Mastering Artificial Intelligence: Learn Fundamentals Ai

Practical AI with Python – Master AI Fundamentals, NLP & Computer Vision with Real-World Projects

What you'll learn

Understand the fundamentals of AI, its history, and key subfields such as NLP, Computer Vision, and Generative AI.

Learn the key differences between AI, Machine Learning, and Deep Learning, and when to use each approach.

Work with essential AI tools and Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.

Apply AI techniques in Natural Language Processing (NLP), including text classification, word embeddings, and Named Entity Recognition (NER).

Implement Computer Vision applications using OpenCV, including image processing, object recognition, and feature extraction.

Explore Search Algorithms & Optimization, including A* search and gradient descent techniques.

Build AI-driven applications using Generative AI models, including Autoencoders and AI-generated text.

Develop hands-on AI projects, such as a text classification model, an image classification system, and an AI-powered search algorithm.

Requirements

Basic programming experience in Python (knowledge of NumPy, Pandas, and Matplotlib is helpful but not required).

Understanding of fundamental Machine Learning concepts will be beneficial but is not mandatory.

Enthusiasm to learn Artificial Intelligence through hands-on coding and real-world projects.

Description

Master Artificial Intelligence with Hands-On Projects and Real-World ApplicationsArtificial Intelligence is transforming industries and shaping the future of technology. This course provides a comprehensive introduction to AI, covering key concepts, tools, and techniques needed to build intelligent systems.This course is designed for beginners and professionals looking to enhance their skills in AI, Machine Learning, and Deep Learning. Through step-by-step explanations and hands-on projects, learners will gain a strong foundation in AI applications.What You’ll Learn in This Course:AI Fundamentals – Understand AI concepts, applications, and history.Machine Learning & AI – Learn how AI systems make predictions and process data.Natural Language Processing (NLP) – Work with text-processing techniques like tokenization, sentiment analysis, and text classification.Computer Vision – Implement image processing and object recognition models.AI Project Development – Build AI-powered applications such as chatbots, AI assistants, and intelligent systems.Hands-On Learning – Use Python, TensorFlow, OpenCV, and Scikit-learn to develop AI solutions.Who Should Take This Course?Beginners interested in learning AI from scratch.Software developers and engineers who want to integrate AI into their work.Data analysts and aspiring AI professionals looking to build AI models.Entrepreneurs and business professionals who want to understand AI applications.No prior AI experience is required. The course provides step-by-step instructions to help learners gain practical AI skills.Why Take This Course?Structured, beginner-friendly approach – Covers AI fundamentals without overwhelming technical details.Real-world AI projects – Hands-on exercises to apply concepts in practical scenarios.Industry-standard tools – Learn AI using Python, TensorFlow, and OpenCV.Lifetime access and updates – Study at your own pace with continuous improvements to the course.By the end of this course, learners will have a solid understanding of AI fundamentals and the confidence to build AI-powered applications.

Overview

Section 1: Introduction to Artificial Intelligence

Lecture 1 Course Overview

Lecture 2 What is Artificial Intelligence?

Lecture 3 AI vs. ML vs. DL

Lecture 4 Core Components of AI

Section 2: Foundations of Python for AI

Lecture 5 Essential Python Libraries for AI

Lecture 6 Data Management Techniques

Section 3: Natural Language Processing (NLP)

Lecture 7 NLP Fundamentals

Lecture 8 Working with Word Embeddings

Lecture 9 NLP Applications

Lecture 10 Project - Build a basic text classification model

Section 4: Computer Vision

Lecture 11 Basics of Image Processing

Lecture 12 Object Recognition and Feature Extraction

Lecture 13 Applications in Vision

Lecture 14 Project - Create a pipeline for image classification

Section 5: Search and Optimization in AI

Lecture 15 Fundamentals of Search

Lecture 16 Optimization Techniques

Lecture 17 Applications of Search in AI

Lecture 18 Project - Implement A* search

Section 6: Reinforcement Learning

Lecture 19 Introduction to Reinforcement Learning

Lecture 20 Q-Learning Basics

Section 7: Generative AI

Lecture 21 Introduction to Generative AI

Lecture 22 Working with Autoencoders

Lecture 23 Applications of Generative AI

Lecture 24 Project - Generate realistic text using an AI-based language model

Anyone looking to build practical AI projects, such as chatbots, recommendation systems, and AI-driven search engines.,Students and professionals with a background in Machine Learning or strong programming skills who want to dive into AI.,AI enthusiasts eager to explore Natural Language Processing (NLP), Computer Vision, Search Algorithms, and Generative AI.,Data analysts and data scientists looking to expand their expertise into AI-driven solutions.,Developers interested in integrating AI capabilities into their projects, such as NLP-powered applications or AI-enhanced automation.