AI Basics and Machine Learning

What is Artificial Intelligence?

Artificial intelligence (AI) is designed to enable computers to mimic human behavior and mind. It is evident in any machine or system that exhibits human-like behaviors. In the simplest form of AI, computers can be taught to mimic human behavior using data from previous instances of the activity. This includes everything from distinguishing between a cat and a bird to performing complex tasks in an industrial environment. AI allows machines to evaluate large volumes of data quickly and efficiently. They can find solutions using supervised, non-supervised, or reinforced learning. AI can accelerate work, eliminate human errors, and perform many other tasks.

What is Vertical AI?

Vertical AI service controls the workflow and creates an AI-based solution to meet a particular consumer need. This involves solving problems for a certain type of client and being part of a specific sector.

What is Horizontal AI/Hybrid AI?

Horizontal AI is a science-oriented service. Academics often work on a fundamental issue while helping a wide range of industries. Apple's Siri and Amazon's Alexa are examples. These services are usually developed as part of funded research projects for academic institutions, military organizations, and businesses that innovate with fundamental sciences.

What is Machine Learning?

Artificial intelligence is the collection of algorithms and information. Machine learning (ML) is one. Deep learning is another method used for machine learning. ML is a part of AI. The design and deployment of AI algorithmic learning systems that can learn from previous data and references.

AI Learning Prerequisites

Mathematics

• Linear Algebra and Differentials
• Multivariable Calculus and Coordinate Geometry
• Nonlinear Transformations
• Linear and Logistic Regressions
• Numerical Analysis

Probability & Statistics

• Discrete Math
• Hypothesis Tests
• Distribution Functions

Computer Science

• Theory
• Algorithms
• Coding
• Data Structures
• Computer Hardware Systems

Answering the Three Burning Questions

Who can learn AI or ML? Does the AI Program limit participants to IT Professionals only?

You can see data science and machine learning course are open to everyone.

For those who do not have an IT background but want to learn AI or Machine Learning?

The prerequisites are essential. You do not need an IT degree to master AI, but your foundation should be solid. Your interest in AI and approach to it will determine whether you pursue it as a profession.

Do you need to program/code to learn AI?

There are some excellent AI and machine learning courses, but you should learn Python as a primary programming language. You must know how to code to create an application and handle complex problems. AI isn't just a programming discipline. Its scope goes far beyond that.

Prerequisites to Learning ML

Mathematics and Numerical Analysis

• Linear Algebra
• Calculus
• Trigonometry

Probability and Statistics

• Probability Theory
• Statistical Inference

Programming Skills

• Python
• R
• Java

Data Analytics and Visualization Tools

• Data Exploration
• Data Manipulation (Excel, SQL)
• Data Modeling (Predictive Statistics, Decision Models)

Data Tackling

The Ultimate Goal of Machine Learning

Experience in handling large volumes of data is crucial. The most critical part lies in data wrangling, which contributes to generating "optimized-precise-accurate" actionable insights.

The Machine Learning Model is Built on Three Pillars

• Intention
• Invention
• Adaptation

Key Areas of Expertise

• Data Patterns and Trends
• Improve Decision-Making Parameters/Factors
• Solve Real-Time Complex Problems
• Representation: How to Formulate the Entire Analysis
• Evaluation: How to Identify Critical Differentiators
• Optimization: How Efficiently the Data is Processed

Learn AI Programming

Popular AI Programming Languages

• Python: The most popular AI programming language, suitable for beginners due to its simplicity and consistency of syntax.
• Other Languages: Java, JS (JavaScript), C++, Julia, Haskell, Prolog, Scala, Rust.

Why learn AI Programming amid No-Code hype?

AI applications require a combination of computer science, mathematics, and dynamic programming in design. Learning AI programming and MS in Computer Science allows you to create solutions that closely mimic human intelligence and perform tasks efficiently.

How to learn AI Programming now?

• Free Courses: Kaggle and Google AI offer excellent free courses for beginners.
• Full-Stack Courses: Great Learning Help you learn artificial intelligence programs comprehensively.

How difficult is it to learn AI?

AI is challenging due to its complex curriculum, requiring knowledge of various fields. However, it is manageable with the right approach and resources. The key challenges include rigid programming, the necessity of handling data, and understanding the vast scope of AI.

Conclusion

Artificial intelligence and machine learning are two of the most popular topics today. Over the past ten years, these technologies have evolved and developed to be a part of our everyday lives. From 2022 to 2030, the global artificial intelligence market is expected to grow with an annual CAGR of 38.1%. AI's increasing popularity and everyday use have made it an attractive career option for IT professionals, students, and anyone interested in advanced technology. Learning AI and machine learning step-by-step with a solid foundation in prerequisites will help you master advanced concepts and pursue a rewarding career in this exciting field.