Want to learn the required mathematical background need for learning digital technologies
like Machine Learning, Deep Learning, Computer Vision, Data Science and NLP? ?
This Classroom has everything that you need to get started!
Author: Yogesh Pandey (Personal Page)
-
Classroom Overview
-
Basic of Python and NumPy
- Video - Python Basics
- Video - Introduction to NumPy and Matplotlib
- Lab - Basic of Python, NumPy and Matplotlib [Python]
-
Linear Algebra
- Video - What are Linear Equations?
- Video - What are Functions?
- Video - Introduction to Vectors
- Lab - Understanding Linear Algebra [Python]
-
Introduction to Matrices
- Video - Introduction to Matrices
- Video - Solving Linear Equations with Matrices
- Video - What are Eigenvalues and Eigenvectors?
- Lab - Introduction to Matrices [Python]
-
Basics of Calculus
- Video - What is the rate of change?
- Video - Introduction to differentiation
- Video - Introduction to Integration
- Lab - Basics of Calculus [Python]
-
Statistics and Probability
- Video - Introduction to Statistics
- Video - Visualizing data
- Video - Introduction to Probability Theory
- Lab - Statistics and Probability [Python]
-
Implementation examples: Predicting Something
- Video - Making Predictions
- Lab - Predicting Housing Prices[Python]
The goal of this classroom is to provide you with necessary mathematical background knowledge help you start your journey into the world of digital technology.
The topics and techniques demonstrated in this classroom are primarily oriented towards learners wanting to learn Mathematical concepts used in the field of Computer Science, Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Data Science and NLP.
The Classroom is aimed at providing blended and experiential learning and is written to facilitate learning by doing. You will find the notebooks with embedded videos on the sub-topics, hands-on exercises and documentation on the topics all in one place. The videos are time-stamped so you can skip the parts that you are already familiar with.
You can run the classroom content in two ways:
If you want to experiment with the code in a live environment you can also use binder.
Binder allows to create a live environment where you can execute code just as if you were on your computer based on a GitHub repository, it is very awesome!
Click on the button below to launch binder:
Note: you could use binder to complete the exercises but it will not save!!
You can essentially "download" the contents of this repository by cloning the repository or by clicking "Clone or download" button and then "Download ZIP":
After you download and extracted the zip file into a folder you can follow the steps to set up your local environment:
These labs have been validated on Windows-10. But you can use them in any environment.
- Python3-pip
- Jupyter
- Run the Jupyter Notebook
$ py -m notebook- It opens in the default browser, locate the required Jupyter notebook (m4dt.ipynb) file and double click on it to open and run.
If you have questions or experience problems please use the issues tab of this repository.