This repository is to demonstrate Machine Learning Algorithms Implementation from scratch in Python and also using scikit learn python library. So you can compare the result from both. It'll help you to understand what is happening under the hood when you predict using machine learning algorithms.
In recent years, we have seen the enormous development in the field of AI (Artificial Intelligence) and machine learning. Machine learning is giving us tremendous results in the analysis of medical images and predict diseases. Which is very helpful to the doctors for the treatment. Recently, you have heard about the Google Alpha-go program which was able to beat a world champion in the strategy game by using deep reinforcement learning.
You can see different types of machine learning tools like recommendation tools which are mostly used by E-commerce websites. You often see your scrolled products on every site you visit as an advertisement and different chat-bots which is available to give you an information 24 Hours. So this is the power of machine learning and artificial intelligence.
So, Machine learning is used in many industries, like finance, online advertising, medicine, and robotics and every new field comes with AI.
In this course, we are first going to understand the basics of machine learning and then we will cover the supervised machine learning algorithms one by one. We have covered the following Supervised Machine Learning algorithms:
- Linear Regression
- Multiple Linear Regression
- Logistic Regression
- K nearest neighbor (KNN)
- Naive Bayes
- Support Vector Machine
- Decision Tree
- Random Forest
We have explained each algorithm in 3 steps - Overview, Maths behind the algorithm and Python Implementation
We’ll discuss the Sci-Kit Learn library because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.
We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.
All the materials for this course are FREE. You can download and install Anaconda Package with simple commands on Windows, Linux, or Mac.
###TIPS for Learning (for getting through the course):
- Don't rush! Watch it at 1x and don't go to the next video until you're fully confident.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don't, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Realize that most exercises will take you days or weeks to complete.
- Write code yourself, don't just sit there and look at my code.