Introduction to AI & Machine Learning JumpStart (TTML5503)

Learning Objectives

Working in a hands-on learnng environment led by our expert practitioner you’ll explore:

• Grasp AI & Machine Learning Basics: You'll start your journey by understanding what AI and Machine Learning are, distinguishing between them, and discovering how they're applied in various fields. You'll also get a good look at practical examples of Machine Learning.
• Decode Types of Machine Learning: You'll navigate through the different types of machine learning, including supervised, unsupervised, and reinforcement learning, and gain insight into their distinctive applications, and explore their practical application.
• Master Data Prep: You’ll learn and apply critical methods for cleaning and simplifying data
• Master Algorithms: You’ll explore popular machine learning algorithms, their applicability and limitations
• Get Hands-On: You'll learn how to code linear regression and logistic regression algorithms in Python, and gain hands-on experience applying them in real-world scenarios in a machine learning environment working with various machine learning packages and tools.
• Optimize Machine Learning Models: You'll dig into the art of model optimization, learning how to prevent underfitting and overfitting to ensure your machine learning models are accurate and reliable.
• Conquer Classification: You'll uncover the secrets of the perceptron algorithm and logistic classifiers, learning  how to classify data effectively and carry out sentiment analysis like a pro.
• Responsible AI Development: You’ll gain insight into the ethical considerations and responsible practices in AI, ensuring that solutions are developed with a consciousness of privacy, bias, and societal implications.
• Venture into Generative AI: You'll step into the fascinating world of Generative AI and Generative Adversarial Networks (GANs), exploring their structure, functionality, and the concept of latent space in generative models. After the course, you'll understand how these advanced AI models can contribute to your projects.

Audience & Pre-Requisites

This introductory-level hands-on course is suited for a wide variety of technical learners who need an introduction to the core skills, concepts, tech, tools and skills related to AI programming and machine learning. 

Suitable attendees might include:

• Developers aspiring to be a 'Data Scientist' or Machine Learning engineers
• Analytics Managers who are leading a team of analysts 
• Business Analysts who want to understand data science techniques
• Information Architects who want to gain expertise in Machine Learning algorithms 
• Analytics professionals who want to work in machine learning or artificial intelligence
• Graduates looking to build a career in Data Science and machine learning

Pre-Requisites

Pre-Requisites:  Students should have attended or have incoming skills equivalent to those in this course:

• Basic Understanding of Python as well as familiarity with Python Libraries (Pandas and Numpy, etc.). Attendees without Python background may view labs as follow along exercises or team with others to complete them.
• Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
• Basic Math and Problem-Solving Skills
• Understanding of Basic Data Structures

Take Before: Attending students should have incoming skills equivalent to those in the course(s) below, or should have attended these as a pre-requisite:

• TTPS4800      Introduction to Python (3 days)
• TTPS4873      Introduction to Python in Data Science and Machine Learning (3 days)

Related helpful courses:

• TTPS4880 Hands-on Practical Python for Data Prep, Data Wrangling & Transformation (3 days)