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Supervised Machine Learning with Python (Course VI)

Supervised Machine Learning with Python (Course VI)

Welcome to Course VI!

Hello and Welcome to this course on Machine Learning with Python!

In this course, you will be introduced to the amazing field of Machine Learning and you will learn how to build your Machine Learning models for two different kinds of tasks: regression and classification.

Objectives of the course

The learning objectives of the course are set out as follows:

  • Learn how to create dummy data using Numpy and Pandas
  • Learn about what is Machine Learning and its applications
  • Learn how to build various kinds of regression models
  • Learn how to build various kinds of classification models
  • Learn how to split data and evaluate Machine Learning models

You can expect to have all of these objectives met by the time you reach the end of this course.

Pre-requisites for the course

For the purpose of this course, we will be using Python. Python is a perfect choice for beginners as well as professionals working in the field of Machine Learning and Data Science due to its simplicity and wide range of library support.

If this is your first time working on Python, it may be hard for you to effectively grasp all the concepts. Therefore, the following pre-requisites are required for you to get the best out of the course:

  • Solid understanding of the Python programming language
  • Familiarity with NumPy, Pandas, and Matplotlib

If you do not satisfy the above pre-requisites, don’t worry! You can always come back later to this course once you are ready.

Best way to work through the course

The course is long and requires a good amount of attention from your end.

Before moving to the next lecture, we suggest you to set up your coding environment and open up your Jupyter Notebook. If you are a more advanced user of Python and have your own preferences, please feel free to choose an IDE that you prefer. However, all of the coding examples will be written for execution on Jupyter Notebook cells.

If you come across any problem, please check to see if your code matches exactly with the course or not. If you still are facing errors or have some doubts, please provide your question through the comment section of the specific chapter you are stuck on.

We also recommend you join our community and get connected to our vibrant network of data science aspirants. Once you are in the community, you can share your learnings, form a study group, or even get help building a project around Machine Learning.

Ready to start your journey in Machine Learning? Let us head on to the course and start learning.

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