IIT Kanpur - Professional Certificate Course in Generative AI and Machine Learning 

IITK AIML - Programming Refresher

Comprehensive grasp of procedural and OOP concepts

Installation of Python and IDE

Mastery in utilizing Jupyter Notebook

Implementation of identifiers, indentations, and comments

Identification of Python data types and operators

Understanding different types of Python loops

Exploration of variable scope within functions

Explanation and understanding of OOP characteristics

IITK AIML - Applied Data Science with Python

Introduction to data science and its practical applications

Grasp the essentials of NumPy

Investigation into array indexing and slicing techniques

Application of linear algebra principles in data analysis

Calculation of central tendency and dispersion measures

Explanation of null and alternative hypotheses

Exploration of various hypothesis testing methods including Z-test and T-test

Understanding the concept of ANOVA (Analysis of Variance)

Utilization of Pandas for data loading, indexing, reindexing, and merging

Data preparation, formatting, normalization, and standardization through data binning

Creation of graphical representations using Matplotlib, Seaborn, Plotly, and Bokeh

IITK AIML - Machine Learning

Investigate the machine learning pipeline

Learn about supervised learning and its applications

Understand methods to identify and prevent overfitting and underfitting

Visualize variable linearity using correlation maps

Explore classification algorithms and their practical usage

Master various unsupervised learning techniques

Recognize suitable scenarios for unsupervised algorithms and types of clustering

Develop a recommendation engine using PyTorch

IITK AIML - Deep Learning with Keras and TensorFlow

Differentiate between deep learning and machine learning

Understand neural networks, including forward and backward propagation

Utilize TensorFlow 2 and Keras for model development

Enhance model performance and interpret results effectively

Explore convolutional neural networks (CNNs) and transfer learning for object detection

Learn about recurrent neural networks (RNNs) and autoencoders

IITK AIML - Essentials of GenAI, Prompt Engineering & ChatGPT

Cutting-edge knowledge: Explore generative AI, prompt engineering, and ChatGPT

Hands-on skills: Gain practical insights into real-world business applications

Effective GenAI utilization: Learn to apply Generative AI effectively in various scenarios

Master prompt engineering: Understand its importance in crafting customized outputs

IITK AIML - Advanced Generative A

Transformers' significance in modern AI

Neural networks' suitability for generative tasks

Differentiate generative model types: VAEs, GANs, transformers, autoencoders

Appropriate scenarios for diverse generative AI models

Assess attention mechanisms' efficacy in generative tasks

Analyze GPT and BERT, contrasting their architectural goals in generative AI

Langchain and Workflow Design

Advanced Prompt Engineering Techniques

LLM Application Development

LLM Fine-Tuning and Customization

Benchmarking and Evaluation of LLM Capabilities