Artificial Intelligence & Machine Learning Introductory Program

Goals

• An Insight and comprehensive overview of the concepts of AI and ML
• Implement Machine Learning algorithms.
• How to improve your Machine Learning Models.
• Build a portfolio of work to have on your resume.
• Supervised and Unsupervised Learning.
• Explore large datasets using data visualization tools like Matplotlib.
• Learn NumPy and how it is used in Machine Learning.
• Learn to use the popular library Scikit-learn in your projects.
• Learn to perform Classification and Regression modelling.
• Master Machine Learning and use it on the job.
• Learn which Machine Learning model to choose for each type of problem.
• Learn best practices when it comes to Data Science Workflow
• Learn how to program in Python using the latest Python 3.
• Learn to preprocess data, clean data, and analyze large data.
• Developer Environment setup for Data Science and Machine Learning.
• A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry, with all code and notebooks provided.
• Real-life case studies and projects to understand how things are done in the real world.
• Guidance to choose your career path based on your background and build the path in the next 6 months.
• Comprehensive understanding of foundational concepts like Neurons, Perceptron, Multilayer Perceptron, and Transformers. 
• Good overview on Convolution Neural Networks and Recurrent Neural Networks.

Prerequisites

• Basic Python Programming, Basic Mathematical Concepts like Vector Algebra, Vector Calculus, Probability and Statistics. 

• Non Programmers are also can learn these skills with some initial ground preparation.