MODULE 1: Introduction to Artificial Intelligence and Machine Learning

ARTIFICIAL INTELLIGENCE

Techniques that equip computers to emulate human behavior, enabling to learn, make decisions, recognize patterns, and solve complex problems in a manner a kin to human intelligence

ARTIFICIAL INTELLIGENCE

when we make computers or machines think and act like humans.

ARTIFICIAL INTELLIGENCE

Simulation of human intelligence in machines

ARTIFICIAL INTELLIGENCE

It enables machines to perform tasks such as problem-solving, decision-making, and language understanding

KEY AREAS OF AI

1. Natural Language Processing (NLP),

2. Computer Vision,

3. Robotics,etc.

MACHINE LEARNING (subset of AI)

uses advanced algorithms to detect patterns in Large Data sets, allowing machines to learn and adapt.

MACHINE LEARNING (subset of AI)

 Algorithms use supervised or unsupervised learning methods

MACHINE LEARNING (subset of AI)

Focuses on teaching computers to learn from data.

MACHINE LEARNING (subset of AI)

Machines improve their performance on tasks through experience without being explicitly programmed

MACHINE LEARNING (TECHNIQUES)

1. supervised

2. Classification

3. Regression

SUPERVISED

The algorithm is trained to classify data or make predictions base on known input and output data

SUPERVISED

Is a type of ML where a model is trained using labeled dataset

LABELED DATA

 Refers to the entire dataset where each example (input data) is paired with its corresponding label (output) in a CLASSIFICATION TASK, the Label is a category or class

CLASSIFICATION

Helps to divide input data into different classes using both input and output

REGRESSION

Explains or predicts a specific numerical value by analyzing past data for similar properties

UNSUPERVISED

The algorithm discovers hidden patterns by analyzing unlabeled and unstructured

DEEP LEARNING (subset f ML)

which uses neural networks for in-depth data processing and analytical tasks.

DEEP LEARNING (subset f ML)

leverages multiple layers of artificial neural networks to extract high-level features from raw input data, simulating the way human brains perceive and understand the world

GENERATIVE AI (subset of DL)

models that generates content like text, images. Or code based on provided input.

GENERATIVE AI (subset of DL)

Trained on vast data sets, these models detect patterns and create outputs without explicit instruction, using a mix of supervised and unsupervised learning.

AI

broader concept involving intelligent systems that can mimic human behavior.

ML

A method used in AI for enabling systems to learn from data

WHY is AI and ML Important?

• High demand for AI and ML skills across industries.

• Potential for innovation in various fields.

• Contribution to societal advancement

CLUSTERING

Explores and analyzes the input data to find patterns or groups in it and classifies those data points into specific clusters