Machine Learning using Python

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

Ratings 4.30 / 5.00

What You Will 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

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.

How will this course help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning, Python and predictive modelling in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning, Python and predictive modelling.

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.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques using R, Python, and we have used our experience to include the practical aspects of data analysis in this course.

We are also the creators of some of the most popular online courses - with over 1 million enrollments and thousands of 5-star reviews like these ones:

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

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, machine learning, Python, predictive modelling, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts of machine learning, Python and predictive modelling. Each section contains a practice assignment for you to practically implement your learning on machine learning, Python and predictive modelling.

Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

What are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 3 parts:

Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python

Understanding of models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.