- Definition and history of AI
- Applications of AI in various industries
- Narrow AI vs. General AI
- Machine Learning, Deep Learning, and Data Science
- Ethical considerations in AI
- Bias and fairness in AI models
AI with Python Syllabus
Module 1: Introduction to Artificial Intelligence
1. Overview of AI
Module 2: Python for AI
1. Introduction to Python
- Python basics (variables, data types, control structures)
- Python libraries for AI (NumPy, Pandas, Matplotlib)
- Data loading, cleaning, and preprocessing
- Exploratory Data Analysis (EDA)
Module 3: Machine Learning with Python
1. Introduction to Machine Learning
- Supervised vs. Unsupervised Learning
- Overview of the Machine Learning process
- Linear Regression
- Logistic Regression
- Decision Trees
- Support Vector Machines (SVM)
- k-Nearest Neighbors (k-NN)
- Model evaluation and validation techniques
- Clustering (K-Means, Hierarchical Clustering)
- Dimensionality Reduction (PCA, LDA)
- Feature scaling, normalization, and transformation
- Handling missing data and categorical variables
- Hyperparameter tuning (Grid Search, Random Search)
- Cross-validation techniques
Module 4: Deep Learning with Python
1. Introduction to Neural Networks
- Perceptron’s and Multilayer Perceptron’s (MLP)
- Activation functions and backpropagation
- Introduction to TensorFlow and Keras
- Building and training a neural network with Keras
- Understanding CNN architecture
- Image classification with CNNs
- Introduction to RNNs and LSTMs
- Sequence modelling and text generation
- Pre-trained models and transfer learning
- Fine-tuning models for specific tasks
Module 5: Natural Language Processing (NLP) with Python
1. Introduction to NLP
- Overview of NLP and its applications
- Text preprocessing (tokenization, stemming, lemmatization)
- Bag of Words (BoW)
- TF-IDF
- Word Embeddings (Word2Vec, GloVe)
- Building classifiers for text data
- Sentiment analysis with Python
- Sequence-to-sequence models
- Attention mechanisms and Transformers
- Introduction to GPT and BERT models
Module 6: Reinforcement Learning with Python
1. Introduction to Reinforcement Learning
- Key concepts (agent, environment, rewards)
- Markov Decision Process (MDP)
- Introduction to Q-learning algorithm
- Implementing Q-learning with Python
- Deep Q-Networks (DQN)
- Policy Gradient methods
- Case studies and real-world examples
Module 7: AI Projects and Case Studies
1. AI Project Development
- Project selection and scoping
- Data collection and preprocessing
- Model development and deployment
- Case studies in different domains (e.g., healthcare, finance, robotics)
- Lessons learned and best practices
News & Blog
Latest News & Blogs
24 October 2025
