/PGP-AI-ML

This Is a repository of the modules that i have covered in my Post Graduate Program in Artificial Intelligence and Machine Learning

PGP-AI-ML

Foundations

The Foundations block comprises of two courses where we get our hands dirty with Statistics and Code, head-on. These two courses set our foundations so that we sail through the rest of the journey with minimal hindrance.

Python for AI & ML

Duration 2 Quizzes Duration 1 Project Applied Statistics Duration 3 Quizzes Duration 1 Project Machine Learning The next module is the Machine Learning online course that will teach us all the Machine Learning techniques from scratch, and the popularly used Classical ML algorithms that fall in each of the categories.

Supervised Learning

Duration 4 Quizzes Duration 1 Project

Unsupervised Learning

Duration 2 Quizzes Duration 1 Project

Ensemble Techniques

Duration 2 Quizzes Duration 1 Project

Featurization, Model Selection & Tuning

Duration 2 Quizzes Duration 1 Project Recommendation Systems Duration 3 Quizzes Duration 1 Project

Artificial Intelligence

The next module is the Artificial Intelligence online course that will teach us from the introduction to Artificial Intelligence to taking us beyond the traditional ML into Neural Nets’ realm. We move on to training our models with Unstructured Data like Text and Images from the regular tabular data.

#Introduction to Neural Networks and Deep Learning Duration 3 Quizzes Duration 1 Project

Computer Vision

Duration 5 Quizzes Duration 2 Projects

Natural Language Processing

Duration 4 Quizzes Duration 2 Projects

Additional Modules

This block will teach some additional modules involved in this Python for AIML online course.

EDA

Time Series Forecasting Pre Work for Deep Learning Model Deployment Visualization using Tensor board GANs (Generative Adversarial Networks) Reinforcement Learning Hands-on Projects : Classifying silhouettes of vehicles Classify a given silhouette as one of three types of vehicles, using a set of features extracted from the silhouette. You can view the vehicle from one of many different angles. The data contains features extracted from the silhouette of vehicles from different angles. Four “Corgie” model vehicles were used for the experiment: a double-decker bus, Chevrolet van, Saab 9000, and an Opel Manta 400 cars. This particular combination of vehicles was chosen with the expectation that the bus, van, and either one of the cars would be readily distinguishable. Still, it would be more challenging to distinguish between cars.

Capstone Project

You will get your hands dirty with a real-time project under industry experts’ guidance from introducing you to Python to the introduction to artificial intelligence and machine learning and everything in between Python for AIML. Successful completion of the project will earn you a post-graduate certification in artificial intelligence and machine learning.