https://www.udemy.com/course/machine-learning-linear-regression-case-study

Predicting Boston house price with Linear Regression using scikit-learn !!

What you'll learn: 

Introduction to Machine Learning and linear regression from scratch

Predicting Boston house price with Regression !!

Requirements:

Yes, A basic knowledge in Python 3 is preferred.

Description:

We have covered-

What is Machine Learning and how does it works?

Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

Linear Regression Concept with simple regression model using Scikit Learn Library.

What are the types of Regressions?

Case Study-Boston house price prediction-predicts the price of houses in Boston using a machine learning algorithm called Linear Regression. To train our machine learning model ,we will be using scikit-learn’s boston dataset.

Analyse and visualize data using Linear Regression.

Plot the graph of results of Linear Regression to visually analyze the results.

Linear regression is starting point for a data science this course focus is on making your foundation strong for deep learning and machine learning algorithms.

End of the course you will be able to code your own regression algorithm from scratch.

After completing this course you will be able to:

Interpret and Explain machine learning models which are treated as a black-box

Create an accurate Linear Regression model in python and visually analyze it

Select the best features for a business problem

Remove outliers and variable transformations for better performance

Confidently solve and explain regression problems

This course will give you a very solid foundation in machine learning. You will be able to use the concepts of this course in other machine learning models.Who this course is for:Python developers curious about Machine Learning.Data Science and Machine leaning engineers.

Who this course is for:

Python developers curious about Machine Learning.

Data Science and Machine leaning engineers.