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vanshika1807
vanshika1807

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Understanding the Big Picture: AI to DL

#machinelearning #datascience #ai #deeplearning

Suppose there is a Universe & we call it AI.

The ultimate goal of many AI projects is to build intelligent AI-powered applications.

AI refers to systems that can do its task on its own with little to no human intervention.

Example -

  1. Recommendation System
  2. Self Driving Car
  3. Amazon -> If you buy something, then other similar or useful things are automatically recommended

  • Machine Learning is a subset of AI. It provides us the stats tool to analyze, visualize data & then it will be helpful in forecasting or prediction of new data.

  • DeepLearning is a subset of ML.

How can we teach ML to minimize human efforts?

We use multi layered Neural Network for DL! We train our ML algo!

Where does Data Scientist fits here??

They are the part of everything out of AI/ML/DL, they combine domain knowledge & uses Probability/Statistics/Linear ALegbra/Linear Regression to collect data, analyze it, build predictive models, and solve real-world business problems.

The goal is to generate valuable insights and, in many cases, build intelligent AI-powered applications.

We solved & encounter only 2 type MAINLY

But lets talk about dataset first.

We usualy have dependent(input) & independent variables (target/output)

here, we are taking example of 2-D means 2 features

Taking example of Weight & Age

We will having a basic data set that we will using to train our model. So, We will make a ML algo that takes any unknown age and will gonna give a predicted the weight.

This is nothing but Hypothesis. We use Hypothesis to actually make that straight line.

Usually, We know about the output feature like in our case the output weight (dependent variable)

In supervised learning, we solves two types of problems: Classification & Approximation.

For more details, may be you can refer this article of mine on Classification & Approximation: Classification v/s Approximation

In Unsupevised Learning, the dataset that have no labeled output. Instead, the model tries to discover hidden patterns or groups within the data.

What we do is, we use the most common method called clustering & based on those cluster we build our models.

These clusters can then be used for customer segmentation, recommendation systems, anomaly detection, and many other real-world applications.

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