Introduction to machine learning with Python, robust models , basic understanding of machine learning, using one of the most used and robust machine learning algorithm, supervised ml.
What you”ll learn:
- Training basic machine learning model
- Using Kneighbors machine learning classifier
- How to predict on unseen data
- How to deal with classification problems
Description
This course covers the basic aspects of machine learning in python. We have worked on real life examples to make things clear. We have covered one the most used machine learning algorithm, k nearest neighbor along with the famous flowers classification example.
What you will learn in this course:
- Why and how machine learning.
- How to use python for working with data in excel or csv format.
- How to make data suitable for machine learning algorithm.
- How to train a machine learning algorithm and make prediction from our model.
and much more! gear up!
It is advanced that you should practice the codes as well with us. This will create a strong base of yours in the field of data science and machine learning.
Each code is explained in this course so if you are new to python, still you will find this course helpful. You can contact us in any case as well.
The way models are trained in this course, we can train other model as well with little changes. That’s way we think that this course is good suit for you. Furthermore if you find any error in this course or any other thing that we should concern about, feel free to contact us.
When you will start the course, take it slow and steady. This course can take your 3-4 days (maybe more or maybe less) for sure. But we believe that you will learn some robust stuff here.
Good luck for your journey!
Who this course is for:
- A person who want to get started with machine learning
- A data scientist eager to train machine learning models
- A person want to learn about robust classification machine learning algorithm
