introduction
What if the future of technology does not depend on the work being done by highly skilled professionals, but can be accomplished by anyone who has a desire for knowledge and can follow a process? All of us use AI every day from things like search engines to recommendations, home assistants, medical diagnostic tools, and financial calculating calculators. As technology is changing our way of life, understanding what AI is and how it works will be essential to competing in today’s workforce.
Many people feel overwhelmed at the amount of information (course, applications, terminology, etc…) available to them when they first begin thinking about learning about AI. With a systematic approach to learning about artificial intelligence, most people find it much more manageable and feasible than they had originally expected. This article describes a systematic method of going through the learning process for those who want to learn about artificial intelligence by offering a systematic method for starting with a solid foundation of knowledge, to developing skills through hands-on projects, to maintaining your competency with the ever-changing landscape of AI.
step 1: Develop Strong Foundations in Core Concepts, Math and Programming via Step 1
Artificial Intelligence is based upon clear principles both in mathematics and programming. Everyone interested in AI needs to understand all of the basics before they learn to use all of the advanced models.
Examples of Basic Foundations of AI
- Basic AI Math
- Linear algebra (matrix, vector, matrix multiplication)
- Probability and Statistics (Most common distributions, Mean and Variance)
- Basic calculus (optimization/concepts gradient)
2.Basic Programming Skills
- The use of programming language most commonly used for AI and Technology is Python
- Libraries (NumPy, Pandas, Matplotlib) – these are critical libraries used in AI programming
3.Basic Core Concepts (AI)
- Understanding what machine learning is and how it is different from traditional programming
- Different learning categories (Supervised, Unsupervised, Reinforcement)
As an example, linear algebra allows you to understand how to process data using a neural network, while the use of probability and statistics allows you to determine if AI systems are functioning correctly.
How This Step Will Be Different From Shortcuts
Many beginners to AI try to avoid all fundamentals and rely on AI Toolsets (Pre-built Models) for results that offer faster results but hinder their growth. Building strong AI skills provides the following opportunities to learners:
- Understand how models operate for debugging purposes
- Create customized algorithms
- Use new technologies as AI evolves
Building a strong foundation creates an opportunity for flexibility and a deeper understanding of the components of AI, while using solely toolsets could limit what learners are able to achieve with Artificial Intelligence.
Step 2 – Get Hands-On Experiences Learning Deep Learning and Machine Learning
Once you have a basic understanding of Machine Learning (ML) and Deep Learning (DL), the next step is to learn how to build, train, and evaluate AI models (By actually doing it!).
1 – Core Learning Areas
1 – ML Algorithms
1 – Linear & Logistic Regression
2 – Decision Trees & Random Forests
3 – Linear Support Vector Machines
2 – DL
1 – Neural Networks & Backpropagation
2 – Convolutional Neural Networks (CNNs) using images (image processing)
3 – Recurrent Neural Networks (RNNs) using sequences (text processing)
3 – Frameworks to Practice with
1 – TensorFlow & PyTorch
2 – Scikit-learn (for traditional machine learning analysis)
Practical Example: Create a Simple SPAM Email Classifier – This project will walk you through basic data cleaning/feature extraction/model training/performance evaluation and all types of basic ML (and DL in mainly) use cases in real-world technology companies.
Why Is Project Based Learning More Important Than Just Listening To Lectures And/or Reading Documentation
Many traditional learning approaches are just video presentations or text. However, AI is a skill that requires practice through practical work (projects). When you learn through project-based learning, you can:
1 – Reinforce abstract concepts (that you learned previously)
2 – Build confidence in your capability and problem-solving skills
3 – Develop a Portfolio (to present as proof of actual capabilities)
Learning by doing (through project-based learning) results in job-ready (AI) learners with a working knowledge of both the potential of AI and its limitations, as opposed to an academic approach towards education.
Step 3 – Choosing a Focus Area, Staying Current, and Using Artificial Intelligence in Real Life
AI is growing quickly. Once learners have mastered the basic tools of AI, they need to identify their own area of focus and continue evolving with the field.
Some of the Most Common Areas of Focus for AI
Natural Language Processing (NLP)
Some examples of NLP are chatbots, translation programs, and identifying emotional tone based on words in a sentence.
Computer Vision
Examples of applications of computer vision include recognizing people’s faces, helping a doctor in making decisions based on medical images, and finding objects in visual images.
AI in Business and Technology
Examples of AI in business and technology are recommending items and predicting when and where machines will fail, using big data analysis.
Ethical and Social Issues Related to AI
There are two main areas of concern related to AI—reduction of bias and greater transparency and explainability.
For example, businesses are using AI to drive data and make decisions based on their analyses. Learning to deploy models for use in real-world applications (not just experiments) can provide a link between the two.
AI Is Constantly Changing and Learning, Giving You an Edge Over Your Competitors
Your knowledge and skills in AI will continue to change, as new models, tools, and research continue to be developed regularly. Those who are successful in their pursuit of learning are:
- Following AI’s researchers and their trusted blogs
- Participating in online communities and competitions
- Experimenting with new tools and frameworks
AI professionals who have continued to learn and grow while focus on a specific area of AI will find themselves in demand in comparison to people who have traditional technological job responsibilities.
calculation
To learn AI systematically means you do not need to become an expert immediately but to gradually create good foundational knowledge of mathematics, computer programming and artificial intelligence through practice (using hands-on experience with both machine learning and deep learning) and will evolve into specialized knowledge continuing with progressive education through practical application in the workplace using AI On-Demand.
Artificial Intelligence is being integrated into virtually every area of technology today; therefore, anyone interested in learning about AI will find it accessible to them. Start with simple projects, don’t get discouraged if you do not know everything right at first; keep working toward mastering concepts rather than taking shortcuts. No matter if you want to use AI for professional purposes, innovation or just curiosity, AI has many tools available to help shape the way we will live in the future.
The call to action
to learn AI would have been yesterday. Your second-best opportunity is now. Make sure that you take action today so that you can develop knowledge/skill sets which will be the building blocks of future technologies!!