What is AI?

AI (Artificial Intelligence) is a machine’s ability to perform cognitive functions like humans, such as perceiving, learning, reasoning, and solving problems. AI is a computer system that can perform tasks that normally require human intelligence. It includes recognizing objects, understanding natural language, and making decisions. It is a powerful tool that helps your business grow and improve.

AI Vs. Machine Learning (ML) Vs. Deep Learning

We often hear Artificial Intelligence being used interchangeably with other buzzwords, such as machine learning (ML) or deep learning. However, this is a faulty practice, as machine learning is the subset of the overarching category of AI. If we wanted to define machine learning, we would talk about techniques and algorithms that allow the AI system to “learn” through a set of data that act as examples. The more advanced machine learning becomes, the better results it produces and the less training it needs from humans. Specifically, there are three types of ML: supervised algorithms, unsupervised models, and reinforcement learning.

In turn, deep learning is a subset of machine learning, where neural networks analyze stimuli further and deliver more accurate results. This technique approaches human intelligence a little more since it processes data in several layers where different details and criteria are analyzed. Deep learning is mostly used in natural speech and visual processing devices, such as voice assistants and face recognition systems, respectively.

Why should you learn Artificial Intelligence?

 

I strongly believe that learning Artificial Intelligence (AI) in 2023 is highly beneficial for several reasons:

Increasing Demand: AI is a rapidly growing field with increasing demand in many industries, including healthcare, finance, transportation, and entertainment. As more and more organizations recognize the value of AI, the demand for skilled professionals in this field will continue to grow.

Career Opportunities: The demand for AI professionals is so high that it has created a shortage of qualified candidates. This means that there are plenty of career opportunities for those who have the right skills and knowledge.

High Salaries: AI professionals are highly sought after, and as a result, they command high salaries. According to various reports, AI-related jobs have some of the highest salaries in the tech industry.

Technological Advancements: AI is at the forefront of technological advancements, and it is becoming increasingly important for professionals in many fields to have a basic understanding of AI. From self-driving cars to personalized medicine, AI is transforming the way we live and work.

Personal Growth: Learning AI can be a fulfilling and rewarding experience. It is a challenging field that requires creativity, problem-solving, and critical thinking skills. These skills can help you grow both professionally and personally.

In summary, learning AI in 2023 can open up many opportunities, provide financial rewards, and allow you to stay at the forefront of technological advancements while also developing valuable skills.

 

Applications of Artificial Intelligence

Artificial Intelligence (AI) has a wide range of applications in various industries, including but not limited to:

Healthcare: AI is used in medical imaging, drug discovery, and personalized medicine. AI-powered systems can analyze medical images to detect diseases, identify potential drug targets, and create personalized treatment plans.

Finance: AI is used in fraud detection, credit risk assessment, and algorithmic trading. AI-powered systems can analyze large amounts of data to identify fraudulent transactions and make more accurate predictions about financial markets.

Transportation: AI is used in self-driving cars, traffic management, and logistics optimization. AI-powered systems can analyze traffic patterns and optimize transportation routes to improve efficiency and reduce congestion.

Retail: AI is used in personalized marketing, inventory management, and customer service. AI-powered systems can analyze customer data to create personalized recommendations and improve the overall shopping experience.

Entertainment: AI is used in content creation, recommendation systems, and virtual assistants. AI-powered systems can analyze user data to create personalized recommendations and assist with tasks such as scheduling and email management.

Education: AI is used in intelligent tutoring systems, personalized learning, and plagiarism detection. AI-powered systems can analyze student data to create personalized learning experiences and detect instances of plagiarism.

These are just a few examples of the many applications of AI. As technology continues to evolve, we can expect to see even more innovative applications across various industries.

Some example applications: 

1. Google’s AI-powered predictions (Google Maps)
2. Ride-sharing applications (Uber, Lyft)
3. AI Autopilot in Commercial Flights
4. Spam filters on Emails
5. Plagiarism checkers and tools
6. Facial Recognition
7. Search Recommendations
8. Voice-to-text features
9. Smart personal assistants (Siri, Alexa)
10. Fraud protection and prevention

Prerequisites

At the core of Artificial Intelligence, there are subjects that we have already learned in our high schools. They give you a good foundation to stand upon when you start learning the basics of Artificial Intelligence and Machine Learning. Some of these include –

• Computer Science fundamentals.
• Statistics and Probability.
• Linear Algebra includes topics such as vectors, matrices, and derivatives.
• Calculus.
• Discrete mathematics.
• Data Structures.
• Algorithms and their analysis.
• Python or R programming. 

If you want to learn AI to solve real-life problems, then tons of existing libraries and toolkits will help you with almost any problem out there. But if you want to get into the research field, it’s best to brush up on your mathematical skills before foraying into AI.

If you are from a CS/IT background, you must have oiled data structures and algorithms almost throughout your curriculum. Having data structures and algorithms in your portfolio is always a good thing, and in this case, it’s even better. Finally, to carry out machine learning algorithms for training, you need to know the basics of any language that can carry out statistical computations. Usually, we go with either Python or R because both of them have got excellent sets of libraries that can implement any complex algorithms with a few lines of code.

Roadmap To Learn Artificial Intelligence

Learning Artificial Intelligence (AI) can be a challenging but rewarding experience. Here’s a brief roadmap to help you get started:

Learn the basics of programming: Before diving into AI, you need to have a strong foundation in programming. Learn a programming language like Python or Java, and familiarize yourself with concepts such as loops, conditional statements, and functions.

Learn Data Science: Data Science is an essential component of AI, so learn how to work with data, understand statistics and probability, and learn how to use tools such as pandas, numpy, matplotlib, and seaborn.

Learn Machine Learning: Machine Learning is a subset of AI and involves using algorithms to learn patterns in data. Learn different machine learning models like linear regression, logistic regression, decision trees, random forests, and neural networks. Also, learn about different optimization techniques like gradient descent, stochastic gradient descent, and backpropagation.

Deep Learning: Deep Learning is an advanced subset of Machine Learning, and it involves using neural networks to model complex patterns in data. Learn how to build different types of neural networks such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.

Practice: Practice is key to mastering AI. Start with small projects, like building a simple chatbot or recommendation system, and gradually work your way up to more complex projects.

Stay updated: AI is a rapidly evolving field, and new tools and techniques are being developed all the time. Stay updated with the latest advancements in AI, attend webinars, and participate in online communities to stay connected with fellow AI enthusiasts.

By following this roadmap, you can gain a strong foundation in AI and develop the skills you need to build cutting-edge AI applications.

One of the biggest reasons why people don’t directly jump into the field of Artificial Intelligence is because they don’t know where to start. There is a lot of technical jargon that comes their way when they search for resources to learn Artificial Intelligence.

Here is a detailed process using which you can easily begin your journey with AI.

Master the Fundamentals

 

The best way to start with AI is to brush up on your fundamentals. You can get started with basic mathematics and then preferably get your hands dirty on a coding language. It’s always recommended to go with Python because of its large and supportive community and tons of packages and libraries that will assist you throughout your journey.

Here’s something that you might want to brush up on.

• Matrices and Linear Algebra fundamentals.
• Calculus.
• Graph Theory.
• Vectors.
• Statistics and Probability.

Apart from this, there are a few tools that you might want to learn that will help you to handle data in a better way.

• Database basics.
  • SQL and Joins in SQL.
  • Relational and non-relational databases.
  • NoSQL databases.
• Tabular data (Excel).
• Data Frames and Data Series.
• Data Formats (JSON, CSV, XML).
• Regular Expressions.
• Extract, transform, and load data.

After having brushed up on these topics, it’s the perfect time to dive deep into the basics of a programming language that can seamlessly handle data. There are two options that we generally have – Python and R. Mostly, companies prefer Python over R due to the immense support that it provides. Here’s a roadmap to learning Python for Artificial Intelligence.

• Python basics – Expressions, variables, data structures, functions, packages such as pip, etc.
• After having learned the basics, next you need to learn some important data-handling libraries such as pandas, NumPy, and matplotlib.
• Next, you should get your hands dirty on Virtual Environments and how to use Jupyter Notebooks/Labs effectively and efficiently.

Now, you are well equipped to go to take the next big step in your journey to learn Artificial Intelligence.

Learn Data Preprocessing

Now that you can manipulate data, it’s time to learn different techniques that will help you to convert unstructured data into structured data so that you can gain insights from it by applying Machine Learning algorithms. Such methods include –

• Principal Component Analysis.
• Dimensionality Reduction.
• Normalization.
• Data Scrubbing, handling missing values, etc.
• Unbiased estimators.
• Features extraction.
• Denoising and sampling.

These techniques will help you to organize your data to perform further analysis. From here, you have 3 directions to go – Machine Learning, Data Scientist, and Data engineering.

 

Artificial Intelligence Syllabus of Bachelor’s of Computer Engineering Tribhuvan University, Nepal

Course Objectives: To provide basic knowledge of Artificial Intelligence and the knowledge of Machine Learning, Natural Language, Expert Systems and Neural Network.

1. Goals in problem-solving: (6 hours)

1.1. Goal schemas, use in planning,

1.2. Concept of non-linear planning, Means–end analysis

1.3. Production rules systems,

1.4. forward and backward chaining,

1.5. Mycin-style probabilities and its application.

 

2. Intelligence ( 5 hours)

2.1. Introduction of intelligence

2.2. Modeling humans vs. engineering performance

2.3. Representing intelligence using and acquiring knowledge

3. Knowledge Representation ( 6 hours) 

3.1. Logic

3.2. Semantic networks

3.3. Predicate calculus

3.4. Frames

 

4. Inference and Reasoning ( 6 hours)

4.1. Inference theorems

4.2. Deduction and truth maintenance

4.3. Heuristic search State-space representations, game playing

4.4. Resoning about uncertainty Probability, Bayesian networks

4.5. Case-based Resoning

 

5. Machine Learning ( 8 hours)

5.1. Concepts of learning (based on Winston)

5.2. Learning by analogy, Inductive bias learning

5.3. Neural networks

5.4. Genetic algorithms

5.5. Explanation based learning

5.6. Boltzmann Machines

6. Application of artificial intelligence ( 14 hours)

6.1. Neural networks:

6.1.1. Network Structure

 

6.1.2. Adaline, Madaline

6.1.3. Perceptron

6.1.4. Multi-layer Perceptron

6.1.5. Radial Basis Function

6.1.6. Hopfield network, Kohonen Network,

6.1.7. Elastic net model, back-propagation

6.2. Expert Systems

6.2.1. Architecture of an expert systems

6.2.2. Knowledge acquisition, induction

6.2.3. Knowledge representation, Declarative knowledge, Procedural knowledge

6.2.4. Knowledge elicitation techniques, Intelligent editing programs

6.2.5. Development of expert systems

6.3. Natural language Processing

6.3.1. Levels of analysis: Phonetic, syntactic, semantic, pragmatic

6.3.2. Machine Vision: Bottom-up approach, edge extraction, line detection, line

labeling, shape recognition, image interpretation, need for top-down, hypothesisdriven

Approaches.

 

References Books:

1. E. Rich & K. Knight, Artificial Intelligence (2nd ed.), McGraw-Hill, 1991
2. Haykin: Neural Networks: A Comprehensive Fundamentals, Macmillan, 1994
3. E. Turban, Decision Support and Expert Systems, Macmillan, 1993
4. R. Shingal, Formal Concepts in Artificial Intelligence, Chapman & Hall, 1992
5. G. Gazadar & C. Mellish, Natural Language Processing in Prolog: and introduction to computational linguistics, Addison-Wesley, 1989

6. D. Crookes, Introduction to Programming in Prolog, Prentice Hall, 1988.
7. P. H. Winston, Artificial Intelligence (2nd ed.), Addison-Wesley, 1984
8. Beale & Jackson: Neural Computing, Aam Higler, 1990
9. Hecht-Neilson: Neurocomputing, Addison-Wesley, 1990
10. G. F. Luger & W. A Stubblefield, Artificial Intelligence, Benjamin Cummings, 1993

 

Laboratory Exercises:

1. Design and implementation of Expert system in problem solving
2. Lab work should cover the design and development of artificial intelligence using the

LISP and Prolog software.

3. Laboratory exercises must be designed to develop Search, Inference including forward

and backward chaining in Object-Oriented Language.

 

Select Online Resources:

Artificial Intelligence for Beginners – A Curriculum
https://microsoft.github.io/AI-For-Beginners/

Andrej Karpathy’s videos and courses.
https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThs…

Dive into Deep Learning – (Zhang et al) is a very nice book with the right amount of theory and tons of code.
https://d2l.ai 

How to learn Artificial Intelligence as a Beginner in 2023
https://hackr.io/blog/how-to-learning-artificial-intelligence

Getting started in AI: 2018
https://uxplanet.org/getting-started-in-ai-2018-3db33d54e784

Artificial Intelligence 101: How to get started
https://www.hackerearth.com/blog/developers/artificial-intelligence-101-how-to-get-started/

I hope this guide will help you get started in Artificial Intelligence and ML. Please leave a comment about any other beginner friendly resource you might have come across.

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