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Artificial Intelligence and Machine Learning

Artificial intelligence (AI) can be defined as the development of computer systems which can perform tasks, such as recognizing patterns and pictures, understanding language, learning from experience, at par with human intelligence. Now, the question arises how do we define “intelligence”? Intelligence is the ability to learn, understand, and make judgments based on reason. It can also be defined as the ability to acquire and apply knowledge to real-world scenarios.

This concept of “intelligence” forms the basis for the domain of AI. The idea of an “intelligent” machine was first introduced by Alan Turing in the year 1950 when he proposed a test known as “imitation game”, which is better known as Turing Test. It was aimed to check whether the machine is “intelligent” or not.


Course Content

1. Introduction to AI & Machine Learning
  • What is Artificial Intelligence?
  • Machine Learning vs Deep Learning
  • Applications of AI & ML in Various Industries
  • Ethical Considerations in AI
2. Python for AI & ML
  • Python Basics for AI
  • Data Manipulation with NumPy & Pandas
  • Data Visualization with Matplotlib & Seaborn
  • Working with Jupyter Notebooks
3. Supervised Learning
  • Understanding Supervised Learning
  • Regression Algorithms (Linear, Logistic)
  • Classification Algorithms (SVM, Decision Trees, KNN)
  • Model Evaluation & Performance Metrics
4. Unsupervised Learning
  • Introduction to Unsupervised Learning
  • Clustering Algorithms (K-Means, DBSCAN, Hierarchical Clustering)
  • Dimensionality Reduction Techniques (PCA, t-SNE)
  • Applications of Unsupervised Learning
5. Neural Networks & Deep Learning
  • Introduction to Neural Networks
  • Activation Functions & Backpropagation
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs) & LSTMs
6. Natural Language Processing (NLP)
  • Introduction to NLP
  • Tokenization, Stemming, and Lemmatization
  • Sentiment Analysis & Text Classification
  • Chatbots & Transformers (BERT, GPT)
7. Reinforcement Learning
  • Basics of Reinforcement Learning
  • Markov Decision Process (MDP)
  • Q-Learning & Deep Q Networks
  • Applications in Robotics & Gaming
8. AI & ML Deployment
  • Model Deployment using Flask & FastAPI
  • Cloud Deployment (AWS, GCP, Azure)
  • Scaling AI Models for Production
  • Model Monitoring & Optimization
9. Capstone Project
  • Building an End-to-End AI/ML Project
  • Data Preprocessing & Model Training
  • Model Deployment & Performance Analysis
  • Presentation & Documentation
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