Control Structures & Functions

• for loops
• range() , Functions (pre-defined, user defined) expressions

Week 2: Python Libraries and Fundamentals in Machine Learning – Part 1

Python Libraries

• NumPy arrays and vectorized operations
• Pandas and with exploring a dataset
• Matplotlib basic plotting
• Real-world EDA practice

ML Pipeline Overview

• ML lifecycle and workflow
• Supervised vs. Unsupervised learning

Regression Algorithms

• What is meant by Model
• Linear Regression (Simple & Multiple)
• Regression evaluation metrics (MSE, RMSE, R^2)
• Hands-on: Predicting house prices

Classification Algorithms

• Logistic Regression fundamentals
• Classification use cases (e.g., cancer detection)
• Hands-on with sklearn models

Model Evaluation Techniques

• Confusion Matrix
• Accuracy score

Week 3: Advanced Machine Learning – Part 2

Feature Engineering

• One-hot and Label Encoding
• Feature Scaling: Standard vs MinMax

Data partition:

• Training and Test data

Tree-Based Models

• Decision Tree algorithm
• Splitting Criteria: Gini vs Entropy
• Overfitting and pruning techniques
• Bagging and Random Forest ensemble learning

Unsupervised Learning

• K-Means Clustering: Elbow Method
• Distance metrics: Euclidean
• Applications of clustering in real-world problems
• Hands-on clustering mini project
• Feature selection techniques

# ASSIGNING A PROJECT TO WORK ON MACHINE LEARNING APPLICATIONS

Week 4: Fundamentals in Natural Language Processing – Part 1

Introduction to NLP

• NLP pipeline overview
• Applications: chatbots, sentiment analysis, etc.
• Challenges of NLP
• Text encoding techniques overview
• Introduction to nltk

Text Cleaning & Tokenization

• Removing punctuation, numbers, stop words
• Lemmatization vs Stemming
• Tokenization using nltk
• Case study: cleaning feedbacks data

Text Vectorization

• Bag of Words (BoW)
• TF-IDF implementation
• Sparse matrix representation
• Visualization with word clouds

Text Classification

• Sentiment Analysis
• End-to-end NLP pipeline using Scikit-learn
• Dataset splitting and vectorization
• Training Logistic Regression

# ASSIGNING A PROJECT TO WORK ON NLP BASED MACHINE LEARNING APPLICATION

Week 5: Fundamentals in Deep Learning – Part 1

Neural Networks Fundamentals

• Perceptron model and activation functions
• Feedforward NN architecture
• Forward and backward propagation
• Loss functions: MSE, Cross-Entropy
• Model intuition with simple examples

Training Deep Models

• Epochs, Batches, and Batch Size
• Optimizers: SGD, Adam
• Hands-on: Build a basic NN using Keras

Keras/TensorFlow Practice

• TensorFlow/Keras basics
• Model.compile(), .fit(), .evaluate()
• Evaluate performance on test data
• Case Study: Improve model performance

# ASSIGNING A PROJECT TO WORK ON ANN

Week 6: Advanced in Natural Language Processing – Part 2

Word Embeddings

• Word2Vec: Skip-gram & CBOW
• Visualizing word vectors
• Hands-on: Pre-trained embeddings usage

Sequence Models and RNN Basics

• Sequential data and temporal relationships
• Introduction to RNNs and limitations
• Tokenizing and padding sequences
• Preparing text data for DL
• Keras Embedding layer overview

NLP Capstone Project

• Select a domain-specific problem (e.g., reviews, healthcare)
• Dataset analysis and cleaning
• Model selection and evaluation
• Presenting the outcome
• Review and feedback

# NLP Case Study: Embeddings → RNN Sequence Models

Week 7: Advanced in Deep Learning – Part 2

CNNs for Image Classification

• Convolution and pooling operations
• CNN architecture and feature extraction
• Activation maps
• Building CNNs with Keras
• MNIST image classification project

Transfer Learning

• Concept of Transfer Learning
• Pre-trained models: VGG16, ResNet50
• Feature extraction vs Fine-tuning
• Load, modify and evaluate pre-trained models
• Image classification case study

# ASSIGNING A PROJECT TO WORK ON CONVOLUTIONAL NEURAL NETWORKS(CNN)

Week 8: Advanced AI – Part 2

Transformers & Hugging Face

• Introduction to transformers
• Pretrained models: BERT, GPT
• Hugging Face installation & usage
• Text classification pipeline with Transformers
• Fine-tuning basics

DL with NLP (Transformers)

• Transformer architecture recap
• BERT for sequence classification
• Tokenizer and model pipeline
• Evaluate model performance
• NLP fine-tuning best practices

Deep Learning Capstone Project

• Choose between NLP or Image-based project
• Define objective and strategy
• Model training and validation

# ASSIGNING A PROJECT TO WORK ON (NLP + DL)

Career Designations You Can Aim For:

After completing this program, you’ll be equipped to pursue exciting roles such as:

• Machine Learning Engineer
• Data Scientist
• AI Engineer
• NLP Engineer
• Computer Vision Engineer
• Data Analyst with AI Expertise
• AI Product Consultant / AI Research Associate

These roles span across sectors like IT, healthcare, finance, e-commerce, manufacturing, and even education.