How To Get Into AI

Written by Adam Morris

Updated August 3, 2023
half human half AI character

 The use of Artificial Intelligence offers numerous benefits and possibilities. We are seeing its application across different areas such as healthcare, finance, logistics, and more. AI is becoming increasingly popular in these sectors, giving businesses great opportunities to create smarter systems and processes.

Interested to learn about Artificial Intelligence (AI)? AI can be overwhelming, but don’t worry! Our article will guide you through the important steps and considerations.

AI refers to technology being used to perform tasks that typically require human intelligence, like understanding language or recognizing objects in photos. In order to get started, consider courses and certifications that cover the basics of AI theory and development. You got this!

What is Artificial Intelligence?

AI enables machines to think and act like humans. Tasks range from facial recognition, customer service, expert systems, and predictive analytics. This involves a blend of disciplines including machine learning, deep learning, and NLP.

3D AI root with networks on black background

Machine Learning is the branch of AI that deals with training computer algorithms using large sets of data so that they can learn from them and improve over time without needing explicit instructions on how to handle specific tasks. Deep Learning works by creating complex networks from existing data.

Natural Language Processing deals with the analysis of human language in order for machines to understand it effectively. Ultimately, AI enables computers to learn automatically from examples and experience instead of explicitly programmed instructions — making intelligent behavior achievable with less effort than ever before!

Also see: Should I Learn Machine Learning Before AI?

How to get into AI?

Math Background 

Mathematics is essential for any individual studying Artificial Intelligence. Calculus, linear algebra, probability, and statistics, as well as algorithm applications, are all important topics in this field. Consequently, having a clear understanding of these topics will enable someone to gain a comprehensive comprehension of AI.

Understanding these core topics is an essential first step before diving into courses and other learning platforms related to AI. A rigorous math education will give you an edge when it comes to grasping concepts quickly, applying them in real-world applications, experimenting with different methods and optimization techniques.

With sufficient exposure to the basics of mathematical theory and practice, you will become familiar with powerful tools that can help you build smarter models that improve performance while minimizing errors. Furthermore, having a sharp eye towards making sure your models are not overfitting or underfitting can be significantly improved through understanding mathematical principles such as regularization techniques or boosting based methods.

Determine Your AI Focus

When narrowing down the focus to build an AI solution it is important to have a clear goal and understanding of what needs to be achieved.

Vector illustration of planning to reach a goal

To develop an algorithm that can accurately forecast a person’s blood pressure, data sources and relevant information about the target population must be identified. Furthermore, different technology requirements such as machine-learned libraries and software tools should be accounted for. To ensure efficacy of the model, experiments to evaluate the algorithm performance must be designed.

Having a specific goal and developing a detailed plan before creating the solution, this helps to increase efficiency and results in more successful implementation of AI solutions.

Understanding these elements beforehand allows us to come up with innovative ideas to address our problem while focusing on using resources efficiently, making sure our idea is still feasible within any given constraints that may arise during development or maintenance phases. Taking all these steps gives us an advantage in finally having an effective system that serves its core purpose: predicting blood pressure with accuracy.

Experiment on Simple Problems First

Gaining a better understanding of AI is easy – just try it! Start by solving simple problems with different AI algorithms and gain an appreciation of how they can be used in practical situations. Develop your software development skills while doing so.

Concept of wires getting untangled

Once an easy problem has been identified, a framework for harnessing algorithmic decision-making needs to be established. Try and experiment with multiple approaches such as supervised, unsupervised, and reinforcement learning, whilst running tests and collecting data. With this approach, one can identify the quickest solution with minimal effort while deepening their understanding of AI software development. While further refining your solution with optimization techniques and parameters tuning, the performance of your model will improve significantly over time.

By observing various outcomes from different approaches and testing their efficacy, one can easily determine the quickest solution for a given problem. Ultimately, this allows for experimentation within AI software development and helps build confidence when tackling more difficult challenges in the future.

Learn About Deep Learning

As anyone interested in deep learning must be aware, Artificial Neural Networks are at the core of this method. They are responsible for the extrapolation and association of complex patterns from a large set and thus allow machines to “learn” from data. To get started with this fascinating endeavor, one should gain a thorough understanding of the underlying principles and technologies that make up these networks.

The internet provides numerous materials available to those wishing to dive into this subject matter and become proficient in it. The best starting point might be the Deep Learning online resource: it offers concise explanations of neural networks, detailing their “neurons” structure and components, their information processing properties, their relationship with classical computer algorithms, etc.

Besides books and theoretical readings, aspiring deep-learning practitioners can also experiment with real-life tasks by making use of open-source frameworks such as Google’s TensorFlow –the most popular one– as well as Scikit-learn or Theano. It is through tinkering that one learns!

Choose A Programming Language

Choosing your programming language for AI development can seem like an intimidating task. After all, the language you choose should have robust libraries and tools available to build complex algorithms and programs more efficiently. Performance is another factor you’ll want to consider since immense amounts of computations are required in AI development. Fortunately, there are a few mainstream languages that offer excellent performance while also having great library availability.

Hand typing on laptop with coding language on screen

Choosing Python for data science and machine learning is an excellent choice! It’s user-friendly, straightforward to use, and packed with useful features. Plus, it’s one of the fastest-growing languages currently available when talking about ML/AI developments.

C++ is another popular choice due to its flexibility and portability; it’s especially useful for deep learning development due to its lower-level library requirements. Java provides yet another great option for developing applications with comprehensive helpful libraries for AI-related tasks; it’s definitely faster than Python when running intensive applications too. Lastly, Octave stands out thanks to its intuitive syntax which closely resembles MATLAB; this makes it ideal for prototyping or exploring new research ideas.

Ultimately, choosing a programming language will depend on your specific needs; each offers their own advantages so try out a few. 

Computational Learning Theory

Computational learning theory is an essential tool in data analysis, modeling, and prediction. It is applied to various fields, including natural language processing (NLP). Through this theory, algorithms can be developed that produce reliable predictions.

Natural language processing involves computer systems that are able to comprehend natural language input and provide meaningful output for tasks such as automated understanding, document summarization, text classification, sentiment analysis, machine translation, entity recognition, and more. The development and use of NLP models require a vast amount of knowledge in computational learning theory in order to build an efficient system.

Familiarity with supervised learning algorithms, Bayesian networks, game theory, and reinforcement learning is required.

Furthermore, it necessitates building a deep understanding of theoretical topics related to computational strategies used for NLP applications as well as knowledge regarding statistical models used to estimate probabilities associated with their outputs. Knowing all this will bring you one step close to building proficient NLP systems that can recognize changes in context and semantically learn patterns from large datasets. Consequently, by mastering computational learning theories you will strengthen your foundation in AI development and become much better at the task of developing successful Natural Language Processing systems.

Build A Computer Hardware

To effectively utilize artificial intelligence and machine learning, a high-performance hardware system is essential. This system should contain CPUs, GPUs, and fast network connections for managing large and complex models as well as facilitating rapid experimentation.

Computer hardware parts on wood table

The decision of whether to build in-house powerful computing hardware or opt for a cloud-based solution largely comes down to budget constraints and usage requirements. Building your own supercomputer gives you complete control over the system’s configuration, ensuring that you can customize the hardware setup according to your specific needs.

In addition, having dedicated hardware onsite can be beneficial in certain use cases such as when ultra-secure data storage is required for privacy protection or real-time applications necessitate low-latency communication. On the other hand, cloud services come with their own perks such as scalability and cost savings for companies with peak demand pools or unpredictable utilization periods.

With cloud usage becoming increasingly popular, however, enterprise customers today now have access to virtual machines boasting high-performance levels not far from those achieved by dedicated systems. However, be aware that cloud providers usually charge for usage which can add up quickly if you’re running frequent experiments or using to train models with massive datasets.

Participate in Kaggle Competition

Participating in Kaggle competitions is an excellent way to put one’s newly acquired skills to the test and build up a good portfolio of AI software development projects. Kaggle offers many different kinds of challenges and problems related to artificial intelligence, ranging from image recognition to natural language processing.

Participants exploring AI programming can benefit from the opportunity to explore various approaches and find the optimal solution. Dialogue and idea exchange on a dedicated AI forum page facilitates shared understanding and collaboration amongst the AI community.

Vector illustration of group discussing

In addition, competing in a Kaggle contest can give developers exposure within the AI field as their ranking reflects their success and meaningful contributions to the project. Since several experienced data scientists participate at Kaggle already, joining competitions will enable beginners to network and learn from more experienced peers about the industry’s best practices and technologies.

Kaggle competitions are a great and fun way to advance your knowledge of artificial intelligence! Not only is it enjoyable, but also an incredible opportunity.

Take Free Online Courses

Stanford University and Google both offer excellent free online courses to learn Artificial Intelligence (AI). Stanford’s Machine Learning course is a great option for those wanting an in-depth look into AI concepts. It is instructed by Google Brain’s founder, Andre Ng, and contains current examples of AI technology to help students understand neural networks better.

Woman smiling while writing on notepad in front of laptop

On the other hand, Google’s own project – Learn with Google AI – is also great if you’re just getting started in AI development. This includes their machine learning course merging the TensorFlow Library as a tool to help beginners become accustomed to AI development. So whether you want to start from the basics or go all out with advanced knowledge, it’s worth checking out both courses if you intend to pursue an AI career.

Do you need a Master’s Degree to get into AI?

With a Master’s program, you can take your current understanding and content mastery to another level by deepening your theoretical understanding and accessing specialized materials.

This will bolster problem-solving abilities and create numerous opportunities for exploring different areas of expertise to fulfill your career ambitions.

Most programs will provide the fundamentals of machine learning and theoretical AI courses so that you can build knowledge and expertise in this specific field while offering plenty of hands-on experience with relevant projects. Thus, obtaining a master’s degree can be beneficial for individuals looking to pursue higher levels of leadership roles entailing supervision and developing technological advances to stand out among other applicants.


What are the skills required for ai engineer?

AI engineers need an array of knowledge and skills. Math, statistic, and computer science are integral, as is being adept in coding languages like C++, Java, Python, and R. Additionally, you should have a grasp on AI models–like machine learning, natural language processing, and neural networks–to create digital solutions mirroring the human brain. It’s also important to understand software development processes, large-scale data analysis, and cloud computing technology. Finally, research approaches, problem-solving skills, and debugging are essential for this role.

How to get into ai and machine learning?

If you’re aiming to build a career path in Artificial Intelligence and Machine Learning, there are some basics that could be very helpful. Math, Robotics, Statistics, and Computer Science should form the foundation of your skillset. Expanding on this knowledge with Linear Algebra, Calculus, Neural Networks, and Machine Learning-specific algorithms will take you further. Why not develop further by learning coding languages such as Python or R? And finally get hands-on experience using datasets and projects with TensorFlow or PyTorch.

Can you get into ai without a degree?

You don’t need a degree in AI and Machine Learning to enter the field of AI. Programming, math, engineering, data science, and computer vision expertise can help you make it. To become a professional in this area, consider taking online courses or getting certified. Experience matters too – internships and volunteer work may open opportunities for success in the future.

Who earns more AI or data science?

Data science earns more than AI, though the gap is quickly narrowing. Data scientists command higher salaries due to their extensive knowledge of both data and complex algorithms used to process it. AI engineers mostly automate tasks using pre-existing technology or algorithms from research. Specialization in specific software and frameworks may restrict their earning potential compared to data scientists.

Is AI very tough to learn?

Learning AI is largely dependent on your current skill level, the kind of AI you’re studying, and the amount of time you spend on it. Getting a hold of basic concepts and applications shouldn’t be too difficult – just a few weeks or months should do it. But for advanced coding for machine learning and huge datasets, more practice and training is necessary – this usually takes longer. For a more in-depth discussion, see Is AI Hard To Code?

What qualifications do you need for AI?

Becoming a specialist in Artificial Intelligence requires knowledge in mathematics, programming, data science, and machine learning. Individuals need to be able to pinpoint patterns and form models from large volumes of information. To do this, they must be knowledgeable in coding languages such as Python, Java, C/C++, and MATLAB; as well as frameworks like TensorFlow and Keras. To top it off, friendly communication skills and business acumen are key for delivering powerful business insights within this field. But don’t just take our word for it – getting certified from organizations like Google or IBM would certainly give your credentials a boost!

How do I start learning AI?

Getting into AI is easier than you think! Start with the basics, like neural networks and decision trees, as well as supervised/unsupervised learning algorithms. Once you’ve got a handle on the concepts, time to get coding – Python or R are great languages to start with. Then you can explore helpful libraries like TensorFlow and Scikit-learn. 

What does a machine learning engineer do?

A machine learning engineer is in charge of crafting and constructing a system that can learn and enhance itself through data. They create predictive solutions by developing, testing, and rolling out machine learning models. Working alongside other data engineers and scientists, they figure out which algorithms serve certain tasks best. On top of that, they have to manage the intricacy of deep learning frameworks while dealing with large datasets. To succeed in this role, they need skills in statistics, software engineering, mathematics, and computer science fundamentals.

Adam is a crypto expert & AI enthusiast who has been researching and writing on the topics since 2017.

He’s spoken on numerous podcasts and has been featured in many prominent media publications such as Forbes, CNN & CNBC.