Why?

Why this technology is relevant and what the scope.

Computers transformed the way we interact with things in our daily life. We have a software in almost everything we use, from toaster to our cars. We are witnessing an age where it is being taken to the next level by integrating AI to our daily life things and thereby solving a vast majority of problems we face in medical science to space exploration. AI is the next big thing.

AI will create 133 million jobs by 2025.

Take-Away Skills

After following this resource what you will learn.

• You will become AI literate.
• You will also learn various ML techniques and where each will be used.
• You will get learn how to start a career in Machine Learning.

Syllabus

Summary of what learn to get started.

• What is Machine Learning ?
• Difference between, AI, ML, data science
• Why Machine Learning ?
• Various ML Techniques
• Some Cool ML applications

The content

Details about the technology and learning materials.

What is Machine Learning ?

Machine Learning is creating or learning models from input data and produce predictions on never before seen data.

Training

Creating/learning of ML models is called training. We feed the input data and target output and run the algorithm in a loop till the algorithm learn the underlying patterns in the data. This takes comparatively more time and computation resources than Inference.

Inference

Inference means running trained model with never before seen data and making predictions. After training the model we give an input to a trained model and we get the desired output.

Why using Machine Learning ?

<aside> 💡 Note : You don’t have to use machine learning everywhere!

</aside>

One use machine learning or AI to build softwares that do human level tasks that's hard to write as code.

Machine Learning solve problems that normal code/software find difficult to do or impossible to do.

1. Finding earth like planets.
2. Self driving cars.

Generating Images, videos or other kind of data

1. AI generated faces.

Various ML techniques

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Supervised Learning

Supervised Learning is Machine Learning with Labelled Dataset. Labelled dataset will have features and labels in the dataset.

Features

In the below image, images of ducks, rabbit and human are features.

Labels

In the image below, Duck and Not duck are labels.

Training

In the training phase we feed the features and labels to the supervised learning algorithm and the algorithm will learn the patterns associated with the features and corresponding labels.

Inference

In inference phase we pass an input image to the trained predictive model and the model will output the corresponding label based on the patterns learnt.

Unsupervised Learning

In unsupervised learning, model will learn the underlying pattern from the dataset without manual supervision.

Training

The dataset is fed to the algorithm without any labels. Algorithm will automatically find patterns and divide dataset into different different categories.

Inference

We input a data and the model will add the data to corresponding group/cluster based on the pattern learnt

Reinforcement Learning

Agent learn the best action patterns for a purpose by doing several iterations of different actions in the environment and gaining rewards.

For example, RL can be used to train a model for playing flappy birds game.

Environment

The environment is the world that the agent lives in and interacts with. For example the environment of flappy bird.

Agent

Agent is the actor in the environment. For example, the bird in flappy bird.