Artificial Intelligence

A Step-by-step Guide on how to learn AI and Machine Learning by yourself

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Artificial intelligence and machine learning are one of the hottest topics today. These technologies have developed and evolved over the last ten years to become a part of our lives today. The global artificial intelligence (AI) market is expected to grow at a CAGR of 38.1% from 2022 to 2030.

AI seeks to develop machines that mirror human behavior. It covers anything that gives computers a more human-like appearance or accomplishes challenging activities that are typically the domain of humans. 

The rising popularity of AI and its use in everyday applications has made it a lucrative career prospect for IT professionals, students, or simply anyone interested in advanced technologies. If you wish to know how to learn AI, you are at the right place. 

In this guide, we take you through each stage of how to learn AI and machine learning by yourself. We will also look at the prerequisites to learn machine learning and prerequisites for learning AI

Basics of AI and Machine Learning 

Before we dive into the learning process of how to self-study artificial intelligence, let’s look at the basics and discover what is AI and ML.

What is Artificial Intelligence?

AI is aimed at enabling computers to imitate human-mind and behavior. Any human-like behavior that a machine or system exhibits is evidence of artificial intelligence. In the simplest type of AI, computers are taught to imitate human behavior by utilizing a wealth of data from prior instances of the same activity. This can include everything from identifying the distinctions between a cat and a bird to carrying out complex tasks in a manufacturing environment.

AI enables machines to operate effectively and quickly evaluate massive volumes of data, finding solutions through supervised, unsupervised, or reinforced learning. It can speed up work, remove human mistakes, and carry out many other jobs.

Also read: How do AI and its different components work?

Vertical AI and Horizontal AI are the two broad categories of AI:

Artificial Intelligence categories

What is Vertical AI?

A vertical AI service creates an AI-based solution and controls the entire workflow to address a specific consumer demand. It involves solving issues for a particular type of customer and being a part of a specific industry. 

What is Horizontal AI?

Horizontal AI services are very science-oriented, often academics working on a single, fundamental issue while assisting a variety of sectors. For example, Apple’s Siri or Amazon’s Alexa. They are typically developed from funded research initiatives for academic institutions, the military, or businesses that innovate in fundamental sciences.

What is Machine Learning?

Artificial intelligence is a collection of algorithms and knowledge. One of them is machine learning (ML), and deep learning is one of the methods used in machine learning. ML is a subset of AI. It is the designing and deployment of AI algorithms that can learn things from past data and references.

Defined, machine learning reduces the need for millions of lines of written code. It allows computers to learn by being fed data and statistical approaches to help them improve at a task over time. Following is a list of the three elements of machine learning:

machine learning categories

Supervised Learning

To forecast future events, controlled machine learning algorithms use labeled examples and previous knowledge to apply to new data. The learning process generates a function that can be inferred to forecast output values by examining a known training dataset. 

Once it has received enough training, the system can provide targets for any new input. To identify mistakes and make the model more accurate, it can also compare its output with the desired, proper output.

Unsupervised Learning

When training data does not contain classification or labeling, unsupervised machine learning techniques are utilized. Unsupervised learning investigates how systems might extrapolate a function to explain a concealed structure from unlabeled data. The system never has a specific understanding of the correct output. As opposed to that, it infers what the output should be given datasets.

Reinforcement Learning

Algorithms for reinforcement learning interact with their surroundings by taking actions, identifying mistakes, and learning from successes or failures. Delay in reward and trial-and-error search are the two most essential aspects of reinforcement learning. 

With this technique, software agents and machines may automatically decide what behavior is most appropriate to use in a given situation to function at their best. The reinforcement signal, direct reward feedback, is necessary for the agent to determine which action is better.

How to Self-Study Artificial Intelligence 

AI is an opportunity for IT enthusiasts to explore and address the everyday challenges of using it. Here is the process to learn ai and machine learning by yourself:

1. Prepare a Roadmap

When you start researching prerequisites for learning AI, you will get to know that there are mathematical principles you need to understand. They will enable you to build your models later on in your career and will improve your understanding of how algorithms function in general. Linear algebra, multivariate calculus, and statistics principles, in particular, are essential for comprehending the theory underlying machine learning, currently the most popular AI technique.

2. Brush Up Your Mathematics

AI’s foundation is mathematics since it offers a way of implementing how the goals may be achieved. 

  • Linear Algebra: Vectors and matrices are used extensively in machine learning, from the data structure to the core of the algorithms. 
  • Multivariate Calculus: Calculating the impact of each input on the output is a crucial component of artificial intelligence (AI). Multivariate calculus provides the ideal tools for investigating functions’ connections and inputs. In machine learning, gradients and derivatives are also beneficial tools.
  • Probability and Statistics: Both probability and statistics analyze the frequency of previous occurrences, with probability dealing with estimating the possibility of future events. 

The majority of what you need may be found online for free. In the end, two to three months of study were sufficient to gain the background necessary to approach machine learning.

3. Invest in a Machine Learning Course 

After gaining enough knowledge in mathematics, take a machine learning course to understand how mathematics is employed and how machine learning models are built. It would also help to know how standard algorithms operate. 

By learning from premium machine learning materials, you can create models independently. There are a lot of online courses available that you can take up on platforms such as AnalytixLabs. When you are confident in those machine learning techniques, you can practice them in Python and participate in contests. 

4. Upskill with a Deep Learning Course

Another branch of artificial intelligence that emerged from machine learning is deep learning. Based on networks of artificial neurons modeled after the human brain. These networks consist of tens to hundreds of “layers” of neurons, each receiving and processing data from the preceding layer.

By accumulating more straightforward tasks, a model may learn complex tasks using this layer-by-layer framework. Hence, you need to take a deep learning course to learn more about and understand how it works. 

5. Implement Your Learning 

Having solid theoretical knowledge is excellent but insufficient. Therefore, the final step of the guide is to create a project from start to finish to demonstrate your technical proficiency and to gather experience while making the majority of rookie mistakes before pursuing a job in the area.

Henceforth, you need to research and develop a specific idea for your project, get started writing some code, make tons of mistakes and then learn from them.

How to Learn AI and Machine Learning from Scratch Step by Step?

how to learn AI

At present, the internet has made self-paced learning much more accessible. Be it any discipline, there are tons of resources to get started quickly and gain hands-on experience. 

However, in the case of advanced technological concepts like learning AI and Machine Learning, it becomes pretty overwhelming as well as challenging. Because the content available over the internet is not only huge but also unstructured and complicated. On top of it, this discipline is evolving rapidly in line with the ongoing real-world problems and increasing demands.

Therefore, considering all the aspects you must follow a well-laid-out process to simplify learning ai and machine learning. Follow the below step-by-step roadmap to learn AI and Machine Learning by yourself:   

1. Have Clarity on the Terminologies Involved

The aspirants usually get confused between AI, ML, and Data Science. Indeed, they all are closely connected but are not interchangeable.

In simpler words:

  • Data Science processes a large volume of raw data to extract clean and actionable insights for business intelligence. It uses advanced analytics like AI, ML, statistics, predictive modeling, etc.
  • Artificial Intelligence (AI) enables computer-controlled machines to execute tasks like a human mind. On a broader level, it includes machine learning and human intelligence.
  • Machine Learning (ML) is a subclass of AI which enables machines to learn and process historical data automatically and generate new outcomes. It includes mathematical modeling to make accurate predictions (refined version) with less human intervention. Notably, its study is more based on Data Science.

Therefore, to learn AI from scratch, you must first start with ML and then the general Data Science concepts.

2.  How to Start Machine Learning as a Complete Beginner?

In the first place, understand the prerequisites to learn machine learning (explained later in a separate section) and prepare yourself with the essentials as guided below:

  • Planning, Data Collection, and Validation
  • Data Assumptions and ML Algorithms
  • Data Preprocessing
  • Data Interpretation with ML Models 
  • Different ML Tools like – BigML, Google Cloud AutoML, Azure Machine Learning Studio, Amazon Machine Learning (AML), KNIME, TensorFlow, etc.

Thereafter, study in-depth about:

  • Enhancing the performance and accuracy of ML Models
  • End-to-end data science operations in the business intelligence
  • Machine Learning Workflow

Last but not least, get practical exposure by working on the open-source aggregated datasets available in the ML community. 

Further, start building your portfolio by creating basic or Minimum Viable Products (MVPs) that can solve business complexities and optimize production.

3. You are Ready to Explore AI

Learn AI from scratch by setting your fundamentals strong in mathematics, programming, data structures, and algorithms – these are the prerequisites for learning AI.

Understand the classification of AI technologies based on capabilities – Narrow (ANI), General (AGS), Super (ASI). Further, thoroughly study the below-mentioned AI theories – 

  • Problem Solving and Reasoning
  • Automated Programming
  • Big Data
  • Data Manipulation
  • Natural Language Processing 
  • Deep Learning and Neural Networks
  • Data Engineering
  • Backpropagation
  • Reinforcement Learning Setup

4.  Learn AI Tools and Frameworks

It is a must to understand the practical implications alongside the theoretical knowledge for learning AI and Machine Learning. Simply – learn, apply, experiment, and build MVPs.

The fundamental goal of AI is to assist humans in day-to-day operations. Wherein the vital role of these tools and frameworks is to optimize all these operations devised by developers and data scientists (based on ground problems).

For example, Rainbird, Sisense, Tableau, H2O, Scikit Learn, PyTorch, Theano, etc.

To understand their architectural design and functionalities, learn AI Types – Purely Reactive, Limited, Theory of Mind, and Self-Aware.

5. Begin contributing to the AI/ML communities

Learn AI and Machine learning using the power of communities. No doubt, this is the most constructive approach for growing faster during the learning phase.

Alone self-paced learning can’t help you to master the AI domain. 

Additionally, join group projects, workshops, research writings, boot camps, newsletters, product development cells, live mentorships, questionnaire sessions, etc., to know the latest updates, explore exclusive resources, and expand reach with the experts.

This is how to self-study artificial intelligence – the exact roadmap to get started from absolute scratch. Don’t directly invest in any high-paying program instead –

  • Subscribe to leading blogs, news websites, and free materials to best “test your interest and capability” in the discipline.
  • Hop to the numerous basic AI and ML learning video tutorials on YouTube to strengthen your fundamentals. And club it with comprehensive online books.
  •  Work on the basic projects and real case studies. Then, apply for internships and enroll in paid programs to gain advanced-level expertise.

Prerequisites for Learning AI

How to learn AI for building a concrete foundation to ace the advanced concepts. Here is a tentative study kit- 

  • Mathematics – Linear Algebra, Differentials and Multivariable Calculus, Coordinate Geometry, Nonlinear Transformations, Linear and Logistic Regressions, and Numerical Analysis.
  • Probability and Statistics – Discrete Mathematics, Sampling Distributions, Hypothesis Tests, Elements of Probability, and Distribution Functions.
  • Computer Science –  Theories, Algorithms, Coding, Programming, Data Structures, Computer Hardware Systems, Networking, and Software Systems.

Concerning it, answering the three most burning questions in general –

  • Who can learn AI and ML? Is the AI Program only limited to IT Professionals?

AI, ML, and Data Science Programs are open to all.

  • In case any non-IT background wants to learn AI and Machine Learning?

You need to meet the prerequisites. It does not mean that you need to hold an IT-specific degree, but yes, your foundation must be strong to master AI. It all depends on your interest and approach to pursuing it as a career.

  • Is programming/coding a must to learn AI?

No-code AI courses and boot camps are available but learn Python as a base programming language. Because without coding, you cannot develop an application from scratch and handle advanced complexities. Remember, AI is not entirely a program-oriented discipline, and its scope is far beyond it.

Prerequisites for Learning ML

Are you wondering how to start machine learning? 

Get your basics strong predominantly in three domains – mathematics, numerical analysis, and devising algorithms. Almost all the prerequisites are similar to that of AI. However, the specifics are as follows –

  • Linear Algebra, Calculus, Trigonometry, 
  • Statistics and Probability
  • Coding and Programming Skills – Includes Python, R, Java, etc.
  • Data Exploration – Includes Data Analytics and Visualization Tools
  • Data Manipulation – Includes tools like Excel, SQL, etc.
  • Data Modeling – Includes Predictive, Descriptive, and Decision Models.

The ultimate goal of Machine Learning is data tackling which demands the experience of handling a huge volume of data. Where in the most critical part lies in Data Wrangling and contributes towards generating “optimized-precise-accurate” actionable insights.

The framework of the Machine Learning Model works on three pillars – intention, invention, and adaptation. Therefore, no matter whether you belong to an IT or non-IT background, decent expertise is “a must” to,

  • Deep dive into data patterns and trends
  • Improve decision-making parameters/factors
  • Solve complex real-time problems

Eventually, it will refine your data intelligence in the following aspects:

  • Representation: How the complete analysis is formulated. 
  • Evaluation: How key differentiators are identified.
  • Optimization: How efficiently the data is preprocessed.

Learn AI Programming

Python is the worldwide first-choice AI programming language for developers. Also, good enough for beginners because it is much easier to learn, understand, and implement. 

One of the main reasons for its ever-increasing growth is its simplicity and consistency in syntax, making it faster to execute (read, write, and develop) and automate tasks.

Overall, Python has better tools, libraries, frameworks, and visualization options. As well as, it is flexible, reliable, readable, platform-independent, and requires less coding.

Apart from it, Java, JS, R, C++, Julia, Haskell, Prolog, LISP, Scala, and Rust are the best programming languages for AI development.

The most crucial question is, “Why do you need to learn AI Programming amidst no-code buzz?”

To create robust AI solutions that can closely simulate human intelligence and effortlessly perform tasks. Designing such AI-powered applications requires combined capabilities of dynamic programming, mathematics, and computer science.

Now the question arises how to learn AI Programming? 

Kaggle and Google AI are the most trusted free courses to start at a beginner stage. You can learn AI programming with the full-stack Applied AI course by Analytixlabs to dive deeper than just the basics.

Is AI hard to Learn?  

Nothing is hard if you are passionate enough to dig beneath the surface. However, some really critical problems make AI hard for most of the aspirants –

  • Complex curriculum structure as it involves a multitude of vast fields. The aspirants often get confused about what to pick up first.
  • Rigorous programming becomes a barrier for many. It hardly matters whether you belong to an IT background or not; to develop applications and devise ML algorithms, you need to code. 
  • Incompetence in handling data is the other major roadblock. ML needs data that requires exhaustive intelligence to generate precise and explicit projections.

So, how to self-study artificial intelligence? Define your learning path as follows:

  • Thoroughly study essential maths – calculus, statistics, and probability
  • Master one AI programming language – Python
  • Make a solid foundation in either SQL/Excel
  • Complete Machine Learning Basics
  • Start with AI theories taking forward to algorithms, NLP, and deep learning
  • Build end-to-end basic AI Project 

Learn AI and Machine Learning: Frequently Asked Questions 

How do I start learning AI?

You can start learning AI and machine learning by starting with refreshing your Mathematics and IT basics. To learn AI faster you can enroll in an online course or a data science boot camp. It must include an introduction to machine learning and deep learning for a comprehensive learning experience. These would include technologies like Python, OpenCV, and APIs. After you learn AI programming you can try to create simple chatbots or virtual assistants to practically test what you have studied. 

Can you learn AI on your own? 

You can surely learn AI on your own, as there are many online resources to self-study artificial intelligence. You can refer to YouTube videos, blogs, tutorials, and free online courses. But AI includes much more than programming so you need to be adept with advanced mathematical concepts such as probability, logic, statistics, linear functions, and linear algebra. It is easier to learn AI through an organized course to ensure you understand all concepts properly. 

Is AI difficult to learn?

AI is not difficult to learn but it may be complicated depending upon your background. It involves some advanced concepts that may appear difficult to learn. It is therefore important for you to choose a good program that eases the learning process. Learning the concepts in small chunks makes it easier to learn AI. A good instructor can help you manage the learning process and clarify doubts.

What do I need to learn to learn AI?

You can start learning AI by working on your fundamentals. Beyond basic mathematics, you can look at learning a coding language like Python. You also need to work on advanced concepts such as Calculus, Vectors, Matrices, Statistics, Probability, and Linear Algebra fundamentals.

Additional resources you may like: 

Akancha Tripathi is a Senior Content Writer with experience in writing for SaaS, PaaS, FinTech, technology, and travel industries. Carefully flavoring content to match your brand tone, she writes blog posts, thought-leadership articles, web copy, and social media microcopy.


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