Python for data science basics of Simple Linear regression

Python for data science & basics of Simple Linear regression, available at $59.99, has an average rating of 4, with 42 lectures, 1 quizzes, based on 2 reviews, and has 14 subscribers.

You will learn about Just Basic python required for data science and how to apply them in data science (Teaching Data science and ML is not the objective of this course) Introduction to data science to give you a flavor simple linear regression is used Python popular libraries including Numpy , Pandas and Matplotlib How to prepare the data for data science and how to develop a basic prediction model. This course is a combination of basic python required for data science and how to apply them in data science project environment Data Science and ML is not the objective of this course , those concepts are just used for basic understanding This course is ideal for individuals who are Students , all levels of working professionals wanting to know what is python and its use in data science , business analysts , Six sigma green belts , Six Sigma Black Belts , Entrepreneurs or Only for Novice to Python and Novice to statistics It is particularly useful for Students , all levels of working professionals wanting to know what is python and its use in data science , business analysts , Six sigma green belts , Six Sigma Black Belts , Entrepreneurs or Only for Novice to Python and Novice to statistics.

Enroll now: Python for data science & basics of Simple Linear regression

Summary

Title: Python for data science & basics of Simple Linear regression

Price: $59.99

Average Rating: 4

Number of Lectures: 42

Number of Quizzes: 1

Number of Published Lectures: 42

Number of Published Quizzes: 1

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 48

Original Price: ?1,299

Quality Status: approved

Status: Live

What You Will Learn

Just Basic python required for data science and how to apply them in data science (Teaching Data science and ML is not the objective of this course)

Introduction to data science to give you a flavor simple linear regression is used

Python popular libraries including Numpy , Pandas and Matplotlib

How to prepare the data for data science and how to develop a basic prediction model.

This course is a combination of basic python required for data science and how to apply them in data science project environment

Data Science and ML is not the objective of this course , those concepts are just used for basic understanding

Who Should Attend

Students , all levels of working professionals wanting to know what is python and its use in data science , business analysts , Six sigma green belts , Six Sigma Black Belts , Entrepreneurs

Only for Novice to Python and Novice to statistics

Target Audiences

Students , all levels of working professionals wanting to know what is python and its use in data science , business analysts , Six sigma green belts , Six Sigma Black Belts , Entrepreneurs

Only for Novice to Python and Novice to statistics

This course focuses on two things. First thing is to get you up to speed with understanding of  python required to jet start your data science journey. Python is an object oriented programing language and used extensively in the field of data science. The objective in this course is not master python programing but to understand what is required for your journey in machine learning and master that bit. Secondly ,  building a strong foundation of statistics required for the participants who aspires to become a data scientist or a Machine learning practitioner. This MUST NEED skill set will enable every participants to begin their smooth journey to Deep learning and Artificial Intelligence. This course focuses on maintaining an optimal balance of statistics , python programming for Data science  and core ML algorithms. The course begins with a complete demonstration of how to download and secure all the essential software required for this course and provides rationale for using such software. After helping you secure  necessary software required for the course ,  the course progresses to build your understanding and brush up your memory on basic statistics required  to make you comfortable with Advanced analytics. Once you are comfortable with it , instructor will spend sufficient time to train you on  python programming at the right level  for you to jet start your journey  with Data Science and Machine learning analytics.

Course Curriculum

Chapter 1: Introduction to analytics , data science and Machine learning

Lecture 1: Introduction

Chapter 2: Getting ready for python

Lecture 1: Installation of Anaconda navigator

Lecture 2: Accessing Jupyter notebook – Simplest method

Lecture 3: Setting up Jupyter notebook path

Chapter 3: Basics of Python exclusively for analytics and machine leanring

Lecture 1: Definition of variables

Lecture 2: Various operators used in python

Lecture 3: Some built in functions and Simplifying operators

Lecture 4: Usage of print statements

Lecture 5: Precision width , field width and padding

Lecture 6: Introduction to Data structures

Lecture 7: Introduction to data handling in lists

Lecture 8: Built in functions in lists continued

Lecture 9: Built in functions of list continued

Lecture 10: Built in functions in tuple

Lecture 11: Introduction to data handling in Sets

Lecture 12: Introduction to data handling in dictionary

Lecture 13: Introduction to Strings

Lecture 14: Introduction to strings continued

Lecture 15: Control flow statements for & while loop

Lecture 16: Control flow statements continued – If /else and elif

Lecture 17: Introduction to functions

Lecture 18: User defined functions continued

Lecture 19: Introduction to classes

Lecture 20: Most frequently used Machine Learning Libraries in Python – Numpy

Lecture 21: Most frequently used Machine Learning Libraries in Python – Pandas

Lecture 22: Most frequently used Machine Learning Libraries in Python – Pandas Continued

Lecture 23: Most frequently used Machine Learning Libraries in Python – Pandas Continued

Lecture 24: Most frequently used Machine Learning Libraries in Python – Pandas Continued

Lecture 25: Most frequently used Machine Learning Libraries in Python – Pandas Continued

Lecture 26: Application of Pandas and Numpy to treat for missing values

Chapter 4: Framework to drive Analytics & Data Pre- processing

Lecture 1: Framework to drive Analytics/Data Science /ML projects

Lecture 2: Data pre-processing- Accessing and and setting up variables

Lecture 3: Data pre processing -One Hot Encoder & Label encoder

Lecture 4: How to perform basic statistics with Python

Lecture 5: Handling outliers

Lecture 6: Feature scaling

Lecture 7: Splitting of data into Train Data and Test Data

Chapter 5: Introduction to Machine learning with Simple linear Regression

Lecture 1: Assumptions of Linear Regression

Lecture 2: Simple Linear Regression Model

Lecture 3: SLR on Python

Lecture 4: Cost Function & Gradient Descent

Lecture 5: SDG on Python

Instructors

Mathew Basenth Thomas
Lean, 6σ , Data & Machine Learning Enthusiast & Evangelist

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5 stars: 1 votes

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