Have you ever wanted to know more about machine learning? Have you ever wondered how you could build your own deep-learning model? Or how about using natural language processing to interpret the nuances of human speech?
All of these things are considered in the broad and ever-expanding field of Artificial Intelligence.
No one would blame you for wanting to get into AI considering some of the cool stuff it can do, not to mention the impact it is having on every one of our lives.
Whether you want a career in AI or are just interested in learning more out of interest, In this article, I’ve outlined some of the first steps you can take to learn more about AI.
What Is Artificial Intelligence?
Artificial Intelligence is a subset of computer science with the fundamental goal of creating machines that effectively mimic human intelligence. AI is made up of several different fields including machine learning (ML), deep learning, neural networks, natural language processing (NLP), and computer vision.
Machine learning uses algorithms that allow computers to learn from and make decisions based on data. These algorithms can be categorized into supervised learning, where a machine learning model is trained on a labeled dataset, and unsupervised learning, where the model identifies patterns in unlabeled data.
Deep learning, a subset of ML, utilizes neural networks with several layers (often called “deep” networks) to progressively extract higher-level features from raw input. It has found success in areas like computer vision, enabling machines to process images similarly to the human brain.
Natural language processing enables machines to understand and interact in human language and makes tools like ChatGPT possible.
The scope of AI is quickly broadening as it is applied to more industries and existing technology types. There’s a high chance you’ve used AI in your daily life without even realizing it.
Why Learn Artificial Intelligence?
Becoming technically equipped with Artificial Intelligence (AI) can be a massive advantage for anyone looking to pursue a technical career in the digital age. AI is a dynamic branch of computer science that includes areas like machine learning, deep learning, natural language processing, neural networks, and computer vision.
A deep understanding of machine learning, where machine learning models are trained through supervised learning or unsupervised learning, allows you to create intelligent systems that learn from data and improve over time. Deep learning, a specialized form of machine learning, utilizes complex neural networks to mimic the human brain, enabling machines to perform high-level tasks that once would have cost human workers significant time.
Natural language processing equips machines to understand and interact in human language, enhancing the scope of user-machine interaction, while computer vision allows computers to interpret and understand the visual world.
The learning path to AI generally requires a strong foundation in computer science and mathematics. The good news is there are emerging fields in AI that require little technical knowledge, and take a more creative approach – prompt engineering being one.
5 Steps To Getting Started With AI In 2023
The beauty of the modern age is that the knowledge of humankind is but a few clicks away. The time has never been easier to learn a new skill set, and what better skillset to learn than AI?
While the following list is numbered, it is not ordered. Many of the points can be done simultaneously.
Let’s take a closer look at how you can get started with AI in 2023:
1. Learn The Fundamentals
Embarking on the AI learning path begins with understanding the fundamentals.
Get to work on building a strong foundation in computer science and mathematics. Most people get scared away at this point but you shouldn’t. With a few months of commitment, anyone can learn these skills, and the best part is you only have to learn them once!
These disciplines underpin AI concepts and are critical in understanding advanced concepts that you’ll come across later in your AI journey. Key areas to consider are probability, statistics, and data structures.
Simultaneously, begin to dive into artificial intelligence principles.
Read books on AI and machine learning to learn about various approaches to AI. For example, it’s crucial to understand the difference between supervised learning, where an AI system is trained on a labeled dataset, and unsupervised learning, which identifies patterns in unstructured data.
Familiarize yourself with machine learning and how machine learning models learn from data. Learn how to build a neural network, which is fundamental in deep learning. Once comfortable with these concepts, it’s time to study more specialized areas, like computer vision and natural language processing.
By giving yourself a good idea of the fundamentals you’ll build a solid base. Spending time here will allow you to move faster as the work becomes more advanced.
2. Find What Interests You
Now is a great time to take a closer look at the various fields of artificial intelligence and see which one might interest you the most. When starting out with AI, you might be surprised how much overlap there is between the fields, so don’t get too worried about picking the right one – the knowledge learned as you dive deeper will have rollover effects if you ever decide you want to learn another topic within AI.
Here are some specific areas you could consider diving deeper into:
- Supervised Learning: Is a type of machine learning where a machine learning model learns from labeled data – meaning they know the values of the inputs and the outputs.
- Unsupervised Learning: Another type of machine learning where patterns in data are identified without the need for labeled examples – meaning just the input values are known.
- Deep Learning: Another subfield of machine learning inspired by the structure and function of the human brain based on neural networks that simulate human learning.
- Natural Language Processing: This focuses on the interaction between computers and human language. It’s a key area if you’re interested in how AI understands and generates text.
- Robotics: Involves creating machines to automate tasks. If you’re fascinated by physical applications of AI, this might be for you.
- Data Science: If you’re interested in extracting insights from large volumes of data, consider this domain. Data scientists often use machine learning models to make data-driven decisions.
- Computer Vision: This field enables AI systems to ‘see’ and interpret visual data, making it crucial for applications like facial recognition or autonomous vehicles.
3. Learn Python
Python is the go-to programming language for artificial intelligence, machine learning, and deep learning because of its readability and wide array of libraries and tools designed specifically.
Learning Python puts you on a learning path that data scientists around the world have succeeded with.
The beauty of Python is that it’s beginner-friendly. If you have a foundation in computer science, you’ll appreciate how Python simplifies coding. Its syntax is clean and easy to understand, which makes developing and debugging code faster and more efficient.
To get started, familiarize yourself with the basics of Python programming like understanding variables, control structures, data types, and functions. Delve into more complex topics like object-oriented programming.
Learn about Python libraries that are frequently used in AI and machine learning, like NumPy for handling large data sets, pandas for data manipulation and analysis, and Matplotlib for data visualization. These data analysis tools will prove invaluable as you progress.
Once comfortable, delve into libraries built for machine learning and deep learning, such as Scikit-learn for implementing supervised and unsupervised learning models, TensorFlow for constructing neural networks, and Keras, a high-level neural networks API for building and training deep learning models.
Finally, reinforce your Python learning with reinforcement learning concepts. This type of machine learning trains an agent to make a sequence of decisions to maximize a reward, a method often used in AI to optimize decision-making processes.
There is a lot of talk about coding becoming redundant with the advent of tools like ChatGPT, but if you ask any software engineer or advanced coder they will assure you that AI won’t be taking over any time soon. Smart coders are certainly using AI to optimize their coding workflow but the need to be able to manage and understand complex coding builds is critical.
The point is, don’t let this discourage you from learning a critical skill in the field of AI.
4. Do A Course
Enrolling in a structured course is a great next step in your learning path.
Courses provide a more formal and guided approach to learning, often including assessments, feedback, and certificates that can demonstrate your new skills. The structure of these courses can also help with motivation when undertaking the task of learning such a difficult and vast topic such as AI.
Several reputable institutions and online learning platforms offer courses on artificial intelligence, machine learning, deep learning, and related topics. It’s important to do a course that aligns with your interests and career goals.
Introductory courses are a great place to start if you’re a beginner. They often start with the fundamentals of computer science before moving onto more advanced topics such as artificial intelligence and machine learning – this helps address step 1.
A comprehensive AI course should cover a broad range of topics such as supervised learning, unsupervised learning, and reinforcement learning. It should also provide exposure to various data analysis tools and machine learning libraries, allowing you to get valuable exposure and experience.
For those interested in deep learning, look for courses that delve into neural networks and their applications. Such courses often include practical projects that can help reinforce the learning and provide practical experience.
Remember to pick a course that offers hands-on projects. Theoretical knowledge is essential, but practical application truly cements your understanding of AI. Whether it’s creating your neural network or developing an AI model for a specific use case, these hands-on projects will give you the confidence to apply AI concepts in real-world scenarios.
If you can find a course that offers community that helps too. Interacting with fellow learners and experienced mentors can significantly enhance your learning experience. You can share ideas, seek help, and gain insights from others’ experiences. This network can also serve as a valuable professional network as you advance in your AI journey.
5. Consider Higher Education
If you’ve come this far and are still looking to expand your knowledge in AI, than higher education might be the next step for you. Higher education can be a powerful stepping stone if you choose to pursue a career in AI. It offers an opportunity to immerse yourself deeply into the intricacies of artificial intelligence, machine learning, and deep learning. And get a deep understanding of the field that would be hard to achieve elsewhere.
Universities and colleges worldwide offer degrees in computer science with a focus on AI. These programs typically include a rigorous curriculum covering both theoretical and practical aspects of AI. You’ll delve into advanced concepts of supervised learning, unsupervised learning, and reinforcement learning, and get hands-on with various data analysis tools.
Higher education often provides access to leading AI researchers and professors, opportunities to work on cutting-edge research projects, and the chance to contribute to research papers. These experiences can provide a deep understanding of AI’s core principles and applications.
Such degrees often include software engineering principles too. This is a valuable aspect because implementing AI is not just about developing models. It involves integrating those models into working software systems, a key skill that machine learning engineers need to solve real-world problems.
Masters and PhD programs offer the most intensive AI training and often focus on a specific AI area. For example, you might choose to specialize in deep learning, contributing new knowledge to the field.
Don’t forget the networking opportunities that higher education provides. Your classmates will likely be your future peers in the AI industry, and your professors can provide connections to industry and academic leaders.
Higher education is not necessary for everyone but it can provide a structured and deep education in AI for those looking to take a leap into AI. It’s a considerable investment of time and money, but experts in the field of AI are some of the highest-paid and sought-after engineers in the world – meaning the financial return could be well worth the effort.
The journey to mastering Artificial Intelligence can seem daunting, particularly given the complex and rapidly evolving nature of the field. However, with structured learning, commitment, and a deep-rooted curiosity, you can build a strong understanding of AI and its various components such as machine learning, deep learning, natural language processing, and more.
Begin with the fundamentals, explore various AI disciplines to identify your interests, and delve deeper into learning Python, the predominant language in AI. Structured courses provide a guided approach to learning, while higher education offers a comprehensive, in-depth exploration into the field.
Remember, it’s not just about the end destination but also the journey of learning itself. As you progress, you’ll come to appreciate the remarkable capabilities of AI and the transformative potential it holds across industries and societies. This journey will equip you with a valuable skillset, future-proof your career, and position you at the forefront of technological innovation.
Finally, as an AI aspirant, embrace the continuous nature of learning in this ever-evolving field. Staying updated with the latest developments, actively participating in the AI community, and continually challenging yourself with new projects will ensure your skills remain relevant and cutting-edge. Happy learning!