MACHINE LEARNING AND DEEP LEARNING

"Machine intelligence is the last invention that humanity will ever need to make. Machines will then be better at inventing than we are."
Nick Bostrom

A person's learning capabilities and ability to store, organize, and process data are limited. This is where Machine Learning and Deep Learning play their part. You could use it to automate tasks or improve processes, predict outcomes, and create powerful algorithms that help organisations make important decisions.

PROGRAM OVERVIEW

Machine Learning (ML) and Deep Learning (DL) program is designed to equip you with the knowledge needed to leverage the power of ML and DL techniques to improve performance in a wide range of applications. Your goal is to provide an organization with the ability to use advanced techniques to extract insights from data and make predictions or decisions that can improve the performance of the business. This process involves using algorithms and statistical models to enable computers to learn from data without being explicitly programmed.

CAREER OPPORTUNITIES

Upon completion of your degree, you may pursue careers as:

• Machine Learning Engineer
• Data Scientist
• Natural Language Processing (NLP) Scientist
• Business Intelligence (BI) Developer
• Software Developer
• Software Engineer
• Principal Machine Learning Scientist

LEARNING MODELS

Experience learning data science through various methods that will help you gain competitive advantage before you graduate. You will learn through boot camps, a short-term, intensive training program that aims to give you the skills you need to start a new career in data science. You will also be given a chance to get a certificate and experience hands-on experience in data science through corporate training.

WHAT YOU’LL LEARN

In this specialization, you will be equipped with the knowledge and skills to understand Deep learning, which involves training artificial neural networks with multiple layers to learn from data. You will also learn how to prepare data by collecting, cleaning, and formatting data to make it usable for ML and DL models.
Other things you will learn include using the prepared data to train and evaluate different ML and DL models to select the best-performing one, taking the trained model, and deploying it in a production environment where it can be used to make predictions or decisions.

DID YOU KNOW?