How to Learn Artificial Intelligence: Steps to Get Started

Artificial Intelligence (AI) is used in hospitals to detect diseases, in online shopping to recommend products, and in apps like Google Maps to plan the best route. AI makes tasks easier, faster, and smarter. Learning AI can help you find exciting job opportunities in many fields.

AI will be 10X bigger than the Internet. Imagine how much the Internet changed our lives—AI will have an even bigger impact. AI is what makes apps, devices, and systems smarter.

Some key areas of AI include:

• Machine Learning (ML)
• Deep Learning
• Natural Language Processing (NLP)
• Robotics

Why Should You Learn AI?

• Good Salaries: AI jobs pay well and are in demand.
• Many Career Choices: You can work in healthcare, finance, gaming, education, and more.
• Be Part of the Future: AI is shaping how we work, live, and communicate.

Stay ahead in data science, and artificial intelligence with our latest AI news covering real-time breakthroughs and innovations.

How to Learn Artificial Intelligence

Artificial Intelligence (AI) is growing fast, with over 35% of companies already using it and 91% planning to use it soon. Learning AI can open up big opportunities, and starting with the basics makes it easier. Here’s how to get started:

Step 1: Understand the Fundamentals of AI

Before learning how to code or build AI projects, it’s important to understand what AI is and how is it revolutionizing business operations. This helps you build a strong foundation.

Key AI Concepts

• Machine Learning (ML): This allows computers to learn from data and improve without extra programming.
  • Example: Netflix recommends shows based on what you’ve watched.
• Neural Networks: These are inspired by the human brain and help AI recognize patterns.
  • Example: Neural networks are used in facial recognition on smartphones.
• Deep Learning: A more advanced part of ML that handles tasks like self-driving cars and voice assistants like Alexa.
• Supervised vs. Unsupervised Learning:
  • Supervised: AI learns from labeled data, like predicting house prices based on previous sales.
  • Unsupervised: AI finds patterns in unlabeled data, like grouping similar customers for marketing.

Basic AI Terms

• Algorithms: These are step-by-step instructions that help AI solve problems.
  • Example: Google’s search engine uses algorithms to rank web pages.
• Datasets: Collections of data used to train AI systems.
  • Example: AI for self-driving cars uses datasets of road images.
• AI Ethics: Guidelines to make sure AI is used fairly and safely.
  • Example: AI used in hiring should not discriminate against gender or race.

Resources to Get Started

• Online Learning Platforms: Platforms like upGrad offer beginner-friendly courses to help you start learning AI.

Step 2: Choose a Programming Language

Programming is essential for AI. Choosing the right language makes it easier to start and work on projects. About 70% of AI developers use Python because it’s simple and powerful.

Why Python is the Best Choice for AI

• Easy to Learn: Python has simple commands and is great for beginners. You can focus on learning AI concepts instead of complicated coding.
  • Example: Building a chatbot in Python takes fewer lines of code compared to other languages.
• Libraries and Tools: Python has many pre-built tools to make AI development easier:
  • NumPy: Helps with mathematical calculations.
  • Pandas: Helps organize and analyze data.
  • TensorFlow: Helps build advanced AI models like image recognition.
• Strong Community Support: Python has a large online community. If you get stuck, there are many tutorials, forums, and examples to help you out.

Other Languages You Can Use

While Python is the most popular, other languages are good for specific purposes:

• R: Great for data analysis and creating charts.
  • Example: Used in healthcare to analyze patient data.
• Java: Used for large-scale applications like banking systems.
  • Example: Banks use Java for fraud detection models.
• C++: Perfect for high-performance tasks like robotics or gaming.
  • Example: Self-driving cars often rely on C++ for speed and precision.

How to Start

• Learn Python basics with free online tutorials.
• Try simple AI projects using Python libraries like TensorFlow.
• Experiment with other languages if needed for specific projects.

Step 3: Explore AI Learning Resources

Once you understand the basics of AI, the next step is to find the right resources. Learning from trusted sources will give you the skills and confidence to move forward.

Free Online Courses

You can start learning AI without spending money. upGrad offers free resources that make it easy for beginners to understand the basics of AI and Machine Learning:

• upGrad Free Courses: upGrad provides free learning materials on topics like data science, machine learning, and AI. These courses give you a strong foundation while working at your own pace.
  • Example: upGrad AI Tutorials cover topics like Python for AI, machine learning basics, and data analysis.
• YouTube: upGrad’s YouTube channel includes AI tutorials, coding walkthroughs, and expert sessions to guide you step-by-step through AI concepts.

Recommended Books

Books are a great way to build your theoretical knowledge:

• Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig: A comprehensive guide to AI fundamentals.
• Python Machine Learning by Sebastian Raschka: Focuses on practical applications using Python.

Paid Resources

If you’re looking for more structured programs, paid resources can provide certifications and hands-on experience:

• upGrad: Offers certifications like Machine Learning and AI Specializations.
• Google AI Certification: Learn directly from Google experts with practical AI-focused courses.

Step 4: Work on AI Projects

Hands-on projects are the best way to learn AI. They help you apply concepts, build confidence, and create a portfolio to showcase your skills.

Beginner Projects

Start small with simple, practical projects:

• Chatbots: Use Python or tools like Dialogflow to create a chatbot for answering basic questions.
  • Example: A bot that answers FAQs for a small business.
• Sentiment Analysis: Analyze text to determine if it’s positive, negative, or neutral.
  • Example: Check product reviews to see if customers are happy.
• Basic Image Classification: Use TensorFlow or PyTorch to classify images.
  • Example: Train an AI to recognize cats and dogs from photos.

Advanced Applications

Once you’ve mastered the basics, move on to more complex projects:

• Recommendation Systems: Build a system that suggests products or movies based on user preferences.
  • Example: Similar to Netflix or Amazon’s recommendation engine.
• Fraud Detection: Create models to identify suspicious activities in banking or e-commerce.
  • Example: Detect unusual credit card transactions.
• Self-Driving Car Simulations: Use tools like OpenCV to simulate how AI can navigate roads.
  • Example: A virtual car that can identify stop signs and lanes.

Step 5: Learn About AI Frameworks and Tools

Once you understand AI concepts well, it’s time to learn the frameworks and tools that bring AI ideas to life. These frameworks help you build and train AI models more efficiently.

Frameworks to Learn

• TensorFlow: A powerful open-source library for creating machine learning and deep learning models.
  • Example: Use TensorFlow to build image recognition systems or chatbots.
• PyTorch: Another popular framework, known for its flexibility and ease of debugging.
  • Example: Ideal for building and testing custom neural networks.
• Scikit-learn: A beginner-friendly library for machine learning algorithms like regression and clustering.
  • Example: Use Scikit-learn to predict sales trends or customer behavior.

Specialized Tools

• OpenCV: A library focused on computer vision tasks like object detection, face recognition, and video analysis.
  • Example: Create an AI model that identifies traffic signs in images.
• NLTK (Natural Language Toolkit): A library for processing and analyzing human language.
  • Example: Build an AI that analyzes customer reviews to identify common complaints.

Step 6: Build a Portfolio

Your portfolio is a showcase of your skills and projects. A strong portfolio can help you stand out to employers and clients.

Upload Projects to GitHub

• Share your AI projects on GitHub to show your coding and problem-solving skills.
  • Example: Upload a machine learning project that predicts house prices or classifies images.
• Add clear descriptions to your projects so others can understand your approach and results.

Create a LinkedIn Profile

• Highlight your AI skills, certifications, and projects on LinkedIn.
  • Example: Share a certification from upGrad and include links to your GitHub projects.
• Join LinkedIn AI groups to connect with professionals in the field and stay updated on trends.

Build a Personal Website

• Create a simple website or portfolio where you display your projects, skills, and certifications.
  • Example: Include a section that explains the AI problems you’ve solved and the tools you used.

Prerequisites for Learning Artificial Intelligence

Before starting with AI, there are a few skills that will make your learning easier. Don’t worry if you don’t know them yet—you can pick them up along the way.

1. Mathematics

AI involves some math, but it’s not as scary as it sounds. You’ll need a basic understanding of:

• Linear algebra: Things like vectors and matrices, which are used in machine learning.
• Probability: Useful for making predictions and handling uncertainty in data.
• Calculus: Helps when working with algorithms and training AI models.

2. Programming Basics

You don’t have to be a coding expert, but you should know the basics. This includes:

• Writing simple programs.
• Using loops, functions, and variables.
• Debugging errors in code.

3. Algorithms and Data Structures

AI depends on smart ways to process and organize data. It’s good to know:

• How to sort and search through data (e.g., binary search).
• Optimization techniques for improving results.
• Basic structures like arrays, trees, and linked lists.

4. Critical Thinking

AI is all about solving problems, so thinking logically is key. This means:

• Breaking big problems into smaller, simpler steps.
• Finding creative solutions when things don’t work as expected.

How to Build an AI Learning Path Based on Your Goals

Why is AI the Next Big Thing?

AI is changing the world, and it’s just getting started. It’s already in your daily life. Your phone unlocks with face recognition. Netflix knows what you want to watch next. Even your online shopping cart uses AI to recommend products.

AI is also solving bigger problems:

• In healthcare, AI scans medical images faster than doctors and catches early signs of diseases.
• In transportation, AI powers self-driving cars, making roads safer.
• In education, it personalizes lessons for students, helping them learn at their own pace.

What makes AI the future is its ability to think, adapt, and make decisions. Businesses, from startups to global companies, are racing to use AI because it creates smarter, faster solutions.

Whether it’s building chatbots, designing smart systems, or solving real-world challenges. The best part? The demand for AI professionals is only growing, and now is the perfect time to start. 

How upGrad Can Help You Learn Artificial Intelligence

AI is the future, and upGrad is here to help you master it with programs designed to meet industry demands. 

Whether you’re a beginner or looking to specialize, upGrad has the perfect course for you.

Industry-Relevant Certifications

Gain certifications that are recognized by top employers. Learn from industry leaders and prestigious universities to stay ahead in your AI career:

• Generative AI for Business Leaders – IIIT Bangalore: Master how AI transforms business strategies and operations.
• Advanced Certificate Program in Generative AI: Dive into cutting-edge AI applications like ChatGPT and image generation.

Comprehensive AI Curriculum

upGrad offers a step-by-step learning path, from foundational skills to advanced AI topics, ensuring you’re job-ready:

• Python Bootcamp: Start with Python and build coding confidence.
• Executive Diploma in Machine Learning and AI: Learn machine learning, deep learning, and data analytics in one comprehensive program.
• PGC in Machine Learning and NLP – IIIT Bangalore: Specialize in natural language processing, chatbots, and AI-driven text analysis.

Master AI today! Explore upGrad’s programs and realize your potential in Artificial Intelligence.

Expand your expertise with the best resources available. Browse the programs below to find your ideal fit in Best Machine Learning and AI Courses Online.

Best Machine Learning and AI Courses Online

Discover in-demand Machine Learning skills to expand your expertise. Explore the programs below to find the perfect fit for your goals.

In-demand Machine Learning Skills

Discover popular AI and ML blogs and free courses to deepen your expertise. Explore the programs below to find your perfect fit.

Popular AI and ML Blogs & Free Courses

Source:

https://www.cnbc.com/2023/09/28/brad-gerstner-says-ai-is-going-to-be-bigger-than-the-internet-bigger-than-mobile.html

https://www.sciencedirect.com/science/article/pii/S2773207X24001386