Artificial Intelligence Full Course 2024 | AI Tutorial For Beginners | AI | Simplilearn

Introduction

Welcome to the complete AI full course by Simplilearn, where we will explore the fascinating world of artificial intelligence (AI) that is transforming how we live and work every day. Whether you are new to AI or already have some knowledge, this course is designed for everyone. We will explain AI concepts in simple terms, starting with the basics and progressing to advanced topics like machine learning, neural networks, and deep learning.

Course Overview

You will see how AI is used in real-life situations, solving problems and creating new opportunities in industries ranging from tech to healthcare. With the growing demand for AI in 2024, learning these skills will open doors to many high-paying career opportunities. By the end of this course, you will have a strong understanding of AI and be ready to apply these skills to your projects.

Quick Information on AI Careers

If you're looking to enhance your career in AI, check out Simplilearn's postgraduate program in machine learning and AI in collaboration with Purdue University and IBM. This program is ideal for aspiring AI enthusiasts and professionals looking to switch careers. It focuses on expertise in generative AI, prompt engineering, explainable AI, machine learning algorithms, and many more.

Historical Context of AI

Let's commence our journey with a brief history of artificial intelligence. The term "artificial intelligence" was coined by John McCarthy in 1956, and he hosted the first AI conference that year. By 1969, the first general-purpose mobile robot, Shakey, was built. In 1997, IBM's Deep Blue supercomputer defeated the world chess champion, marking a significant milestone in AI, showcasing a computer using logic to beat a human in a complex game.

From 2002 to today, we have witnessed advancements such as the first commercially successful robotic vacuum cleaner and improvements in speech recognition. AI is now embedded in various aspects of daily life, including smart homes powered by AI technologies.

Understanding AI

So, what is artificial intelligence? AI is a form of computer science used to create intelligent machines that can recognize human speech and objects, learn, plan, and solve problems like humans. At its core, AI's primary function is to solve problems effectively—much like humans do.

Types of Artificial Intelligence

1. Purely Reactive: These machines do not store past experiences but react to present inputs. An example would be AI that plays chess, making moves based purely on the current state of the board.
2. Limited Memory: These AI systems can look into past data to inform their decisions but with restrictions on how much they can rely on that previous information.
3. Theory of Mind: This type is still in development. It aims to understand emotions, thoughts, and social interactions.
4. Self-Aware AI: This remains a theoretical concept. Machines would be capable of having their consciousness, reflecting on their self-state.

Applications of AI

AI has penetrated various sectors such as:

• Banking: For fraud detection by analyzing large transaction datasets.
• Online Customer Support: Most of today's customer support services are automated with AI.
• Cybersecurity: AI helps detect anomalies using machine learning algorithms.

AI Careers and Skills

Is AI a good career choice? Absolutely! The demand for AI professionals has been soaring, with job growth up to 32% in recent years and salaries exceeding $ 100,000 per annum for various roles such as:

• Machine Learning Engineer
• Data Scientist
• Business Intelligence Developer

To secure a position in AI, one needs a solid understanding of machine learning concepts, programming skills (Python, Java, etc.), and familiarity with tools like TensorFlow and PyTorch.

Learning Path to Become an AI Engineer

To become an AI engineer, follow an eight-month learning plan focusing on:

• Month 1: Fundamentals of computer science and Python.
• Month 2: Data structures and algorithms in advanced Python.
• Month 3: Version control and SQL for data manipulation.
• Month 4: Mathematics and statistics essential for AI.
• Month 5: Exploratory data analysis and hands-on machine learning projects.
• Month 6: MLOps and real-world machine learning projects.
• Month 7: Deep learning foundations with practical applications.
• Month 8: Specialization in either NLP or computer vision.

Deep Learning Fundamentals

Deep learning is a subset of machine learning that utilizes neural networks with multiple layers. This allows for the automatic extraction of features from data, making it powerful for tasks involving unstructured data like images, text, and sounds. Understanding various activation functions, loss functions, optimization techniques, and regularization methods is crucial for implementing deep learning models effectively.

Final Thoughts

Learning about AI, neural networks, and machine learning opens up the doors to exciting new opportunities in technology. The advancements in AI, deep learning, and data science can help you build a rewarding career, irrespective of your educational background.

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Keywords

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Generative AI
• Neural Networks
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Data Science
• MLOps
• Computer Vision
• Natural Language Processing
• TensorFlow
• Keras
• Confusion Matrix
• Batch Normalization

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FAQ

1. What is Artificial Intelligence?

   • AI simulates human intelligence to perform tasks such as reasoning, problem-solving, and learning.

2. What is the difference between supervised and unsupervised learning?

   • Supervised learning uses labeled data whereas unsupervised learning does not.

3. What are the types of Artificial Intelligence?

   • Types include purely reactive, limited memory, theory of mind, and self-aware AI.

4. What skills are needed to work in AI?

   • Programming skills (Python, Java), understanding of data structures, and familiarity with machine learning frameworks.

5. How does reinforcement learning work?

   • It teaches an agent to perform actions in an environment based on rewards and penalties.

6. What are confusion matrices used for?

   • They measure the performance of a classification model by showing true vs predicted classifications.

7. What is deep learning?

   • A subset of machine learning that uses neural networks with multiple layers for feature extraction from unstructured data.