Machine Learning Basics with Python by Start-Tech Academy

Machine Learning Basics with Python

Master the basics of Machine Learning in Python and use Regression Models to solve business problems

Testimonials

 
This is very good, i love the fact the all explanation given can be understood by a layman

Joshua
 Good introductory course to Machine Learning. It is true that a lot of the time is spent in setting up the data. This course covers how to do that too. Well done 
Hetal Patel

Concepts are clearly taught. Shared resources also make it easy to follow 

Abhishek

FAQs

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear regression.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

How this course will help you?

After completing this course you will be able to:
  • Identify the business problem which can be solved using linear regression technique of Machine Learning.
  • Create a linear regression model in Python and analyze its result.
  • Confidently practice, discuss and understand Machine Learning concepts

What is covered in this course?

This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.

Below are the course contents of this course on Linear Regression:
  • Section 1 - Basics of Statistics
This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean, median and mode and lastly measures of dispersion like range and standard deviation
  • Section 2 - Python basic
This section gets you started with Python.
This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
  • Section 3 - Introduction to Machine Learning
In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.
  • Section 4 - Data Preprocessing
In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.
  • Section 5 - Regression Model
This section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.
By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.

What's included?

Video Icon 53 videos File Icon 2 files

Contents

Introduction
Welcome to the course!
3 mins
Course contents
6 mins
Course Resourses
8.88 MB
Basics of Statistics
Types of Data
5 mins
Types of Statistics
3 mins
Describing data Graphically
12 mins
Measures of Centers
8 mins
Measures of Dispersion
5 mins
Setting up Python and Jupyter Notebook
Installing Python and Anaconda
4 mins
Opening Jupyter Notebook
10 mins
Introduction to Jupyter
14 mins
Arithmetic operators in Python: Python Basics
5 mins
Strings in Python: Python Basics
20 mins
Lists, Tuples and Directories: Python Basics
19 mins
Working with Numpy Library of Python
12 mins
Working with Pandas Library of Python
10 mins
Working with Seaborn Library of Python
9 mins
Introduction to Machine Learning
Introduction to Machine Learning
17 mins
Building a Machine Learning Model
9 mins
Data Preprocessing
Gathering Business Knowledge
4 mins
Data Exploration
4 mins
The Dataset and the Data Dictionary
8 mins
Study Material and datasets
8.85 MB
Importing Data in Python
7 mins
Univariate analysis and EDD
4 mins
EDD in Python
13 mins
Outlier Treatment
5 mins
Outlier Treatment in Python
15 mins
Missing Value Imputation
4 mins
Missing Value Imputation in Python
5 mins
Seasonality in Data
4 mins
Bi-variate analysis and Variable transformation
17 mins
Variable transformation and deletion in Python
10 mins
Non-usable variables
5 mins
Dummy variable creation: Handling qualitative data
5 mins
Dummy variable creation in Python
6 mins
Correlation Analysis
11 mins
Correlation Analysis in Python
8 mins
Linear Regression
The Problem Statement
2 mins
Basic Equations and Ordinary Least Squares (OLS) method
9 mins
Assessing accuracy of predicted coefficients
15 mins
Assessing Model Accuracy: RSE and R squared
8 mins
Simple Linear Regression in Python
15 mins
Multiple Linear Regression
5 mins
The F - statistic
9 mins
Interpreting results of Categorical variables
6 mins
Multiple Linear Regression in Python
15 mins
Test-train split
10 mins
Bias Variance trade-off
7 mins
Test train split in Python
11 mins
Other Linear Models
Linear models other than OLS
5 mins
Subset selection techniques
12 mins
Shrinkage methods: Ridge and Lasso
8 mins
Ridge regression and Lasso in Python
24 mins
Extras: Heteroscedasticity
3 mins