Data Science ML for Python-Python Data Science Made Easy

Data Science & ML for Python-Python & Data Science Made Easy, available at $19.99, has an average rating of 4.45, with 82 lectures, 12 quizzes, based on 43 reviews, and has 3487 subscribers.

You will learn about 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. This course is ideal for individuals who are Beginners or Intermediate or Python or Machine Learning or Data Science or R programming It is particularly useful for Beginners or Intermediate or Python or Machine Learning or Data Science or R programming.

Enroll now: Data Science & ML for Python-Python & Data Science Made Easy

Summary

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

Price: $19.99

Average Rating: 4.45

Number of Lectures: 82

Number of Quizzes: 12

Number of Published Lectures: 82

Number of Published Quizzes: 12

Number of Curriculum Items: 94

Number of Published Curriculum Objects: 94

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

Beginners

Intermediate

Python

Machine Learning

Data Science

R programming

Target Audiences

Beginners

Intermediate

Python

Machine Learning

Data Science

R programming

This course is for AspirantData 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

Course Curriculum

Chapter 1: Basic and Advanced Level of Python Development

Lecture 1: 1. 1. Introduction to Trainer

Lecture 2: 1. 2. Course Outline

Lecture 3: 1. 3. Why Python Part I

Lecture 4: 1. 4. Why Python Part II

Lecture 5: 1. 5. Downloading and Accessing Python from Spyder

Lecture 6: 1. 6. Using Jupyter based application to write Python codes

Lecture 7: 1. 7. Basic commands in python to comment and execute

Lecture 8: 1. 8. Saving ipynb file and uploading it to your system

Lecture 9: 1. 9. Types of Objects – Single data elements in Python

Lecture 10: 1. 10. Types of Objects – Multiple data elements tuples and lists

Lecture 11: 1. 11 Types of ObjectTypes of Objects – Multiple data elements sets & dictionary

Lecture 12: 1. 12. Summary of Object Types

Lecture 13: 1. 13. Concept of Memory Location

Lecture 14: 1. 14. Python Basic commands

Lecture 15: 1. 15. Concept of Packages

Lecture 16: 1. 16. Panda series at a glance

Lecture 17: 1. 17. Concept of Packages

Lecture 18: 1. 18. Indexing a tuple

Lecture 19: 1. 19. Indexing list and multiple hierarchy objects

Lecture 20: 1. 20. Indexing set and a dictionary

Lecture 21: 1. 21. Converting Object type – Part I

Lecture 22: 1. 22. Converting Object type – Part II- tuple, list, set to Other Object types

Lecture 23: 1. 23. List comprehension

Lecture 24: 1. 24. Set functions

Lecture 25: 1. 25. Operators – Membership and Logical

Lecture 26: 1. 26. Operators – and or

Lecture 27: 1. 27. Case Study with and or Operator

Lecture 28: 1. 28. If else conditions Part I – With 2 conditions

Lecture 29: 1. 29. If else conditions Part II – More than 2 conditions

Lecture 30: 1. 30. If else conditions Part III- Nesting if else

Lecture 31: 1. 31. Python functions and Package specific functions

Lecture 32: 1. 32. User defined function Part I – Non-parameterized function

Lecture 33: 1. 33. User defined function Part II – parameterized function

Lecture 34: 1. 34. User defined function Part III

Lecture 35: 1. 35. Types of Loops – for and while loops

Lecture 36: 1. 36. Types of Loops – for loop in detail with examples

Lecture 37: 1. 37. Types of Loops – While loop in detail with examples

Lecture 38: 1. 38. NumPy Package & Introduction to Array

Lecture 39: 1. 39. NumPy Array – 1D and 2D

Lecture 40: 1. 40. Array – 3D

Lecture 41: 1. 41. Array computations and functions

Lecture 42: 1. 42. Overview of Pandas package

Lecture 43: 1. 43. Pandas Series

Lecture 44: 1. 44. Pandas – Data frames

Lecture 45: 1. 45. Pandas – Dataframe – Indexing

Lecture 46: 1. 46. Concept of working directory and Importing data

Lecture 47: 1. 47. Data wrangling with data frames

Chapter 2: Basic and Advanced R programming

Lecture 1: 2. 1 Brief background about R & Downloading R Studio

Lecture 2: 2. 1. 1 Creating and saving a R script file

Lecture 3: 2. 2 Basic commands in R and Creating a Vector object

Lecture 4: 2. 3 Creating a matrix and data frame

Lecture 5: 2. 4 Concept of Packages

Lecture 6: 2.5 Indexing and subsetting with Vector, matrix, list and data frame

Lecture 7: 2.6 Concept of working directory and Importing & Exporting a data file

Lecture 8: 2.7 dplyr package for data frames

Lecture 9: 2. 8 Confused with Python and R. What to do Next?

Chapter 3: Introduction to Data Science and Decision Making

Lecture 1: 3. 1 What is Analytics with industry examples

Lecture 2: 3. 2 Data Analytics – Case Study E commerce Organization

Lecture 3: 3. 3 Types of Analytics – Descriptive, Diagnostic, Predictive & Prescriptive

Chapter 4: Basic Statistics

Lecture 1: 4. 1 Measures of Central Tendency

Lecture 2: 4. 2 Measures of Spread

Lecture 3: 4. 3 Types of Variables

Chapter 5: Inferential Statistics

Lecture 1: 5. 1 Population vs Sample and Descriptive & Inferential statistics

Lecture 2: 5. 2 Frequency Distribution and Normal distribution

Lecture 3: 5. 3 Normal distribution in detail

Lecture 4: 5. 4 Z-score in Normal Distribution

Lecture 5: 5. 5 Hypothesis Testing

Lecture 6: 5. 6 Hypothesis testing with Python

Chapter 6: Advanced Statistics – Predictive Analytics

Lecture 1: 6. 1 Basic Understanding of Linear regression

Lecture 2: 6. 2 Linear Regression with intercept

Lecture 3: 6. 3 Linear Regression – Prediction and Error rates

Lecture 4: 6. 4 Linear Regression – R – square

Lecture 5: 6. 5 Linear Regression with Python Part I

Lecture 6: 6. 6 Linear Regression with Python Part II

Lecture 7: 6. 7 Supervised and Unsupervised learning Techniques

Lecture 8: 6. 8 Model Validation

Lecture 9: 6. 9 Logistic Regression in Python

Chapter 7: Machine Learning

Lecture 1: 7.1 Machine Learning Model – Support Vector Machine Algorithm

Lecture 2: 7.2 SVM with Python

Lecture 3: 7.3 K nearest neighbor Algorithm

Lecture 4: 7.4 K nearest neighbour with Python

Instructors

Steven Martin
Data Scientist /BI Professional & Machine Learning Engineer

Rating Distribution

1 stars: 2 votes

2 stars: 3 votes

3 stars: 7 votes

4 stars: 17 votes

5 stars: 14 votes

Frequently Asked Questions

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