Introduction To Ai - Machine Learning And Deep Learning

Jumpstart your career in Machine Learning and Deep Learning with this foundational course

What you'll learn

Understand the basic concepts and terminology in Machine Learning and Deep Learning

Gain intuition about how various Machine Learning and Deep Learning algorithms work

Learn how to use the concepts learned to solve a business problem

Be able to apply this knowledge to pursue a vendor certification

Requirements

Students must have a basic knowledge of undergraduate level mathematics in areas like Linear Algebra, Probability, Statistics and Calculus.

Although the course will not involve programming, a basic knowledge of Computer Science and programming would help. The algorithms discussed in the course will be shown using pseudo code. We have an optional module on Python that will be a refresher for those who have basic familiarity with the language.

Description

Today we see AI all around us.From apps on our phone, to voice assistants in our room, we have gadgets powered by AI and Machine Learning.If you’re curious to know how machine learning works, or want to get started with this technology, then this course is for you.This is a beginner level course in AI - Machine Learning and Deep Learning.As students, you will gain immensely by knowing about this transformative technology, its potential and how to make the best use of it. It will open up opportunities in your existing jobs as well as prepare you for new careers.It will go over the basic concepts, introduce the terminology and discuss popular Machine Learning and Deep Learning algorithms using examples.It will be ideal for•Students aspiring to begin a career in AI•IT Professionals and Managers who want to understand the basic concepts•Just about anyone who is curious to learn about AIAt the end of this course, you will•Understand the basic concepts and terminologies in Machine Learning•Gain intuition about how various Machine Learning and Deep Learning algorithms work•Learn how to use Machine Learning to solve a business problem•Be able to apply this knowledge to pursue a vendor certificationAre there any pre-requisites?Students must have a basic knowledge of undergraduate level mathematics in areas like Linear Algebra, Probability, Statistics and Calculus.  The course will provide a basic refresher on these concepts.How much programming is needed?Although there are labs in the course, they are optional. You can go through the course without doing any programming. However, a basic knowledge of Computer Science and programming would help.The algorithms discussed in the course will be shown using pseudo code.We have an optional module on Python that will be a refresher for those who have basic familiarity with the language.Throughout the course we will provide various quizzes to test your understanding of the material.This course will benefit both technical and non technical users who want a foundational understanding of this technology. And it will help you learn the fundamentals and begin exploring this space on your own.And for those looking to get certified in this area, this course will be that important first step.Join the AI revolution. Don't wait. Enroll now!

Overview

Section 1: Introduction

Lecture 1 About this course

Lecture 2 Installation of software and course materials

Lecture 3 Video Installation of software (Mac) - Optional

Lecture 4 Python Jupyter notebook refresher - Optional

Lecture 5 Video Python Refresher - Optional

Lecture 6 Video Numpy Pandas Matplotlib refresher - Optional

Lecture 7 Introduction to Machine Learning and Deep Learning

Section 2: Math Refresher

Lecture 8 Math refresher - Statistics and Probability

Lecture 9 Math refresher - Functions And Calculus

Lecture 10 Video Math Refresher Functions

Section 3: Data Concepts

Lecture 11 Data Concepts

Section 4: Machine Learning And Deep Learning

Lecture 12 Machine Learning Terminology

Lecture 13 Machine Learning Overview

Section 5: Regression

Lecture 14 Regression

Lecture 15 Video sklearn linear regression - Optional

Lecture 16 Video sklearn logistic regression - Optional

Lecture 17 Video sklearn multi variate regression - Optional

Section 6: Decision Trees

Lecture 18 Decision Trees

Lecture 19 Video sklearn decision trees - Optional

Lecture 20 Video sklearn decision trees iris - Optional

Lecture 21 Video xgboost decision tree iris - Optional

Section 7: Neural Networks

Lecture 22 Neural Networks

Lecture 23 Video keras - Optional

Section 8: K Means

Lecture 24 K Means

Lecture 25 Video sklearn KMeans - Optional

Section 9: Estimating performance

Lecture 26 Estimating performance

Section 10: Advanced algorithms

Lecture 27 Advanced algorithms

Section 11: Practice Test

Section 12: Closing thoughts

Lecture 28 Bonus Lecture

Students aspiring to begin a career in AI,IT Professionals and Managers who want to understand the basic concepts,Just about anyone who is curious to learn about AI