Machine Learning and Data Science using Python & R
4.2 (17 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.
3,416 students enrolled

Machine Learning and Data Science using Python & R

An intermediate to expertise level course to learn Python and R with Machine Learning and Statistical Algorithms
New
4.2 (17 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.
3,416 students enrolled
Created by Steven Martin
Last updated 6/2020
English
English [Auto]
Current price: $11.99 Original price: $19.99 Discount: 40% off
2 days left at this price!
30-Day Money-Back Guarantee
This course includes
  • 11 hours on-demand video
  • 61 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • ✓ Python & R programming for Structured data/ tables. ✓ Python in demand packages used by Data Scientist and Machine Learning professionals. ✓ Basic, Inferential and Advanced Statistics. ✓ Linear and Logistic Regression. ✓ Machine Learning Algorithms.
Requirements
  • No pre-requisites. Good to have knowledge of Statistics and/or Programming
Description

This course is for Aspirant Data Scientists, Business/Data Analyst, Machine Learning & AI professionals planning to ignite their career/ enhance Knowledge in niche technologies like Python and R. You will learn with this program:

Basics of Python, marketability and importance

Understanding most of python programming from scratch to handle structured data inclusive of concepts like OOP,  Creating python objects like list, tuple, set, dictionary etc; Creating numpy arrays, ,Creating tables/ data frames, wrangling data, creating new columns etc.

Various In demand Python packages are covered like sklearn, sklearn.linear_model etc.; NumPy, pandas, scipy  etc.

R packages are discussed to name few of them are dplyr, MASS etc.

Basics of Statistics - Understanding of Measures of Central Tendency, Quartiles, standard deviation, variance etc.

Types of variables

Advanced/ Inferential Statistics - Concept of probability with frequency distribution from scratch, concepts like Normal distribution, Population and sample

Statistical Algorithms to predict price of houses with Linear Regression

Statistical Algorithms to predict patient suffering from Malignant or Benign Cancer with Logistic Regression

Machine learning algorithms like SVM, KNN

Implementation of Machine learning (SVM, KNN) and Statistical Algorithms (Linear/ Logistic Regression) with Python programming code

Who this course is for:
  • Beginners, Intermediate or expertise in Python and/or Statistics
Course content
Expand all 81 lectures 10:53:35
+ Basic and Advanced Level of Python programming language
47 lectures 05:29:12

Introduction to trainer experience in industry and training delivery. Intro to softwares and Machine learning algorithms that trainer has expertise.

Preview 03:56
1. 3. Why Python Part I
06:51
1. 4. Why Python Part II
10:33
1. 6. Using Jupyter based application to write Python codes
10:26
1. 8. Saving ipynb file and uploading it to your system
05:13
1. 9. Types of Objects - Single data elements in Python
08:56
1. 11 Types of ObjectTypes of Objects - Multiple data elements sets & dictionary
05:06
1. 12. Summary of Object Types
05:15
1. 13. Concept of Memory Location
04:43
1. 14. Python Basic commands
03:23
1. 15. Concept of Packages
06:54
1. 16. Panda series at a glance
05:58
1. 17. Concept of Packages
06:54
1. 19. Indexing list and multiple hierarchy objects
05:41
1. 20. Indexing set and a dictionary
04:22

Converting Single data Element types object to Other object type

1. 21. Converting Object type - Part I
13:14
1. 22. Converting Object type - Part II- tuple, list, set to Other Object types
09:47
1. 23. List comprehension
03:50
1. 24. Set functions
07:55
1. 25. Operators - Membership and Logical
04:59
1. 26. Operators - and or
08:35
1. 27. Case Study with and or Operator
03:33
1. 28. If else conditions Part I - With 2 conditions
08:06
1. 29. If else conditions Part II - More than 2 conditions
03:13
1. 30. If else conditions Part III- Nesting if else
09:16
1. 31. Python functions and Package specific functions
04:57
1. 32. User defined function Part I - Non-parameterized function
05:44
1. 33. User defined function Part II - parameterized function
06:23
1. 34. User defined function Part III
02:56
1. 35. Types of Loops - for and while loops
08:10
1. 36. Types of Loops - for loop in detail with examples
04:49
1. 37. Types of Loops - While loop in detail with examples
07:42
1. 38. NumPy Package & Introduction to Array
03:30
1. 39. NumPy Array - 1D and 2D
12:48
1. 40. Array - 3D
04:21
1. 41. Array computations and functions
10:34
1. 42. Overview of Pandas package
06:10
1. 43. Pandas Series
08:24
1. 44. Pandas - Data frames
04:47
1. 45. Pandas - Dataframe - Indexing
10:28
1. 46. Concept of working directory and Importing data
09:44
1. 47. Data wrangling with data frames
13:12
+ Basic and Advanced R programming
9 lectures 01:21:52
2. 1 Brief background about R & Downloading R Studio
06:31
2. 1. 1 Creating and saving a R script file
03:07
2. 2 Basic commands in R and Creating a Vector object
13:24
2. 3 Creating a matrix and data frame
16:25
2. 4 Concept of Packages
04:32
2.5 Indexing and subsetting with Vector, matrix, list and data frame
10:34
2.6 Concept of working directory and Importing & Exporting a data file
03:51
2.7 dplyr package for data frames
19:11

To get more clarity on the key differences between Python and R programming AND to reconcile the 2 languages in a short duration of time please refer to below course:

https://www.udemy.com/course/python-vs-r-key-differences-in-commands-and-syntaxes/

2. 8 Confused with Python and R. What to do Next?
04:17
+ Introduction to Data Analytics and Decision Making
3 lectures 36:24
3. 1 What is Analytics with industry examples
16:06
3. 2 Data Analytics - Case Study E commerce Organization
14:58
+ Basic Statistics
3 lectures 30:56
4. 1 Measures of Central Tendency
07:52
4. 2 Measures of Spread
14:59
+ Inferential Statistics
6 lectures 49:09
5. 1 Population vs Sample and Descriptive & Inferential statistics
08:04
5. 2 Frequency Distribution and Normal distribution
12:25
5. 3 Normal distribution in detail
08:01
5. 4 Z-score in Normal Distribution
05:31
5. 5 Hypothesis Testing
12:17
5. 6 Hypothesis testing with Python
02:51
+ Advanced Statistics - Predictive Analytics
9 lectures 01:43:14
6. 3 Linear Regression - Prediction and Error rates
17:16
6. 4 Linear Regression - R - square
06:00
6. 5 Linear Regression with Python Part I
12:39
6. 6 Linear Regression with Python Part II
15:58
6. 7 Supervised and Unsupervised learning Techniques
05:24
6. 8 Model Validation
06:23

Logistic regression on cancer data

6. 9 Logistic Regression in Python
26:57
+ Machine Learning
4 lectures 22:48
7.1 Machine Learning Model - Support Vector Machine Algorithm
06:29
7.2 SVM with Python
07:20
7.3 K nearest neighbor Algorithm
06:59
7.4 K nearest neighbour with Python
02:00
Basic object types in python
1 question
Machine learning
1 question