Why this technology is relevant and what the scope.
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After following this resource what you will learn.
Summary of what learn to get started.
Details about the technology and learning materials.
Machine Learning is creating or learning models from input data and produce predictions on never before seen data.
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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 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.
<aside> 💡 Note : You don’t have to use machine learning everywhere!
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One use machine learning or AI to build softwares that do human level tasks that's hard to write as code.
Supervised Learning is Machine Learning with Labelled Dataset. Labelled dataset will have features and labels in the dataset.
In the below image, images of ducks, rabbit and human are features.
In the image below, Duck and Not duck are labels.
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.
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.
In unsupervised learning, model will learn the underlying pattern from the dataset without manual supervision.
The dataset is fed to the algorithm without any labels. Algorithm will automatically find patterns and divide dataset into different different categories.
We input a data and the model will add the data to corresponding group/cluster based on the pattern learnt
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.
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The environment is the world that the agent lives in and interacts with. For example the environment of flappy bird.
Agent is the actor in the environment. For example, the bird in flappy bird.
Agent apply an action to the environment (for example up arrow at a particular frame in flappy bird) and move to a new state from the current state.
A reward is given to the agent, which indicate how good the new state is. The goal of the agent is to maximise its cumulative reward.
Reinforcement learning methods are ways that the agent can learn behaviors to achieve its goal.
https://www.youtube.com/watch?v=kopoLzvh5jY
<aside> 👶 Level: Beginner
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<aside> 💼 Career Path: ‣
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A Neural Network in 11 lines of Python (Part 1)