Machine Learning using Python [FREE]

Free Certification Course Title: Machine Learning using Python 

Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in Python

Machine Learning using Python

What you’ll learn:

  • Learn how to solve real life problem using the Machine learning techniques
  • Advanced Machine Learning models such as Decision trees, Random Forest, SVM etc.
  • How to do basic statistical operations and run ML models in Python
  • Understanding of basics of statistics and concepts of Machine Learning
  • How to convert business problem into a Machine learning problem
  • In-depth knowledge of data collection and data preprocessing for Machine Learning problem

Requirements:

  • Students will need to install Anaconda software but we have a separate lecture to guide you install the same

Who this course is for:

  • People pursuing a career in data science
  • Working Professionals beginning their Data journey

Description:

You’re looking for a complete Machine Learning course in Python that can help you launch a flourishing career in the field of Data Science and Machine Learning, right?

You’ve found the right Machine Learning course!

After completing this course, you will be able to:

· Confidently build predictive Machine Learning models using Python to solve business problems and create business strategy

· Answer Machine Learning related interview questions

· Participate and perform in online Data Analytics competitions such as Kaggle competitions

Check out the table of contents below to see what all Machine Learning models you are going to learn.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear regression. This course will give you an in-depth understanding of machine learning and predictive modelling techniques using Python.

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.