Artificial Intelligence (AI) and its components illustration

How to Learn AI from scratch in 2024 . A complete Guide from the Experts


We are currently living  in a technology driven world , as the importance of artificial intelligence (AI) technologies is becoming more and more evident. According to a Forbes Advisor survey, 97% of business owners think that chatbots like ChatGPT will bring about positive changes in their operations. Additionally, tools such as ChatGPTMidjourney, and Bard are making AI more established . 

If you are an aspiring data scientist, machine learning engineer, AI researcher, or just an enthusiast about AI, this blog is for you. Throughout the blog , we will detail how to learn AI from the ground level . We will also provide insights from industry experts to help guide your journey. We cover the skills and tools you’ll need to master, as well as how AI help businesses to grow .

What is Artificial Intelligence (AI)?

Artificial intelligence (AI) is a field of computer science centered on developing systems that can carry out functions typically requiring human cognition. These functions include  natural language, identifying patterns, making choices, and learning from prior experiences. AI is a broad field with a wide range of sub-fields, each with distinct goals and concentrations .Learn basics of artificial Intelligence for begineers

1.  Weak AI or Narrow AI

Artificial Narrow Intelligence (ANI): This type of AI, which we interact with regularly nowadays, is designed for one specific task, like speech recognition or generating recommendations on streaming platforms

2. General AI

Artificial General Intelligence (AGI): AGI refers to an AI system that can demonstrate human-level competence across a broad range of capabilities like understanding, learning, adapting, and applying knowledge. Although large language models like ChatGPT showcase some ability to generalize across numerous tasks, true AGI remains hypothetical as of 2023

3.  Super AI

Artificial Super Intelligence (ASI): ASI represents a hypothetical future stage where AI systems surpass human intelligence on nearly all economically valuable work. Some key characteristics of strong AI include the ability to think, reason, solve puzzles, make judgments, plan, learn, and communicate on its own.

The difference between data science, artificial intelligence, machine learning & deep learning 

Artificial intelligence consists of computer systems capable of exhibiting intelligent behavior, reasoning, and learning similar to humans.

Machine learning is a branch of artificial intelligence concentrated on designing algorithms that can learn on their own without the need for explicit programming instructions.

Deep learning represents a subdivision of machine learning. It is the technology behind many of the most impressive recent advances in AI like self-driving cars and ChatGPT.

Deep learning algorithms take inspiration from brain architecture and are especially adept at working with unstructured data such as images, text and videos.

The key differences are:
TermDefinitionFocusTechniques Used
Data ScienceAn interdisciplinary field focused on extracting insights from data by leveraging statistics, programming, algorithms, machine learning, and moreFinding patterns in data to provide insights for business decisions and product improvementsStatistics, mathematics, machine learning, data visualization programming
Artificial IntelligenceThe broad concept of machines being able to carry out tasks associated with intelligent beingsEnabling computers and robots to learn, reason, and carry out decision making autonomouslyMachine learning, deep learning, knowledge representation, logic, search, robotic control systems
Machine LearningThe study of computer algorithms that analyze data, learn from it, and then make a prediction or decision without being explicitly programmedCreating algorithms that can learn from and make predictions on dataClassification algorithms, regression algorithms, clustering algorithms
Deep LearningA subset of machine learning based on artificial neural networks with multiple abstraction layersEnabling machines to learn in a way loosely inspired by biological brains via neural networksConvolutional neural networks, recurrent neural networks, generative adversarial networks, transformers

Why Should You Learn Artificial Intelligence from ground up ?

Artificial intelligence is an incredibly revolutionary  technology that is fundamentally changing the way we work, live, and communicate. As the amount of data in the world explodes and the need to predict  from that data , demand for AI skills is surging day by day .  There has never been a better moment to begin updating yourself with AI capabilities. The reasons are as follows:

AI is a fast-growing field

The number of AI jobs has seen significant growth over recent years. According to the World Economic Forum’s Future of Jobs report, AI and machine learning specialists top the list of fast-growing jobs over the next five years. As industries continue to adopt AI technologies to streamline their operations and make better decisions, the demand for AI specialists will likely only increase.

Artificial intelligence (AI) market size worldwide in 2021 with a forecast until 2030 :

AI is a high-paying job

Based on January  2024 figures from Glassdoor, the average annual salary for an AI engineer in the US is $174845, with likely additional compensation from bonuses and profit sharing.

The average salary for Artificial Intelligence is US$174,845 per year in the United States. The average additional cash compensation for an Artificial Intelligence in the United States is US$40,882, with a range from US$30,662 – US$57,235. Salaries estimates are based on 36 salaries submitted anonymously to Glassdoor by Artificial Intelligence employees in the United States.

Master the prerequisite skills

Basic mathematics: Math provides the foundational language and analytical tools needed to understand and advance artificial intelligence. Concepts of statistics, linear algebra, and calculus enable building machine learning algorithms that can identify complex patterns

A willingness to learn: A growth mindset characterized by curiosity, grit, and humility is vital for achieving success in AI. With technology progressing so swiftly, a flexible, self-motivated commitment to lifelong learning enables impactful contributions to the field.

Programming: Programming enables creating the software that powers artificial intelligence. It involves using languages like Python and frameworks like TensorFlow to code algorithms and models that can learn from data. Programming builds the logic and pipeline to feed data to AI systems so they can be trained to accomplish complex tasks like computer vision, speech and image recognition.

Data structures: Data structures are ways of organizing data that allow for efficient storage, access, and manipulation. In data science, structures like arrays, linked lists, stacks, queues, trees, and graphs enable analyzing relationships in data at scale. Choosing the right data structures improves performance of algorithms and models used for tasks like classification, prediction, and recommendation. Carefully designed data structures help manage computer memory and run key operations like sorting, searching, inserting, and deleting needed for tasks from data preprocessing to machine learning model training and prediction.

Data manipulation:  Data manipulation refers to the preprocessing, cleaning, normalization and formatting of data before feeding it into AI algorithms. For AI systems, properly structured and clean data is essential for effectively detecting patterns, making predictions, and generating insights. Data manipulation transforms raw data into a reliable information source, handling issues like missing values and variability to improve quality. It enables selecting the right features, dealing with outliers, and transforming variables so algorithms can accurately find relationships during training. Handling bias in datasets through manipulation makes models inclusive and trustworthy. Overall, meticulous data manipulation determines the ultimate integrity and utility of the data that powers artificial intelligence.

Data science:Data science sits at the intersection of computer science, statistics, and domain expertise to extract insights from data. It employs techniques and theories drawn from mathematics, statistics, information visualization, graphic design, and business analysis to process, analyze, and gain actionable knowledge from raw data. From building AI training datasets to tracking model performance in production, data science leverages statistical and computational methods to enable key artificial intelligence capabilities such as computer vision, speech recognition, and autonomous decision-making. The foundational and evolving contributions of data science continue to progress artificial intelligence towards more accurate, robust, and human-like intelligence.

Machine learning: Machine learning is a subset of artificial intelligence that enables computers to learn patterns from data, without being explicitly programmed. It involves feeding data to algorithms that can recognize underlying relationships and make predictions or decisions based on insights learned from the data. The algorithms continue to improve themselves over time as they process more data. Machine learning techniques like neural networks, regression, clustering, and decision trees identify patterns that drive artificial intelligence capabilities such as image recognition, natural language processing, recommendation engines, and predictive analytics.

Top Python AI tools and packagesblog idea


Pandas is a Python programming language library with comprehensive tools for data analysis. Data scientists utilize Pandas for a variety of critical tasks, including cleaning messy data, transforming data into more usable formats, and performing statistical data analysis. One of  Pandas’ strengths is its ability to work effectively with incomplete, disorderly, or unlabeled data. This makes Pandas an invaluable component of many data scientists’ toolkits when they need to preprocess and wrangle datasets before analysis.


NumPy, which stands for Numerical Python, is a Python programming library that enables support for large multidimensional arrays and matrices. It also provides a substantial set of high-level mathematical functions that can perform operations on these arrays and matrices. NumPy is an essential library for performing scientific computations using Python across many domains, including artificial intelligence


Scikit-Learn is a powerful Python library for data mining and machine learning applications. Constructed on top of other core Python scientific and numerical computing packages like NumPy, SciPy, and matplotlib, Scikit-Learn is open-source and thus accessible free of cost to all users. The library contains implementations of a wide variety of standard machine learning algorithms for classification, regression, clustering, dimensionality reduction. 


PyTorch is an open-source framework built on Torch optimized for deep learning research. It offers flexible and rapid prototyping of everything from natural language processing to computer vision through neural networks. The primary benefit of PyTorch is its speed and adaptability, empowering researchers to quickly iterate on neural network architectures.


Keras is an accessible and easy-to-use deep learning framework for Python focused on enabling fast experimentation. It provides a high-level set of abstractions and interfaces to simplify and accelerate the process of creating neural network models. The modular architecture of Keras allows for unprecedented flexibility in rapidly assembling models to test innovative ideas in deep learning. 

Commercially available APIs

Leading providers like OpenAI, Cohere, and Anthropic offer pay-as-you-go access to state-of-the-art models through user-friendly interfaces. Their services enable skipping over the complex machine learning pipeline for model development and instead interface with production-ready systems optimized for tasks like text generation, semantic search, speech recognition and more. 


How long does it take to learn AI?

The path to AI competency varies from self-driven to conventional education, spanning months to  one year . Independently developing skills could enable gaining proficiency in Python, linear algebra, statistics, neural networks and other fundamentals in as little as 12 months. 

Why should I learn Artificial Intelligence right now?

AI is a rapidly expanding field that offers high-paying and intellectually demanding jobs. The AI market size is projected to grow at a CAGR of 37.3% by 2030

Who can benefit from learning AI?

Anyone can learn AI in their career or personal life. AI is changing the way people do things, solve problems, and make choices. Even if you aren’t employed in software engineering, data analysis, or research, basic AI skills will help you understand current and future developments in the AI world.

Is AI difficult to learn?

AI can be difficult to master, but with hard work and a strategic mindset, it’s an attainable goal. There are many resources, courses and communities to help you on your journey.

What skills should I develop to learn AI?

In order to learn AI, you’ll need to have basic skills in Statistics, Mathematics (such as Linear Algebra and Probability), Programming, Data Structures, and Data Manipulation.

Which AI tools and packages should I learn?

The most widely used languages for AI are Python and R, while learning libraries such as pandas, numpy, simkit-learn, pytorch, keras can also be useful. These libraries provide functions for manipulating data, analyzing statistics, creating machine learning models, and developing deep learning algorithms.

Can I find a job in AI without a degree?

Getting a degree is important, but it’s not the only way to advance in AI. You’ll want to stay up to date on the latest developments in the field, create a portfolio of your AI projects, customize your resume for ATS. By sharing your work, thoughts, and passions on social media platforms such as LinkedIn and Twitter, you’ll be able to draw in hiring managers’ attention