Artificial intelligence
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course overview
This comprehensive AI and Technology Training Program is designed to equip learners with the essential skills needed to excel in the rapidly evolving world of artificial intelligence and modern development. The course begins with Generative AI, introducing cutting-edge models that create text, images, and audio, followed by an in-depth exploration of Natural Language Processing (NLP) to help systems understand and interact with human language. Learners then build strong foundations in Machine Learning and Data Science, mastering data handling, model training, and analysis.
Generative AI
- Basics of Gen AI
- Technological aspects of Gen AI
- Major LLMs and their producers – Chat GPT,Gemini,Llama,cloude etc
- Prompt Engineering
- Frameworks of Gen AI
- Langchain
- HuggingFace
- Haystack
- LamIndex
- Amazon Bedrock
- Olama
- Groq
- Lora , Qlora
- RAG
- Vector Database
- Tools
- Agents
Note: A two-day introductory Python class will be conducted, focusing on its applications in Generative AI.
Natural Language Processing
- Basics of neural Networks
- Basics of recurrent neural Networks
- Types of recurrent neural networks
- LSTM 2. GRU
- Data Collection
- Data Preprocessing
- Bag of Word , Tf-idf
- Tect classification through Naivebys algorithm
- Embedding
- Sentiments Analysis using LSTM
- POS tagging using LSTM
- Introduction to Encoder- Decoder model
- Text Generation using LSTM
- Translation using LSTM
- Introduction to Transformer architecture
- Introduction to BERT
- Introduction to Huggingface and Models
Note : We will use Python as the programming language throughout the course.
For programming tasks, we will work with scikit-learn, NumPy, and Pandas.
For deep learning, we will use TensorFlow. All concepts will be taught accordingly.
Machine learning and Data Science
- Basics Of Python
- Introduction of Machine Learning
- Basics of Statistics , Linear Algebra , Probablity
- Problem Definition
- Data Collection
- Data Preprocessing
- EDA
- Data Partioning
- Metrics for Regression and Cllasification
- Machine Learning Algorithms
- Simple Linear Regression
- Model Underfitting , Overfitting
- Regularisation
- Logistics Regression
- Clustering , PCA
- Tree based Alogorithm
- SVM
- KNN
- Ensamble Techniquies
- Random Forest , Xtream Random Forest
- Ada boost
- Xgboost
- Extre4am Xgboost
- Time Series Analysis
- Recommendation systems
- Hyper parameter tuning – Gridsearch CV , Random Grid search
- Model evaulation
- Model Deployment
- Model Monitoring
- Data Drift
- Model Retrainig
- Deep Learning ( Only Basics with Regression and Clasification
- Clouds and Auto ML
- App Development by Flask ,Streamlit
- Development through Vscode , GitHub(CI/CD)
- Containers – Dockers
Note : We will use Python as the primary programming language.
For programming tasks, we will work with scikit-learn, NumPy, and Pandas.
For deep learning, we will use the TensorFlow framework. All topics will be taught accordingly.
End to End Machine Learning Project
- Problem Definition ( Any one Algorithm will be used)
- Data Collection
- Data Preprocessing
- Feature Engineering
- EDA
- Model Selection
- Model Evaluation
- Model hyper parameter tuning
- Usases of various model
- Selection of best model
- Deployment of model
- Model monitoring
- Analysis of Data Drifts
- Model Retraining
Note: We will use the TensorFlow deep learning framework for programming.
A two-day introductory Python class will be conducted, focusing on its use in Computer Vision.
Deep Learning
- Introduction of Deep Learning
- Basics of Statistics , Linear Algebra , Probablity
- Problem Definition
- Data Collection
- Data Preprocessing
- EDA
- Data Partioning
- Metrics for Regression and Cllasification
- Simple Perceptron
- Types of Layers ( Dense , hidden , Input)
- Optimizers
- Regularisation , Batch Normalisation
- Methods of Model Building
- Introduction to Convoultional Network
- Prebuilt models
- Transfer Learning
- GAN
- Clusstering , RBM
- Introduction to Embeddings
- Introduction to LSTMS
- NLP Tasks ( Encoder – Decoder, Transformers)
- Autoencoders
- Hyper parameter tuning
- Model evaulation
- Model Deployment
- Model Monitoring
- Data Drift
- Model Retrainig
- Deep Learning in Clouds
- Deep Learning for Mobiles and IOT
- Auto ML
Note : Our programming will be done in Python.
We will use scikit-learn, NumPy, and Pandas for general programming tasks, and the TensorFlow deep learning framework for building AI models.
All topics will be taught accordingly.
Computer Vision
- Basics of neural Networks
- Basics of Convolutional neural Networks
- Details of Layers in Convolutional Network
- Object Classification , Object Segementation , Object Detection,
- Building of Network for Classification of images
- Information of prebuilt models
- Transfer learning concept
- Introduction to Open CV package
- Working with images , videos in Open CV
- Introduction to YOLO model for object detection , Tracking
- Configuration of YOLO for custom use.
Note : We will use the TensorFlow deep learning framework for programming, with Python as the primary language.
All concepts will be taught accordingly.
Cyber Security ( Including AI methods )
Cyber Security ( Including AI methods ) – Course – TBC
Reinforcement Learning
- Introduction to Reinforcement Learning
- Markov Decision Processes (MDP)
- RL Components and Problem Formulation
- Dynamic Programming for RL
- Monte Carlo Methods
- Temporal Difference (TD) Learning
- Deep Q-Networks (DQN)
- Policy Gradient Methods
- Advanced Topics
- Projects and Applications
- Solving OpenAI Gym environments (CartPole, MountainCar, etc.)
- Mini-project: train an RL agent on a chosen environment
Genetic Algorithm
- Introduction to Genetic Algorithms
- Basic Concepts
- Representation Techniques
- Selection Methods
- Genetic Operators – Crossover
- Genetic Operators – Mutation
- Fitness Function Design
- GA Implementation in Python
- Hybrid GA approaches
- Case Studies and Project
React Native ( Mobile app Development)
React Native ( Mobile app Development) – TBC
Python Programming for AI
Python Programming for AI – TBC
Full-Stack AI Training
Full-Stack AI Training- TBC
Topics we will cover
| Course Name | Duration | Sessions |
| Generative AI | 1 month | 15 |
| Natural Language Processing (NPL) | 2 month | 30 |
| Machine learning and Data Science | 3 month | 45 |
| End to End Machine Learning Project | 1 month | 15 |
| Deep Learning | 3 month | 45 |
| Computer Vision | 1 month | 15 |
| Cyber Security ( Including AI methods ) | 2 month | 30 |
| Reinforcement Learning | 15 days | 10 |
| Genetic Algorithm | 15 days | 10 |
| React Native ( Mobile app Development) | 6 month | 90 |
| Python Programming for AI | 3 month | 45 |
| Full-Stack AI Training | 6 month | 90 |