This course have been created keeping in mind to deliver the foundation of ML to students, working professionals.
Hey there, I am a professional ML engineer, working for a big US retail client, and worked on many complex problems with their solutions. This course is all about my experience and what I think is important for students to learn. Please join me and I hope you enjoy this course, the same way I did making this course for you.
What you’ll learn
- One of the best slides and learning material from scratch for Learners..
- Learn very basics to pro level in machine learning..
- Learn the practical application which they can use with ML..
- Identify what strategy they can use to solve a given ML problem..
- Drive a given ML projects and have great understanding about end to end ML approaches..
Course Content
- Introduction –> 2 lectures • 52min.
- Statistics and mathematics –> 5 lectures • 1hr 54min.
- Data Preprocessing –> 2 lectures • 55min.
- Feature Engineering –> 2 lectures • 24min.
- Regression –> 5 lectures • 1hr 30min.
- Classification Algorithms –> 5 lectures • 2hr 3min.
- Unsupervised Learning –> 3 lectures • 1hr 5min.
- Time series Modelling concepts –> 3 lectures • 48min.
- Ensemble Learning –> 3 lectures • 36min.
Requirements
Hey there, I am a professional ML engineer, working for a big US retail client, and worked on many complex problems with their solutions. This course is all about my experience and what I think is important for students to learn. Please join me and I hope you enjoy this course, the same way I did making this course for you.
In this course, we will be discussing ML algorithms along with detailed examples. We will also be discussing the final end project that will comprise building a real-life very common application.
This course not only targets very new people like students but also targets experienced professionals looking to increase their knowledge of ML.
The only expectation is you should know some basic Python programming and be able to install Python libraries like sklearn, pandas, scipy, etc, and Jupyter Notebook in which we have coded our solution.
We have divided this course into several sections each section will go first into detail in a given presentation which is free to download and use for your purpose and then that course fully goes into the demo which will increase your understanding of how to implement that in the Python library.
Once you figure out the pattern of the course we have developed it will be fairly easy that you can explore and search for your own solution and thought process to develop your project.
It’s an amazing journey that we can have, Let’s learn together and build a better world with Machine learning.