Beginner Guide to Artificial Intelligence & Machine Learning

About this course

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 3Learn to pre process data, clean data, and analyze large data.

Developer Environment setup for Data Science and Machine LearningA 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 next 6 months

Comprehensive understanding on foundational concepts like Neurons, Perceptron, Multilayer Perceptron, Transformers. Good overview on Convolution Neural Networks and Recurrent Neural Networks

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