The world is becoming data driven. And the simple truth is that you are either in or out. The application of data and machine learning has proven successful in several sectors ranging from data security to health care to financial trading to fraud detection to recommendations, online search, natural language processing to smart cars (Self Driving cars).

These technologies have come to stay and there is room for everyone. Despite your domain in Electrical Engineering, mechanical Engineering, Medicine, Physics, Law, mathematics, Statistics, to mention a few, this is the time for you to take advantage of this opportunity and learn something.

Now the big challenge becomes “How do I get started”?

Before I talk about “How to get Started”, I would give an overview of Machine Learning to kinda make you understand it and hopefully get you interested.

Machine learning is simply the study of teaching a computer program or algorithm how to progressively improve upon a set task that it is given.

On the research-side of things, machine learning can be viewed through the lens of theoretical and mathematical modeling of how this process works. However, more practically it is the study of how to build applications that exhibit this iterative improvement.

Machine Learning is largely classified into 3 categories:

1. Supervised Machine Learning (Task Driven):
It is the easiest to understand and the simplest to implement.

Input and output data is provided to the computer (known as features and labels), the computer learns from it based on the input and its corresponding output, and it creates and algorithm which can perform similar tasks such that it can predict the output of an unknown input.
Successful applications of Supervised Learning are in the areas of

Face Recognition
Spam Detection
Breast Cancer Detection
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2. UNSUPERVISED LEARNING

Unsupervised learning is very much the opposite of supervised learning. It features no labels. Instead, our algorithm would be fed a lot of data and given the tools to understand the properties of the data. From there, it can learn to group, cluster, and/or organize the data in a way such that a human (or other intelligent algorithm) can come in and make sense of the newly organized data.

What makes unsupervised learning such an interesting area is that an overwhelming majority of data in this world is unlabeled.

Successful applications of Unsupervised ML are:

Recommender Systems: Example is used by Youtube or Netflix to recommend movies for you based on the ones you’ve already watched.

Grouping User Logs: This can help companies identify central themes to issues their customers face and rectify these issues, through improving a product or designing an FAQ to handle common issues.

Buying Habits: It is likely that your buying habits are contained in a database somewhere and that data is being bought and sold actively at this time.

3. Reinforcement Learning:
Reinforcement learning is fairly different when compared to supervised and unsupervised learning.
I prefer to look at reinforcement learning as learning from mistakes. Place a reinforcement learning algorithm into any environment and it will make a lot of mistakes in the beginning. So long as we provide some sort of signal to the algorithm that associates good behaviors with a positive signal and bad behaviors with a negative one, we can reinforce our algorithm to prefer good behaviors over bad ones. Over time, our learning algorithm learns to make less mistakes than it used to.

Application of Reinforcement Learning
Video Games
Industrial Simulation
Resource Management

Check out more on my thread:
https://www.nairaland.com/4945611/thread-30-days-tutorial-python

So let’s talk about how you can learn, and trust me, it is not as difficult as it seems.

[b]Linear Algebra: [/b]Some people would say that Math is not really necessary for machine learning, and I understand their view. But honestly, if you wanna take understand how it works, it is important to understand some topics in Linear Algebra. Not indepth knowledge, but these topics should be at your fingertips:

1. Linear equations
2. Scalars
3. Matrices
4. Vectors
5. Graphing linear equations
6. Polynomials
7. Quadratic Equation
8. Eigen-decomposition, Eigenvalues and Eigenvectors

Hello Paapii,

This is a great opportunity and prospect. I am a BA for the science team at my job.

Well done Sir.

Paapii3d:
The world is becoming data driven. And the simple truth is that you are either in or out. The application of data and machine learning has proven successful in several sectors ranging from data security to health care to financial trading to fraud detection to recommendations, online search, natural language processing to smart cars (Self Driving cars).

These technologies have come to stay and there is room for everyone. Despite your domain in Electrical Engineering, mechanical Engineering, Medicine, Physics, Law, mathematics, Statistics, to mention a few, this is the time for you to take advantage of this opportunity and learn something.

Now the big challenge becomes “How do I get started”?

Before I talk about “How to get Started”, I would give an overview of Machine Learning to kinda make you understand it and hopefully get you interested.

Machine learning is simply the study of teaching a computer program or algorithm how to progressively improve upon a set task that it is given.

On the research-side of things, machine learning can be viewed through the lens of theoretical and mathematical modeling of how this process works. However, more practically it is the study of how to build applications that exhibit this iterative improvement.

Need to get ur contact bro

Paapii3d:
The world is becoming data driven. And the simple truth is that you are either in or out. The application of data and machine learning has proven successful in several sectors ranging from data security to health care to financial trading to fraud detection to recommendations, online search, natural language processing to smart cars (Self Driving cars).

These technologies have come to stay and there is room for everyone. Despite your domain in Electrical Engineering, mechanical Engineering, Medicine, Physics, Law, mathematics, Statistics, to mention a few, this is the time for you to take advantage of this opportunity and learn something.

Now the big challenge becomes “How do I get started”?

Before I talk about “How to get Started”, I would give an overview of Machine Learning to kinda make you understand it and hopefully get you interested.

Machine learning is simply the study of teaching a computer program or algorithm how to progressively improve upon a set task that it is given.

On the research-side of things, machine learning can be viewed through the lens of theoretical and mathematical modeling of how this process works. However, more practically it is the study of how to build applications that exhibit this iterative improvement.

My WhatsApp 07016543752. Pls chat me up

Paapii3d:
So let’s talk about how you can learn, and trust me, it is not as difficult as it seems.

[b]Linear Algebra: [/b]Some people would say that Math is not really necessary for machine learning, and I understand their view. But honestly, if you wanna take understand how it works, it is important to understand some topics in Linear Algebra. Not indepth knowledge, but these topics should be at your fingertips:

1. Linear equations
2. Scalars
3. Matrices
4. Vectors
5. Graphing linear equations
6. Polynomials
7. Quadratic Equation
8. Eigen-decomposition, Eigenvalues and Eigenvectors

I need to speak with you. 07037706852

This is a very helpful ethical hacking video. I recommend you watch, practice and ask me questions. I will be more than willing to assist


https://www.youtube.com/watch?v=WqMB7tTAT80

https://www.youtube.com/watch?v=5IuFxjy_rws&t=9s