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Artificial Intelligence (AI)

The Artificial Intelligence course covers the definition and history of AI, exploring its evolution and impact. You'll learn about the different types of AI, including Narrow AI and General AI, and understand the distinctions between AI, Machine Learning, and Deep Learning. The course also delves into various real-world applications of AI, demonstrating its transformative potential across industries.

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Artificial Intelligence (AI) Course Overview

Chapter 1: Introduction To Artificial Intelligence

What is AI?
  • Definition and history of AI
  • Types of AI: Narrow AI vs. General AI
  • AI vs. Machine Learning vs. Deep Learning
  • Real-world applications of AI

Ai Tools And Technologies

  • Overview of popular AI tools and frameworks (TensorFlow, PyTorch, etc.)
  • Introduction to AI platforms (Google AI, IBM Watson, Microsoft Azure AI)

Ai In Everyday Life

  • Real-world applications
  • Case studies

Ai In Business And Industry

  • Use cases in various sectors (e.g., healthcare, finance, retail)

Ai Technologies Overview

  • Machine Learning,
  • Deep Learning,
  • Natural Language Processing

Chapter 2: Programming For Ai

Python Programming

  • Introduction to Python
  • Installing Python and Jupyter Notebook / PyCharm
  • Basic Python programming concepts
  • Variables, data types, control structures, functions
  • Libraries for AI: NumPy, Pandas, Matplotlib

Advance Python And Data Handling

  • Object-oriented programming
  • Data preprocessing and manipulation

Chapter 3: Machine Learning Concepts In Ai

Understanding Machine Learning

  • Definition and types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
  • Key concepts: Training, testing, and validation datasets

Data Handling And Preprocessing

  • Importing datasets with pandas
  • Data cleaning and preprocessing
  • Feature selection and engineering

Introduction To Scikit-Learn

  • Overview of Scikit-Learn
  • Building a simple machine-learning model
  • Linear Regression
  • Classification with k-Nearest Neighbors (k-NN)

Chapter 4: Deep Learning In Ai

Introduction To Neural Networks

  • Basic concepts of neural networks
  • Perceptron and multi-layer perceptrons

Introduction To Tensorflow And Keras

  • Overview of TensorFlow and Keras
  • Building a simple neural network with Keras

Image Classification Task

  • Loading image data
  • Building and training a Convolutional Neural Network (CNN)
  • Evaluating the model

Chapter 5: Natural Language Processing (Nlp)

Introduction To Nlp

  • Basic concepts of NLP
  • Common NLP tasks (sentiment analysis, text, classification, etc)

Text Processing With Nltk And Spacy

  • Tokenization, stemming, lemmatization
  • Named Entity Recognition (NER)

Building A Simple Nlp Model

  • Sentiment analysis using Naive Bayes Classifier
  • Text classification with Scikit-Learn

Chapter 6: Translate Content, Analyze Text, And Label Video With Ai Tools And Stremlit App

  • Text-to-speech generation
  • Create natural-sounding, synthetic speech as playable audio
  • Text-to-Speech API converts arbitrary strings, words, and sentences into the sound
  • Label and analyze video with AI
  • Detect objects and actions in stored and streaming video

Chapter 7: Learning Computer Vision With Tensorflow

  • Build powerful multi-class image classifiers
  • Build a neural feature extractor that can embed images into a dense and rich vector space.
  • Perform fine-tuning optimization on new predictive tasks using pre-trained neural networks
  • Optimize a neural network with stochastic gradient descent and other advanced optimization methods
  • Build functional model classes and methods with TensorFlow-Keras' Functional API
  • Choose the right loss function and evaluation metric for the right task
  • Build a computational graph representation of a Neural Network
  • Train a neural network with automatic back propagation

Chapter 8: Project

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