Artificial intelligence and Machine Learning
AI and Data Science are intertwined fields, with AI encompassing a broader scope of intelligent system development and Data Science focusing specifically on extracting knowledge and insights from data. The collaboration between these fields is crucial for building advanced and effective AI systems.
AIML
Start Date: June 2025
What you'll learn
- Master Python, data preprocessing, and key libraries like NumPy, pandas, and scikit-learn.
- Learn supervised and unsupervised ML techniques, and develop models with TensorFlow or PyTorch.
- Use tools like Matplotlib and Seaborn to interpret and present data insights.
Approach
- Lead by industry experts
- Hands-on
- Real time project implementation
- Industry certificate
- Python
- Data science
- Machine learning
- Google Colab
- GPU
Course content
Conceptual Sessions followed by Project implementation
- Fundamentals of Python language – basic syntax, Data structures, operators, conditional statements, loops, function, class.
- Woking with libraries, python libraries, installation and usage for writing the AI ML Mode ex – Tensorflow , Keras, Matplot, Pandas etc.
- AI ML – Data Science, Machine learning and NLP based concepts exploring
- AI ML models – Classification and regression models
- Model architecture, parameters, implementation flow and working.
- Feed Forward Network, Convolutional Neural Network, Recurrent Neural Network …. Etc.
- Method of implementation for own AI and ML model
- Annotation methods and Tools, Data cleaning and Augmentation methods with python coding.
- Training and Testing the ML model in the Google colab, TensorFlow and GPU system for various ML models.
- Hands on with basic python programming
- Projects on function, class and data processing in python
- Python Libraries based projects – Numpy, Pandas, Opencv, Matplot, Tensorflow, and Keras.
- Regression based projects like – Linear regression, Binary tree implementation on real time use cases example price prediction for crops, homes etc.
- ML project implementation in TensorFlow, Anaconda, Spyder and Google Colab for MNIST handwritten datasets, fashion MNIST datasets and Kaggle datasets and own dataset.
- Hands-on
- Industry based use cases
- Hybrid mode (Online & Offline)
- 1 Month concepts oriented sessions followed by next 1 month project mentoring (hybrid)
- Complete Python programming Knowledge
- Industry Oriented expose for implementing the ML Models
- Hands on with real time use cases
Knowledge of all Machine learning Models and its implementation methods as per industry perspective.
- Basic level of programming understanding L0
- Knowledge of system handling for the python installation, VS code or any other python interpreter. (Anaconda, Spyder, Jyupter Notebook)
Highlights:
- Foundations: Master Python, data preprocessing, and key libraries like NumPy, pandas, and scikit-learn.
- Model Building: Learn supervised and unsupervised ML techniques, and develop models with TensorFlow or PyTorch.
- Data Visualization: Use tools like Matplotlib and Seaborn to interpret and present data insights.
