Data Science A-Z : Machine Learning with Python & R
3.9 (152 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
617 students enrolled

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

By Data Scientist / IITian for Beginners . Data Science/Machine Learning with Python & R for beginners to advance
3.9 (152 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
617 students enrolled
Created by Arpan Gupta
Last updated 3/2020
English
English [Auto]
Current price: $20.99 Original price: $34.99 Discount: 40% off
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30-Day Money-Back Guarantee
This course includes
  • 12.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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What you'll learn
  • Data Science & Machine Learning
  • How to do machine learning in Python & R
  • How to do Data Manipulation & Preprocessing
  • How to Create Data Visualizations
  • Use Python & R for Data Analysis
Course content
Expand all 90 lectures 12:18:02
+ Introduction to R & RStudio
4 lectures 20:23
Install R and RStudio
01:18
Introduction to RStudio
08:25
What is Package
03:43
How to Install Package
06:57
+ Data Types and Data Structures
8 lectures 01:48:51
Data Types
06:32
Vectors
11:53
Basic Operations in Vectors
08:46
List
13:32
DataFrame
20:23
Matrices
16:30
How to handle data frames and matrices in R and how to convert into each other.
Data Frames and Data Matrix
4 questions
Accessing the Elements or Subsetting
27:03
How to read csv file
04:12
+ Data Visualization using ggplot2
9 lectures 51:08
Numerical and Categorical Variables
06:19

How to plot One Numerical Variable 

One Numerical Variable
07:59

How to plot two numerical variables 

Two Numerical Variables
05:11
Two Numerical and One Categorical
04:00
Two Numerical and Two Categorical
03:57
One Categorical Variable : Barplot
03:17
Two Categorical Barplot
04:13
More than Five/Six Variables :Facet_Wrap
10:26
One,Two and more than three Variables : Boxplot
05:46
+ Data Manipulation
7 lectures 48:06
Introduction to Data Manipulation
06:06
Select the Column :Select
06:42
Filter the rows :Filter
07:34
Mutate :Create New Column
03:09
Summarize the columns :Summarize
05:33
Summarize by groups : Group and Summarize
07:14
apply,lapply and sapply functions
11:48
+ Problems dealt in Machine Learning
1 lecture 08:05
Difference between Regression & Classification
08:05
+ Model Fitting Process : Classification
7 lectures 52:46
Model Fitting Process : Importing Required Libraries
03:17
Model Fitting Process : Set the Seed
01:26
Model Fitting Process : Reading the data set
09:30
Model Fitting Process : Converting Categorical into Factor
03:41
Model Fitting Process : Data Partition
04:29
Model Fitting Process : Fitting Model
17:30
Model Fitting Process : Predictions
12:53
+ Other Classification Models
3 lectures 23:06
Random Forest
08:09
What is Support Vector Machines
04:08
Support Vector Machines
10:49
+ Regression
1 lecture 31:27
Linear Regression
31:27
Requirements
  • No Prerequisite
Description

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 !!

Who this course is for:
  • Who wants to be data scientist
  • For Software Developer who wants to be data scientist
  • 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.