Quick Start
Getting Started with Mindect.
Introduction
This docs are in progress and for now information about various things in Machine Learning including Supervised Learning, Unsupervised Learning and algorithms including Activation Function, Backward Propogation, Decision Trees Etc.
The level of the pages are marked as Beginner Friendly, Intermidiate and Advanced for the level of the pages.
What is machine learning?
You probably use it many times a day without even knowing it. Anytime you want to find out something like how do I make a sushi roll? You can do a web search on Google, Bing or Baidu to find out. And that works so well because their machine learning software has figured out how to rank web pages.
Or when you upload pictures to Instagram or Snapchat and think to yourself, I want to tag my friends so they can see their pictures. Well These apps can recognize your friends in your pictures and label them as well. That's also machine learning.
Or if you've just finished watching a Star Wars movie on the video streaming service and you think what other similar movies can I watch? Well the streaming service will likely use machine learning to recommend something that you might like.
Each time you use voice to text on your phone to write a text message. Hey Sam, how's it going? Or tell your phone. Hey Siri play a song by Rihanna, or ask your other phone okay Google show me Indian restaurants near me. That's also machine learning.
Each time you receive an email titled, Congratulations! You've won a million dollars. Well maybe you're rich, congratulations. Or more likely your email service will probably flag it as spam. That too is an application of machine learning.
Beyond consumer applications that you might use, AI is also rapidly making its way into big companies and into industrial applications. For example, In climate change, I'm glad to see that machine learning is already hoping to optimize wind turbine power generation.
Or in healthcare, is starting to make its way into hospitals to help doctors make accurate diagnosis. Or recently at Landing AI have been doing a lot of work, putting computer vision into factories to help inspect if something coming off the assembly line has any defects.
That's machine learning, it's the science of getting computers to learn without being explicitly programmed.
You can learn further about it by clicking here.
Note
In this Machine Learning Notes a significant emphasis is placed on providing practical advice for the application of learning algorithms. It is crucial to not only possess a comprehensive set of tools but also to have the expertise to apply them effectively. Merely having access to state-of-the-art tools does not guarantee the ability to construct a complex structure, such as a three-story house. Similarly, in the field of machine learning, it is essential to have both the tools and the knowledge to utilize them efficiently. It is a common occurrence for even experienced machine learning teams at top tech companies to struggle with the application of machine learning algorithms to certain problems, sometimes spending six months without significant progress. Often, a different approach to using these tools could have led to a more successful outcome. This knowledge aims to prevent students from experiencing the setbacks of pursuing ineffective strategies for extended periods.
Contributing to Mindect
Mindect is in the very initial stages now and any contributions will be greatly valued. To contribute you can click here or visit https://github.com/gitstar-oc/mindect.
Setup
The most widely used tool by machine learning and data science practitioners today is the Jupyter Notebook. This is the default environments that a lot of skilled data scientist and analyist use to code and experiment and try things out. You can learn more about installing it here.