Data Science A-Z : Machine Learning with Python & R

Interested in the field of Data Science & Machine Learning? Then this course is for you!

Learn Data Science & Machine Learning by doing! Hands On Experience 

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! 

Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

This course is for those  :

• Anyone interested in Machine Learning.

• Students who have at least high school knowledge in math and who want to start learning Machine Learning.

• Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

• Any students in college who want to start a career in Data Science.
  

• Any data analysts who want to level up in Machine Learning.

• Any people who are not satisfied with their job and who want to become a Data Scientist.

• Any people who want to create added value to their business by using powerful Machine Learning tools.

  
  

What is Data Science ?

Data science is used  to extract patterns or insights from data to predict future or to understand customer behavior and so on.

Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data

Mining large amounts of structured and unstructured data to identify patterns can help an organization to reduce costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.

Some Data Science and machine learning Applications

• Netflix  uses data science & machine learning to mine movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce.

• Companies like Flipkart and Amazon uses data science and machine learning to understand the customer shopping behavior to do better recommendations.

• Gmail's spam filter uses data science (machine learning algorithm) to process incoming mail and determines if a message is junk or not..

• Proctor & Gamble utilizes data science (machine learning ) models to more clearly understand future demand, which help plan for production levels more optimally.

Why Programming  Won't Work in some Cases??

Have you ever thought of the scenario where all the cars will be moving without a driver that means something like automated machines say for example automatic washing machine.

But there is a difference.

1. For automatic washing machine,we can write programs for the washing machine functionality.

2. For automated cars without drivers in high traffic.Just imagine ,how complex and dangerous it will be when someone starts coding /programming for such functionalities.For cars to automate we would require something which is called "Machine Learning "

COURSE DETAILS AS BELOW  :

• DATA STRUCTURES ,etc. in R & PYTHON  as follows :

1. Vectors

2. Matrices

3. Data Frames

4. Factors 

5. Numerical/Categorical Variables

6. List

7. How to convert matrix into data frame

• PROGRAMMING IN R &PYTHON

• DATA VISUALIZATION

  
  

• IMPLEMENTATION OF MACHINE LEARNING MODELS  as follows:

1. Linear Regression & Logistic Regression

2. Decision Tree

3. Random Forest

4.Neural Networks

5. Deep learning 

6. H2o framework

7. Cross validation /How to avoid Over fitting

8. Dimensionality Reduction Techniques

• LEARN FROM SCRATCH [HOW TO DO ML IN PYTHON]

  
  

• SEE IN  REAL TIME HOW OPTIMIZATION WORKS TO GET A MACHINE LEARNING MODEL

All the materials for this data science & machine learning course are FREE. You can download and install R & Python,  with simple commands on Windows, Linux, or Mac.

This course focuses on "how to build and understand", not just "how to use".It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. 

THE COURSE IS DESIGNED IN SUCH A WAY WHICH GIVES MORE OF PRACTICAL SENSE FOR MACHINE LEARNING & DATA SCIENCE  IN VERY LESS AMOUNT OF TIME

So what are you waiting for ? Enroll in this course and start your future journey !!