Artificial intelligence is an excellent and expanding field in data science. AI is already part of our daily lives, and we can benefit from learning AI. Some AI applications, like self-driving cars, are still being developed, but others, like predictive analysis, are already here.
How to learn AI from scratch? You can start by learning AI and how to use AI tools to explore new possibilities in data analysis. AI is a versatile field with applications in all industries, which means that AI-related jobs are in high demand.
A McKinsey survey found that AI is used for various purposes, such as service optimization, product enhancement, data analysis, risk modeling, and fraud prevention. The demand for computer and information research jobs will grow by 22% by 2030.
While AI and AI tools can’t replace human intelligence, this fascinating branch of computer science can help us do much more. And if you want a career that is always in demand, you must learn AI techniques and how to use machine learning algorithms to your advantage!
What Is Artificial Intelligence?
Artificial intelligence is a branch of data science that makes computer programs that can copy human intelligence tasks. Artificial intelligence uses computer programming and large data sets to solve problems.
Learning AI involves machine learning, deep learning, and natural language processing, which allow computers to “learn” from experience and perform human-like tasks like data visualization or data manipulation, often better and quicker than humans. This type of artificial intelligence is called narrow or weak artificial intelligence.
It is when a computer does a specific task by detecting patterns in large data sets. Some examples of narrow artificial intelligence are streaming recommendations, chess bots, and smart speakers. Narrow artificial intelligence can adapt to inputs but can’t do anything outside its parameters.
But it is functional.
The Fourth Industrial Revolution and the digital-first approach of modern businesses create a lot of data that can fuel narrow artificial intelligence applications. Strong AI, also called artificial general intelligence (AGI), is the kind of artificial intelligence linked to robots in science fiction plots – the ones that beat or mimic human intelligence. This type of artificial intelligence will be around for a while, but developers are working to overcome the challenges of AGI, such as prediction and control models.
What are the Different Types of Artificial Intelligence?
AI is a popular and diverse technology that can be classified into three levels based on its abilities:
- Artificial Narrow Intelligence (ANI): This is the most common type of AI we use today. ANI is made to do a single task, like voice recognition or streaming recommendations.
- Artificial General Intelligence (AGI): An AI with AGI can understand, learn, adapt, and apply knowledge across many tasks at a human level. While large language models and tools like ChatGPT can do many jobs—as of 2023, this is still a theoretical idea.
- Artificial Super Intelligence (ASI): The final level of AI, ASI, refers to a future situation where AI exceeds human intelligence in almost all economically valuable work. This idea, while interesting, remains primarily speculative.
How Long Does it Take to Learn AI?
How long it takes to learn artificial intelligence depends on several factors, such as:
- Prerequisite knowledge: If you know math and statistics, you can learn AI skills and tools immediately.
- Career intent: If you want an AI job, you’ll need more education than someone who just wants to use AI for data analysis.
- Background knowledge: If you’re switching from another field, it’ll take longer to learn than someone who already works in technology and knows the complex terms.
How To Learn AI from Scratch? A Complete Roadmap
Here are four steps to guide your learning. To start your journey into AI, make a learning plan by assessing your current knowledge and the time and resources you have for education.
1. Make A Learning Plan.
Before you start a class, we suggest making a learning plan. This includes a timeline, skill goals, and the necessary activities, programs, and resources to gain those skills. First, ask yourself these questions:
- Your knowledge of artificial intelligence: Are you a beginner? Do you have math and statistical skills? Do you know basic terms and concepts?
- Your reason for learning: Do you want a new career or add to your current one?
- How much time can you learn? Do you have a job? Do you want to know full-time or part-time?
- How much money can you spend? Do you want to pay for a boot camp, online courses, or watch videos on YouTube and TikTok?
- How do you want to learn? Do you want to do a degree program, a boot camp, or self-teach through online courses? Later in this article, we’ll give an example of a learning plan to help you make yours.
2. Master The Prerequisite Skills.
Before you start learning AI, you need to have some basic skills. These skills will help you understand complex AI skills and tools.
- Basic statistics: AI skills are more accessible if you know statistics and data interpretation. You need to understand concepts like statistical significance, regression, distribution, and likelihood, which are used in AI applications.
- Basic math: To understand AI, especially for machine learning and deep learning, you need to know math concepts like calculus, probability, and linear algebra. These are used in AI algorithms and models.
- Curiosity and adaptability: AI is complex and fast-changing, so you need to keep learning new techniques and tools. If you want an AI career, you should have a strong desire for learning and a flexible mindset for problem-solving.
How much you need to learn these skills depends on your career goals. An AI engineer must master these, while a data analyst who wants to learn AI may start with an introductory class. If you know statistics and math and are willing to learn, you can attend Step 3.
3. Start Learning AI Skills.
After you learn the basics, you need some essential skills for AI. Your skill level will depend on the role you want.
You must code to make AI applications because you can create AI algorithms and models, work with data, and use AI programs. Python is one of the popular languages because it is simple and adaptable. R is another, and there are others, like Java and C++.
A data structure is a way to organize, store, get, and change data. You need to know the different types, like trees, lists, and arrays, to write code that can become complex AI algorithms and models.
Data science is using tools and algorithms to find patterns in raw data. Data scientists know the user and the process of getting insights from data. AI professionals need to understand data science to make suitable algorithms.
This standard part of AI makes many products and services today. Machines learn from data to make predictions and improve performance. AI professionals must know different algorithms, how they work, and when to use them.
Deep learning is a part of machine learning that uses many layers of neural networks to understand patterns in data. It is used in the most advanced AI applications, like self-driving cars.
4. Get Familiar With AI Tools And Programs.
You need to know how to use AI tools and programs, like libraries and frameworks, to help you learn AI. You should know which programming languages they use because many tools depend on the terminology used. Here are some popular tools and libraries for Python:
- NumPy: A library for working with arrays and matrices of numbers.
- Scikit-learn: A library for machine learning tasks, such as classification, regression, clustering, and dimensionality reduction.
- Pandas: A library for data analysis and manipulation, such as reading, filtering, grouping, and aggregating data.
- Tensorflow: A framework for building and training neural networks and other deep learning models.
- Seaborn: A library for data visualization, such as creating plots, histograms, and heat maps.
- Theano: A library for numerical computation, such as defining and evaluating mathematical expressions involving multidimensional arrays.
- Keras: A high-level API for building and training neural networks and other deep learning models.
- PyTorch: A framework for building and training neural networks and other deep learning models.
- Matplotlib: A library for creating static, animated, and interactive visualizations.
You can learn more AI tools to master yourself about Artificial Intelligence.
How To Develop A Learning Plan
Want to learn AI on your own and stay on track? Make a learning plan to show how and where to spend your time. Here is an example of a nine-month intensive learning plan, but your timeline may vary depending on your career goals.
Month 1-3: Learn math and statistics, programming, and data structures
- Math and statistics: Learn calculus, algebra, statistics, and probability, which are the basics for AI.
- Programming: Pick a programming language, like Python or R, to learn. You’ll get used to libraries and packages.
- Data structures: Learn how to store, get, and change datasets, and then how to clean and prepare them, which is needed for any AI project.
Month 4-6: Explore data science, machine learning, and deep learning
- Data science: Learn the basics of data science and how AI can help get and use insights from data.
- Machine learning: Learn the different types of machine learning algorithms, such as supervised, unsupervised, and reinforcement learning.
- Deep learning: Learn neural networks and the concepts of deep learning.
Month 7-9: Use AI tools and pick a specialization
- AI tools: After you learn the basics, you can use the different libraries for the programming language you learned and other AI tools like ChatGPT.
- Specialization: You should focus on a specific area of AI, such as natural language processing, or how to apply AI to another field.
- Further learning and job search: Start looking for jobs if that is your reason for education. Keep up with AI trends with blogs, podcasts, and more.
Learning AI is a worthwhile pursuit that leads to a world of new technologies and exciting career opportunities. The skills and knowledge you gain from this process are more than books and lectures. It involves a dynamic cycle of learning, applying, experimenting, and improving. Doing a hands-on approach, primarily through courses and AI projects, speeds up learning and builds essential skills in problem-solving, critical thinking, and creativity.
If you’re new to AI, we’ve listed many helpful resources to help you start and an example learning plan for some of the key topics you’ll need to master to become good at artificial intelligence. How to learn AI from scratch? You can start by reading this article.
- US Bureau of Labor Statistics. “Computer and Information Research Scientists,
- Artificial intelligence
- The state of AI in 2022—and a half decade in review | McKinsey
- Companies Are Making Serious Money With AI (mit.edu)
- Marketing AI: Ultimate Guide to Artificial Intelligence for Marketers (martech.org)
- The Outlook for In-Demand IT Jobs | U.S. Department of Labor Blog (dol.gov)