Course subtitle

If you want to learn Machine Learning from basic to advance level then this course is for you.

What will students learn in this course?

The course for Introduction to Machine Learning is to help you understand what machine learning can and can’t do for you today and what it might do for you in the future. Part of the emphasis of this course is on using the right tools.

This course uses both Python and R to perform various tasks.

The emphasis is on getting you up and running as quickly as possible, and to make examples straightforward and simple so that the application code doesn’t become a stumbling block to learning.

Who are the target students?

Everyone who is interested in making things better understandable, usable, and reliable.

It’s a beginner course and believes that learned students will be benefited maximally and able to concrete concepts for their future endeavors.

Course description

Course Name: An Introduction to Machine Learning

The term machine learning has all sorts of meanings attached to it today, especially after Hollywood’s (and others’) movie studios have gotten into the picture. Films such as Ex Machina have tantalized the imaginations of moviegoers the world over and made machine learning into all sorts of things that it really isn’t. Of course, most of us have to live in the real world, where machine learning actually does perform an incredible array of tasks that have nothing to do with androids that can pass the Turing Test (fooling their makers into believing they’re human). An Introduction to Machine Learning provides you with a view of machine learning in the real world and exposes you to the amazing feats you really can perform using this technology. Even though the tasks that you perform using machine learning may seem a bit mundane when compared to the movie version, by the time you finish this video lectures, you realize that these mundane tasks have the power to impact the lives of everyone on the planet in nearly every aspect of their daily lives. In short, machine learning is an incredible technology — just not in the way that some people have imagined.

Why do we use Machine Learning?

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real-time – organizations are able to work more efficiently or gain an advantage over competitors. The Different Industries which are engaging heavily for development and deployment of Machine Learning are:

1. Financial Services

2. Health Care

3. Oil & Gas

4. Government

5. Retail

6. Transportation

What we can do with Machine Learning?

Using algorithms to build models that uncover connections, organizations can make better decisions without human intervention.

1. Opportunities for machine learning in business

2. Applying machine learning to IoT

3. Robot locomotion

4. Medical diagnosis

5. Search engines

6. Telecommunication

7. General game playing

These are some few applications example and numerous others have not listed.

Course content

PART 1: INTRODUCING HOW MACHINES LEARN.

Getting the Real Story about AI

Learning in the Age of Big Data

Having a Glance at the Future

PART 2: PREPARING YOUR LEARNING TOOLS

Installing an R Distribution

Coding in R Using RStudio

Installing a Python Distribution

Coding in Python Using Anaconda

Exploring Other Machine Learning Tools

PART 3: GETTING STARTED WITH THE MATH BASICS

Demystifying the Math behind machine Learning

Descending the Right Curve

Validating Machine Learning

Starting with Simple Learners

PART 4: LEARNING FROM SMART AND BIG DATA

Preprocessing Data

Leveraging Similarity

Working with Linear Models the Easy Way

Hitting Complexity with Neural Networks

Going a Step beyond Using SupportVector Machines

Resorting to Ensembles of Learners

PART 5: APPLYING LEARNING TO REAL PROBLEMS

Classifying Images

Scoring Opinions and Sentiments

Recommending Products and Movies

PART 6: THE PART OF TENS

Ten Machine Learning Packages to Master

Ten Ways to Improve Your MachineLearning Models

Course Curriculum

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