

Pros and cons: Overfitting risks

Hands-on: Train a decision tree model (e.g., iris dataset)

Module 10: Random Forests

Ensemble learning: Combining multiple trees

Bagging and feature randomness

Improving accuracy and reducing overfitting

Hyperparameter tuning (e.g., number of trees)

Hands-on: Build a random forest model

Module 11: KNN

Concept: Predicting based on nearest data points

Distance metrics: Euclidean, Manhattan

Choosing the right ‘k’ value

Strengths and weaknesses

Hands-on: Apply KNN to a classification problem

Module 12: Support Vector Machines (SVM)

Goal: Finding the best separating line (hyperplane)

Margins and support vectors

Kernels: Linear, polynomial, RBF

Handling non-linear data

Hands-on: Train an SVM model on a dataset

Module 13: Clustering with K-Means

Unsupervised learning: Grouping similar data

K-Means algorithm: Steps and iteration

Choosing the number of clusters (elbow method)

Applications: Customer segmentation

Hands-on: Cluster a dataset (e.g., customer data)

Module 14: PCA

Variance and eigenvectors explained

Steps to apply PCA

Visualizing reduced data

Hands-on: Reduce dimensions of a dataset using PCA

Module 15: Introduction to Neural Networks

What are neural networks? Mimicking the brain

Structure: Input, hidden, output layers

Activation functions: Sigmoid, ReLU

Forward propagation basics

Hands-on: Build a simple neural network in Python

Module 16: Deep Learning with TensorFlow and Keras

Why deep learning? Power of multiple layers

TensorFlow and Keras: Overview and setup

Building a multi-layer perceptron (MLP)

Training process: Epochs, batch size

Hands-on: Train a deep learning model

Module 17: CNNs

Layers: Convolution, pooling, fully connected

Filters and feature extraction

Applications: Image classification

Module 18: RNNs

Handling sequential data (e.g., time series, text)

RNN structure: Loops and memory

Variants: LSTM and GRU

Challenges: Vanishing gradients

Hands-on: Train an RNN on a time series dataset

Module 19: Natural Language Processing (NLP) Basics

What is NLP? Text as data

Text preprocessing: Tokenization, stemming, lemmatization

Bag of Words and TF-IDF models

Applications: Sentiment analysis

Hands-on: Process and analyze text data

Module 20: Generative AI Introduction

Creating new data

Types: GANs, VAEs, and autoregressive models

Real-world uses: Art, text generation

Challenges in training generative models

Module 21: GANs

GAN structure: Generator vs. Discriminator

Training process: Adversarial competition

Applications: Image generation

Stability issues and solutions

Hands-on: Build a simple GAN

Module 22: Reinforcement Learning Basics

Concept: Learning through rewards

Key terms: Agent, environment, reward

Markov Decision Process (MDP)

Q-Learning introduction

Hands-on: Simulate a basic RL problem

Module 23: Model Evaluation & its Tuning

Accuracy, F1-score and ROC-AUC

True positives & false negatives

Cross-validation techniques

Hyperparameter tuning: Grid search

Hands-on: Evaluate and tune a model

Module 24: AI/ML in Industry - Part 1

Healthcare: Disease prediction, imaging

Finance: Fraud detection, stock prediction

Retail: Recommendation systems

Case studies: Real-world examples

Discussion: Industry trends

Module 25: AI/ML in Industry - Part 2

Manufacturing: Predictive maintenance

Marketing: Customer segmentation

Transportation: Autonomous vehicles

Hands-on: Solve an industry-specific problem

Module 26: Model Deployment

Why deploy models? From code to production

Tools: Flask, FastAPI for APIs

Cloud platforms: AWS, GCP basics

Saving and loading models

Hands-on: Deploy a model as an API

Module 27: Ethics and Responsible AI

Bias in AI: Causes and examples

Privacy concerns: Data handling

Fairness and transparency in models

Regulations: GDPR, AI ethics guidelines

Discussion: Ethical dilemmas

Module 28: Placement Preparation - Part 1

Resume building: Highlighting AI/ML skills

Portfolio creation: Showcasing projects

Common interview questions in AI/ML

Mock interviews: Technical round

Hands-on: Prepare a resume

Module 29: Placement Preparation - Part 2

Coding challenges: Practice problems

Behavioral interviews: Communication skills

Industry networking: LinkedIn, job portals

Mock interviews: HR round

Hands-on: Solve coding exercises

Module 30: Capstone Project

Project selection: Real-world problem (e.g., image classification, chatbot)

Full workflow: Data collection, preprocessing, modeling, evaluation

Presentation: Explain your solution

Feedback and iteration

Final submission: Project report and demo