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 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
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
7. General game playing
These are some few applications example and numerous others have not listed.
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
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
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
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