Why should you learn AI now?
Before we go in-depth into the nitty-gritty of AI, let's understand why learning AI is a pressing matter:
Global Impact:
AI has the potential to reshape our world, with sectors like healthcare, education, and finance already incorporating AI strategies. It is a driving force in the shift towards a more digitized future.
Job Opportunities:
The demand for AI specialists is rapidly growing, with a plethora of roles such as data scientists, machine learning engineers, or AI architects. There's a projected 46% growth in AI job openings by 2023 [2].
Check out our AI Career roadmap for more information!
Competitive Advantage:
Mastery over AI not only opens up a wide range of roles but also gives you a competitive edge in your current job.
AI: A high-paying and fast-growing field
AI professionals are some of the highest-paid individuals in the tech industry with an average salary of $120,273 per year [3]. The salaries can range even higher depending on expertise and experience.
The intellectual challenge offered by AI
AI is a rapidly growing field that offers the fascinating challenge of solving complex problems and the opportunity to be at the forefront of technological innovation. AI professionals get to build technologies that can change the way we function, offering a highly satisfying career.
How IU’s AI degrees prepare you for a future in AI
At IU, our AI degrees focus on delivering practical, hands-on learning experiences. You'll learn about applying AI in various sectors, work on real-life projects, and be nurtured by a team of experienced faculty. Our degrees in applied artificial intelligence, artificial intelligence, and MBA in artificial intelligence are tailor-made for both beginners and advanced learners, ensuring a complete how-to-learn AI journey.
How long does it take to learn AI?
Many prospective learners often ask the question: "How long does it take to learn AI?" The answer isn't one-size-fits-all. It depends on various factors, including:
- Your current educational background and knowledge
- The level of expertise you're aiming for
- The time you can dedicate to learning AI
- The complexity of AI topics you want to master
For someone with foundational knowledge in mathematics and programming, it could take anywhere from 6 to 12 months of consistent study to develop an understanding of Artificial Intelligence basics and get comfortable with Machine Learning processes. A deeper, more specialised knowledge in different AI branches like Neural Networks or Reinforcement Learning can add another 6 to 12 months.
Understanding that how to study Artificial Intelligence varies from person to person, the bachelor and master programs offered by IU are specifically designed to cater to individual learning paths. Our program structure takes into account your pre-existing knowledge and experience, helping you master AI at your own pace.
Remember, mastering AI is not a race, it's a marathon. The key isn't just to learn fast, but to learn thoroughly. So, start small, build a strong foundation, and keep expanding your knowledge base. This gradual climb not only makes the journey more manageable but also more enjoyable.
How to learn AI for beginners
As with any vast subject, the thought of learning AI can seem overwhelming at first. It comprises several diverse fields, each as important as the other. However, the process becomes easier once you break it down into manageable chunks.
How to Learn AI Step 1: Mastering the prerequisite skills
Embarking on the journey to master AI begins by fortifying the basic building blocks - Statistics, Mathematics, Programming, and understanding Data Structures. These key skill sets act as stepping stones propelling you towards proficiency in AI.
Foundation skills: statistics and mathematics
Statistics:
Statistics forms the backbone of Machine Learning, a pivotal subset of Artificial Intelligence. By understanding statistical measures, distributions, and concepts like standard deviation or variance, you would guide AI systems in drawing inferences from data.
Mathematics:
To thoroughly grasp the functioning and execution of Machine Learning algorithms in AI, a solid foundation in certain areas of math like linear algebra, calculus, probability theory, and optimization methods is essential. These facilitate critical tasks of navigating through arrays effectively, performing complex computations, understanding randomness and irregularities in data and, ultimately, optimizing Machine Learning models for better artificial intelligence functions.
Core Skill: Programming
Machine Learning processes are brought to life through programming. Hence, understanding and learning to code is critical in learning AI.
Python:
Owing to its simplified syntax and an extensive assortment of libraries expressly designed for data analysis and Machine Learning, Python is frequently the go-to language for AI. Python is a powerful yet beginner-friendly language that aids in effectively implementing Machine Learning models.
R:
Predominantly employed in Data Science, R is another language that supports performing statistical functions and creating engaging data visualizations. Although it has a learning curve steeper than Python, it is worth the investment for enthusiasts looking to dwell deep into AI and Data Science.
Java or C++:
While Python and R have their strengths, languages like Java and C++ provide efficient low-level machine control making them desirable when building scalable Machine Learning or Artificial Intelligence models.
Fundamental concepts: Data structures and manipulation
Understanding data structures is another cornerstone of AI learning. Knowledge of organized data in optimal ways like arrays, stacks, or trees is required. Coupling this with the ability to manipulate this data to perform desired actions forms the basis of creating complex Machine Learning algorithms and subsequently helping AI models make sense of data.
How to Learn AI Step 2: Developing specialist AI skills
Once you have a strong foundation of the basics, the next step in learning AI involves diving into specialized AI fields and tools.
The Importance of data science in AI
The role of Data Science in the realm of AI is of paramount importance as it serves as the linchpin for unravelling the potential locked within massive datasets crucial for AI's functioning. A proficient data scientist toolkit encompasses data preprocessing expertise, adeptness in statistical testing, and adept data visualization skills collectively empower them to navigate the intricate process of refining and interpreting data. This enables a data scientist to extract meaningful insights, identify patterns, and uncover trends that are essential for AI systems to operate effectively.
Without Data Science's meticulous attention to data refinement and analysis, the foundational pillars of AI would remain shaky and incomplete.
The role of Machine Learning and Deep Learning in AI
Within the expansive domain of artificial intelligence (AI), the pivotal roles played by Machine Learning (ML) and Deep Learning (DL) stand as indispensable cornerstones. These two interconnected disciplines serve as the dynamic engines propelling AI's evolution from theoretical concepts to practical reality. Machine Learning, with its ability to equip systems with the capacity to learn and improve from experience, forms the bedrock upon which AI systems make informed decisions and predictions.
Meanwhile, Deep Learning, a subset of ML, leverages neural networks to simulate the intricate web of human cognition, enabling AI models to automatically discover intricate patterns and features within data that might elude traditional programming paradigms.
Together, Machine Learning and Deep Learning infuse AI with the prowess to adapt, learn, and even emulate human intelligence, underpinning the transformative potential that AI holds across diverse industries and facets of modern life.
How to Learn AI Step 3: Familiarize yourself with essential AI tools and packages
Having a strong theoretical understanding of how AI works is beneficial, but applying that knowledge practically is imperative. This understanding is developed through using AI tools and packages.
An overview: Top Python AI tools and packages
Python, being the most popular language for AI, offers extensive libraries and tools which simplify various AI processes. Here are some essential ones that you'll need when figuring out how to learn artificial intelligence:
The significance of Pandas, NumPy, and Scikit-Learn
Pandas:
A software tool ideal for data manipulation and analysis in Python. It provides robust data structures for efficiently storing and manipulating data, which is essential for any AI practitioner.
NumPy:
A Python library used for working with arrays. It also has functions for working in the domain of linear algebra, Fourier transform, and matrices.
Scikit-Learn:
This library brings ML into play. It features various algorithms like support vector machines, random forests, and k-neighbours, and it also supports Python numerical and scientific libraries like NumPy and SciPy.
The power of PyTorch and Keras
PyTorch:
This library provides maximum flexibility and speed in building Machine Learning and deep learning models and is primarily used in Natural Language Processing in AI.
Keras:
A user-friendly neural network library written in Python. It's perfect for newbie learners as it is easy to use and understand.
All of the above tools and steps, along with intentional practice and consistent learning, are the best route to mastering AI. Start with basic foundations, and then gradually move on to more complex projects and advanced tools. Enjoy the journey!
How to Learn AI effectively: Practical tips
Along with a structured learning plan and the right resources, some practical tips can make your journey in learning AI smoother.
1. Choosing your AI focus: Specialization vs Generalization
AI is large and vast. Speciality fields within the AI umbrella include Machine Learning, Robotics, Natural Language Data Processing, Computer Vision, and many more. It's advisable to obtain a broad understanding of AI, subsequently finding the niche that fascinates you the most. Specializing in a particular segment allows you to delve deeper and become an expert in that domain.
2. Starting your learning journey through projects
Integrating your theoretical knowledge with practical work generates the best learning outcomes. Start off with simple tasks such as applying an ML algorithm to a dataset, then slowly advance to more complex projects like building a recommendation system or creating a chatbot.
3. The power of community in AI learning
Engage with the AI community. Platforms such as GitHub, Stack Overflow, or Reddit provide immense scope for learning and collaboration. Participate in discussions, contribute to open projects, and absorb fresh ideas from peers.
4. The art of iterating and enhancing skills
Mastering AI requires patience and repetition. Don't hold back from revisiting topics. Enhance your skills by iterating over your projects and trying to improve them.
At IU, we understand that learning AI can be a challenging yet incredibly rewarding journey. This is why our courses incorporate these practical tips to ensure you gain a comprehensive and practical understanding of AI, irrespective of whether your goal is to learn artificial intelligence from scratch or enhance your pre-existing knowledge.
Additional methods to learn AI
In addition to structured courses and online tutorials, there are multiple other ways in which you can enhance your AI learning experience. Let's delve deeper into a few of them.
Top AI courses to supercharge your learning
Must-read AI books to learn AI for beginners and experts
There is no better friend than a book, and this holds true in understanding AI too. Here are a few must-reads:
Handy AI cheat sheets for quick reference
Cheat sheets can come in handy when you want to quickly glance over syntax, functions or algorithms. Some of the popular ones are:
- Python For Data Science Cheat Sheet [13]
- Stanford's Machine learning cheat sheet [14]
- Deep Learning Cheat Sheet [15]
Each of these additional resources can be beneficial to your goal of understanding how to learn AI. Remember that the journey may appear overwhelming at first, but with the right tools and guidance, you can navigate the challenges. The AI degrees offered by IU have been designed keeping these utilitarian requirements in mind, targeting strong foundational learning enhanced by extensive practical exposure.
