Artificial Intelligence Course

Learn Artificial Intelligence through practical, project-based training at
Acrosys Technologies. This six-month course covers Python for AI, machine
learning, deep learning, natural language processing, generative AI,
model deployment and real-world AI applications.

Artificial Intelligence Course Syllabus

Month 1: Foundations of AI and Python for AI

• Introduction to Artificial Intelligence, its history and applications


• Types of AI: Narrow AI, General AI and Super AI


• Python for AI: Data types, functions and object-oriented programming


• Python libraries: NumPy, Pandas, Matplotlib and Seaborn


• Mathematics refresher: Linear algebra, probability and statistics


• Ethics in Artificial Intelligence

Mini Project:
Python data analysis project using a real-world dataset such as the Titanic dataset.

Month 2: Machine Learning Basics

• Supervised learning and unsupervised learning


• Regression techniques: Linear regression and multiple regression


• Classification techniques: Logistic regression, KNN and SVM


• Model evaluation using accuracy, precision, recall and F1-score


• Clustering techniques: K-means and hierarchical clustering


• Feature scaling, feature selection and feature engineering

Mini Project:
Predict housing prices using regression techniques.

Month 3: Advanced Machine Learning

• Decision Trees and Random Forests


• Gradient Boosting using XGBoost and LightGBM


• Cross-validation techniques


• Hyperparameter tuning using Grid Search and Random Search


• Ensemble learning methods


• Dimensionality reduction using PCA and t-SNE


• Introduction to machine learning pipelines

Mini Project:
Customer segmentation using clustering techniques.

Month 4: Deep Learning with Neural Networks

• Neural network architecture: Perceptron and Multi-Layer Perceptron


• Activation functions and loss functions


• Forward propagation, backpropagation and optimizers


• Introduction to TensorFlow and Keras


• Convolutional Neural Networks for image processing


• Recurrent Neural Networks and LSTM for sequence data

Mini Project:
Build an image classifier using CNNs and the MNIST handwritten digit dataset.

Month 5: Generative AI and Natural Language Processing

• Natural Language Processing using spaCy and NLTK


• Text preprocessing techniques


• Word embeddings using Word2Vec and GloVe


• Transformers and attention mechanisms


• Introduction to Hugging Face and OpenAI APIs


• Generative AI and text generation


• Prompt engineering techniques


• Chatbot development using Large Language Models

Mini Project:
Build an AI chatbot using OpenAI GPT.

Month 6: Real-World AI Applications and Capstone Project

• Artificial Intelligence applications in healthcare, finance and retail


• Model deployment using Flask, Streamlit and FastAPI


• Model monitoring and versioning


• Ethics, fairness and bias in Artificial Intelligence


• Resume and portfolio building

Capstone Project Options

• AI-powered product recommendation system


• Fake news classifier


• Stock price prediction system


• Voice command recognition system

Assessments and Deliverables

• Weekly quizzes and assignments


• Monthly mini projects


• One final capstone project


• Certificate of Completion