Course Description

Artificial Intelligence (AI) is one of the most promising career paths in the world right now ! Whether you’re a beginner or an experienced professional, there’s an AI job waiting for you.

An AI course teaches the theory and application of artificial intelligence technologies, including machine learning, neural networks, computer vision, and natural language processing.An AI course offers many advantages, whether you’re aiming for a career shift, skill upgrade, or academic growth. High-Demand Career Opportunities,Career Flexibility,High Salary Potential, Strong Skillset Development,Research & Academic Growth,Real-World Impact,Creativity & Innovation are the Advantages of Taking an AI Course.

Course Outline :

Module 1: Introduction to Python for Machine Learning

Duration: 1-2 Weeks
Theory
● Introduction to Python for ML
● Installing Python and Jupyter Notebook
● Data types, Variables, and Operators
● Conditional Statements and Loops
● Functions and Modules
● NumPy for numerical computing
● Pandas for data manipulation
● Matplotlib & Seaborn for data visualization
Code
● Writing Python scripts
● Handling datasets with Pandas
● Visualizing datasets

Fun Project
“Data Explorer”: Build a simple Python script that loads a dataset, performs basic data
analysis, and generates visualizations.

Module 2: Foundations of Machine Learning

Duration: 1 Week
Theory
● What is Machine Learning?
● Supervised vs Unsupervised Learning
● Overview of Regression & Classification
● The ML Pipeline: Data Collection, Preprocessing, Model Training, Evaluation
Code
● Implementing Linear Regression from scratch
● Using Scikit-learn for simple ML tasks
Fun Project

“Predict House Prices”: Train a simple Linear Regression model to predict house prices from
features like square footage and number of rooms.

Module 3: Data Preprocessing & Feature Engineering

Duration: 1 Week
Theory
● Handling missing values
● Feature scaling and normalization
● One-hot encoding & label encoding
● Feature selection techniques
Code
● Implementing feature engineering using Scikit-learn
● Handling categorical and numerical data

Fun Project

“Titanic Survivor Prediction”: Process the Titanic dataset, engineer features, and train a
classifier to predict survival.
Module 4: Supervised Learning – Regression Models

Duration: 1-2 Weeks
Theory
● Simple & Multiple Linear Regression
● Polynomial Regression
● Ridge & Lasso Regression
● Evaluation Metrics (MSE, RMSE, R² Score)
Code
● Implement regression models in Scikit-learn
● Implement Ridge and Lasso from scratch
Fun Project

“Stock Price Prediction”: Use regression to predict stock prices based on historical data.

Module 5: Supervised Learning – Classification Models

Duration: 1-2 Weeks
Theory
● Logistic Regression
● k-Nearest Neighbors (k-NN)
● Support Vector Machines (SVM)
● Decision Trees & Random Forest
● Model evaluation (Confusion Matrix, Precision, Recall, F1-Score, AUC-ROC)
Code
● Train and compare different classifiers
● Implement SVM and Decision Trees from scratch
Fun Project

“Spam Email Detector”: Build a classifier to detect spam emails.

Module 6: Unsupervised Learning – Clustering &
Dimensionality Reduction

Duration: 1-2 Weeks
Theory
● k-Means Clustering
● Hierarchical Clustering
● Principal Component Analysis (PCA)
● t-SNE for visualization
Code
● Implement clustering algorithms in Python
● Reduce dataset dimensions with PCA
Fun Project

“Customer Segmentation”: Cluster customers based on shopping patterns.

Module 7: Neural Networks & Deep Learning

Duration: 2 Weeks
Theory
● Introduction to Deep Learning
● Basics of Artificial Neural Networks (ANN)
● Activation Functions
● Backpropagation & Gradient Descent
● Introduction to TensorFlow & PyTorch
Code
● Implement an ANN using TensorFlow/Keras
● Train a deep learning model
Fun Project

“Handwritten Digit Recognition”: Train an ANN to recognize digits from the MNIST dataset.

Module 8: Convolutional Neural Networks (CNNs)

Duration: 2 Weeks
Theory
● What are CNNs?
● Convolutional layers, Pooling layers
● Transfer Learning
Code
● Train a CNN from scratch using TensorFlow/Keras
● Use pre-trained models like VGG16 & ResNet
Fun Project

“Build an Image Classifier”: Train a CNN to classify images of cats and dogs.

Module 9: Recurrent Neural Networks (RNNs) & NLP

Duration: 2 Weeks
Theory
● Introduction to NLP
● Word Embeddings (Word2Vec, GloVe)
● LSTMs & GRUs
Code
● Build an RNN for text generation
● Implement LSTM for sentiment analysis
Fun Project

“AI Chatbot”: Build a chatbot using LSTMs.

Module 10: Generative Adversarial Networks (GANs)

Duration: 2 Weeks
Theory
● What are GANs?
● Generator vs Discriminator
● Training GANs
Code
● Implement a simple GAN from scratch
● Train a GAN to generate images
Fun Project

“Create AI Art”: Train a GAN to generate realistic images.

Final Capstone Project: Build a Complete ML
Application

Duration: 2 Weeks
● Choose a problem (e.g., face recognition, fraud detection, AI music generator)
● Collect and preprocess data
● Train and optimize a model
● Deploy the model as a web application using Flask/Django

Bonus Topics

(Optional for Enthusiasts!)
● Reinforcement Learning
● AutoML
● Explainable AI
● AI Ethics

Course Outcome
By the end of this course, students will:
✅Master Machine Learning concepts from scratch

✅Build & deploy real-world ML applications

✅Understand Deep Learning & Generative AI

✅Be ready for jobs in AI & Data Science