Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Data Science & ML for Python-Python & Data Science Made Easy
Rating: 4.2 out of 5(45 ratings)
3,493 students

Data Science & ML for Python-Python & Data Science Made Easy

Beginners in Python & R for Data Science: Introduction to Data science and Practical applications of Data Science and ML
Created bySteven Martin
Last updated 8/2020
English

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
  • Concept of Linear and Logistic Regression implementing with Python code
  • Machine Learning (ML) Algorithms concepts with Python code
  • ML Algorithms - Support Vector Machine
  • Machine Learning Algorithms. - K nearest neighbors
  • Practical Application of Data Science and Machine Learning in Healthcare and Real estate Industry
  • An approach and outlook a Data Scientist and ML professional should adopt while solving business problems in real life
  • Engaging Course with Multiple choice questions for Students towards end of each section for Knowledge tests
  • Practical & Comprehensive Assignment with Guidelines explaining challenges faced by DS/ML professional and how to deal with such roadblocks.

Course content

8 sections • 82 lectures • 10h 53m total length

  • 1. 1. Introduction to Trainer3:56

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

  • 1. 2. Course Outline6:17
  • 1. 3. Why Python Part I6:51
  • 1. 4. Why Python Part II10:33
  • 1. 5. Downloading and Accessing Python from Spyder10:37
  • 1. 6. Using Jupyter based application to write Python codes10:26
  • 1. 7. Basic commands in python to comment and execute5:16
  • 1. 8. Saving ipynb file and uploading it to your system5:13
  • 1. 9. Types of Objects - Single data elements in Python8:56
  • 1. 10. Types of Objects - Multiple data elements tuples and lists7:05
  • 1. 11 Types of ObjectTypes of Objects - Multiple data elements sets & dictionary5:06
  • 1. 12. Summary of Object Types5:15
  • Types of Objects in Python
  • 1. 13. Concept of Memory Location4:43
  • 1. 14. Python Basic commands3:23
  • 1. 15. Concept of Packages6:54
  • 1. 16. Panda series at a glance5:58
  • 1. 17. Concept of Packages6:54
  • 1. 18. Indexing a tuple8:39
  • 1. 19. Indexing list and multiple hierarchy objects5:41
  • 1. 20. Indexing set and a dictionary4:22
  • 1. 21. Converting Object type - Part I13:14

    Converting Single data Element types object to Other object type

  • 1. 22. Converting Object type - Part II- tuple, list, set to Other Object types9:47
  • 1. 23. List comprehension3:50
  • 1. 24. Set functions7:55
  • 1. 25. Operators - Membership and Logical4:59
  • 1. 26. Operators - and or8:35
  • 1. 27. Case Study with and or Operator3:33
  • 1. 28. If else conditions Part I - With 2 conditions8:06
  • 1. 29. If else conditions Part II - More than 2 conditions3:13
  • 1. 30. If else conditions Part III- Nesting if else9:16
  • 1. 31. Python functions and Package specific functions4:57
  • 1. 32. User defined function Part I - Non-parameterized function5:44
  • 1. 33. User defined function Part II - parameterized function6:23
  • 1. 34. User defined function Part III2:56
  • 1. 35. Types of Loops - for and while loops8:10
  • 1. 36. Types of Loops - for loop in detail with examples4:49
  • 1. 37. Types of Loops - While loop in detail with examples7:42
  • 1. 38. NumPy Package & Introduction to Array3:30
  • 1. 39. NumPy Array - 1D and 2D12:48
  • 1. 40. Array - 3D4:21
  • 1. 41. Array computations and functions10:34
  • Knowledge Test - Numpy Arrays
  • 1. 42. Overview of Pandas package6:10
  • 1. 43. Pandas Series8:24
  • 1. 44. Pandas - Data frames4:47
  • 1. 45. Pandas - Dataframe - Indexing10:28
  • Knowledge Test - Indexing a data frame
  • 1. 46. Concept of working directory and Importing data9:44
  • 1. 47. Data wrangling with data frames13:12
  • Knowledge Test - Pandas Data Frame: Data Wrangling

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
  • Python
  • Machine Learning
  • Data Science
  • R programming

Report abuse