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Artificial Intelligence Project Ideas for 2020 to Become an AI expert

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Artificial Intelligence Project Ideas for 2020 to Become an AI expert

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Artificial intelligence is a very frequent topic for movies, books, and all other media.

Did you ever wish you could make an AI of your own? Well, this article is the best way to get started.

The term Artificial intelligence is used to define machines or computers that mimic the functions of the human mind such as learning and problem-solving.

Natural language processing, computer vision, and decision-making are some of the functions expected of an advanced artificial intelligence program.
WAIT!!
Do you often find yourself short of really cool project ideas to work upon? Okay, not anymore.

Because we care for you and have compiled a list of over 500+ project ideas. Quickly bookmark this and dedicate all your time in implementing the projects & become industry-ready.

1. Python Project Ideas

2. Python Django (Web Development) Project Ideas

3. Python Game Development Project Ideas

4. Python Artificial Intelligence Project Ideas

5. Python Machine Learning Project Ideas

6. Python Data Science Project Ideas

7. Python Deep Learning Project Ideas

8. Python Computer Vision Project Ideas

9. Python Internet of Things Project Ideas

Now, we are going to start with the easier ones first and build up the difficulty level from there. So let's get started with some basic artificial intelligence project ideas.

Artificial Intelligence Project Ideas for Beginners for 2021

1. Handwritten Digits Recognition

AI Project Idea - Digits written by humans vary a lot in curves and sizes as they are hand-drawn and everyone's writing is not the same.

It is a great way to start artificial intelligence by building a handwritten digits recognition system that can identify the digit drawn by humans.
Source Code: Handwritten Digits Recognition Project
2. Spoiler Blocker Extension

AI Project Idea - When a good movie or show comes, people always spoil the fun of others by spoiling it.

We can create a browser extension that will block out all the mentions of your favourite show that you don't want to get spoiled.

You can replace the mentions with a cute picture of cats.

3. Lane Line Detection

AI Project Idea - Lane line detection technique is used in many self-driving autonomous vehicles as well as line-following robots.

We can use computer vision techniques such as color thresholding to detect the lanes.

4. Spam Classifier

AI Project Idea - Every day we get dozens of email notifications and most of them are spam.

Build a tool that can classify the emails as spam or non-spam based on the content of the email alone.

5. Optimal Path

AI Project Idea - One of the challenging tasks of AI is to find the optimal path from one place to the destination place.

The project idea is to find the optimal path for a vehicle to travel so that cost and time can be minimized. This is a business problem that needs solutions.

Intermediate Artificial Intelligence Project Ideas for 2021

1. Artificial Intelligence in Pneumonia Detection

AI Project Idea - Doctors use Chest X-rays to detect Pneumonia and we can build an artificial intelligence system that is capable of identifying the infection in patients' X-ray images.

For this purpose, we will use Convolutional Neural networks to build the AI system.

2. Game of Chess

AI Project Idea - The game of chess is very popular and for a better experience of this game, we must implement a good artificial intelligence system that can give competition to humans and make the game of chess a challenging task.

3. Fire Detection and Localization Using Surveillance Camera

AI Project Idea - CNN has become the state of the art in image classification and computer vision-related tasks.

We can improve the fire detection system through surveillance cameras by building a model that can not only detect the fire but also the location of the fire to provide effective detection and reporting system for the safety of people.

4. Website Evaluation Using Opinion Mining

AI Project Idea - A website evaluation system where users can comment on a particular website about the genuineness, time taken to deliver products, etc.

It will analyze the comments to rate the website on these factors.

5. T-rex Dino Bot

AI Project Idea - The Dino is a famous google chrome game that we can play when the internet connection is off.

It is a good game to implement reinforcement-learning and understand how it works.

We can build an algorithm for the bot that will learn itself by making mistakes.

6. Next word predictor

AI Project Idea - When you type a message your phone can automatically predict the next word you want to type.

We can build an artificial intelligence model that can predict the next word that is most likely to come.

To implement this you need to have knowledge about Natural language processing and deep learning.

7. Chatbot using AIML

AI Project Idea - Chatbots are widely used in the industry level where every company requires a chatbot to automate some of the customer interaction processes.

AIML is an Artificial Intelligence Markup Language that is used for building chatbots.
Source Code: Chatbot Project
Advanced Artificial Intelligence Project Ideas for 2021

1. Fake Product Review Monitoring System

AI Project Idea - A big problem on the internet is that companies post fake reviews to sell their products or to disturb their competitors.

A user has no idea if a product review is fake or genuine.

To solve this problem, we can build a system that can track the IP addresses and use opinion mining to find out the fake reviews.

2. Learn to Drive with Reinforcement Learning

AI Project Idea- Build a path for cars with obstacles on the race-course.

The objective of the car(agent) is to learn how to drive by avoiding the obstacles. The concept of reinforcement learning would be used in this project.

3. Automatic Attendance System

AI Project Idea - In schools and colleges, a lot of time is wasted in taking the attendance of the students.

The idea of the project is to automate the attendance system by using a camera that automatically recognizes the faces and marks the attendance of the people.

4. Price Negotiator Ecommerce Chatbot System

AI Project Idea - A chatbot system that can negotiate the price of the products.

Many eCommerce companies are researching on this project as it has a lot of scope in business.

The chatbot should be able to communicate with users and handle the bargaining of customers just like in the real world.

5. AI Bot to Play Snake Game

AI Project Idea - Many games need to implement an artificial bot that can compete with humans.

In this project, you can start by building a bot that can learn to play the snake game.

This project is great to dive into genetic algorithms and understand how a machine can evolve understanding with generations.

6. Self-Driving Car

AI Project Idea - A self-driving car is a huge project that involves a lot of sensors and cameras to obtain information of the surroundings.

Then the information needs to be processed and effective decisions should be made.

7. Music Recommendation App

AI Project Idea - The Spotify app is a great music streaming platform that knows exactly what type of music they like.

You can learn to build a model that will analyze the users' music tastes and will recommend new music to them based on their interests.

Summary

So now, you have so many interesting Artificial Intelligence Project Ideas for 2021.

These projects will help you to whet your skills in AI. Also, these projects will not only ensure that you're on the path of becoming an expert in AI but will also make you industry-ready.

Anyone can practice these really cool artificial intelligence projects whether you're a beginner or you're already working in the AI field.

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Artificial Intelligence Project Ideas for 2021 - DataFlair

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Artificial intelligence is a very frequent topic for movies, books, and all other media. Did you ever wish you could make an AI of your own? Well, this article is the best way to get started. The term Artificial intelligence is used to define machines or computers that mimic the functions of the human mind such as learning and problem-solving.

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Online Course: Introduction to Artificial Intelligence with Python

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Online Course: Introduction to Artificial Intelligence with Python

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AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career.

CS50's Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever, CS50. You'll learn the theoretical frameworks that enable these new technologies while gaining practical experience in how to apply these powerful techniques in your work.

More about this course

graph search algorithms

adversarial search

knowledge representation

logical inference

probability theory

Bayesian networks

Markov models

constraint satisfaction

machine learning

reinforcement learning

neural networks

natural language processing

Expand what you'll learn

Pursue a Verified Certificate to highlight the knowledge and skills you gain

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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CS50's Introduction to Artificial Intelligence with Python

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AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly.

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1. Python AI Tutorial

Today, in this Python AI Tutorial, we will take on an introduction to Artificial Intelligence. Moreover, in this Artificial Intelligence Programming, we will see AI Problems, Tools in AI, and Artificial Intelligence approaches.

So, let's start the Python AI Tutorial.

2. What is Artificial Intelligence?

Artificial Intelligence, often dubbed AI, is the intelligence a machine demonstrates. With machine intelligence, it is possible to give a device the ability to discern its environment and act to maximize its chances of success in achieving its goals. In other words, AI is when a machine can mimic cognitive functions like learning and problem-solving.
"AI is whatever hasn't been done yet."

As we said, an AI takes in its environment and acts to maximize its chances of success in achieving its goals. A goal can be simple or complex, explicit or induced. It is also true that many algorithms in AI can learn from data, learn new heuristics to improve and write other algorithms.
Do you know about AI Algorithms
One difference to humans is that AI does not possess the features of human commonsense reasoning and folk psychology. This makes it end up making different mistakes than a human would.

3. Python AI Tutorial - AI Problems

When simulating or creating AI, we may run into problems around the following traits-

a. Reasoning and Problem Solving

Earlier, algorithms mimicked step-by-step reasoning that humans display. AI research later introduced methods to work with incomplete and uncertain information. However, as the problems grew larger, these algorithms became exponentially slower. Humans often use fast, intuitive judgments instead of a step-by-step deduction.

b. Knowledge Representation

Some expert systems accumulate esoteric knowledge from experts. A comprehensive commonsense knowledge base holds many things including- objects, properties, categories, relations between objects, situations, events, states, time, causes, effects, knowledge about knowledge, and other domains. When we talk about ontology, we talk about what exists. Under knowledge representation, we observe the following domains-
Have a look at Robotics and Artificial Intelligence

Default reasoning; Qualification problem

The breadth of commonsense knowledge

The subsymbolic form of some commonsense knowledge

An intelligent agent should be capable of setting goals, achieving them, and visualizing the future. Assuming it is the only system in the world, an agent can be certain of their actions' consequences. If there are more actors, the agent should be able to reason under uncertainty. For this, it should be able to assess its environment, make predictions, evaluate predictions, and adapt according to its assessment. With multi-agent planning, we observe multiple agents cooperate and compete to achieve a goal.

AI is related to Machine Learning in some way. We have often talked about unsupervised learning- the ability to take a stream of input and find patterns in it. This includes classification and numerical regression. We classify things into categories and produce a function that describes how inputs and outputs relate and change each other. These function approximators.

e. Natural Language Processing

NLP is an area of Computer Science that gives machines the ability to read the human language and understand it. With it, we can retrieve information, mine text, answer questions, and translating using machines. We use strategies like keyword spotting and lexical affinity.

f. Perception

With machine perception, we can take input from sensors like cameras, microphones, and lidar to recognize objects. We can use it for applications like speech recognition, facial recognition, and object recognition. We can also analyze visual input with Computer Vision.

g. Motion and Manipulation

With AI, we can develop advanced robotic arms and more for modern factories. These can use the experience to learn to deal with friction and gear slippage. The term Motion Planning means dividing a task into primitives like individual joint movements.

h. Social Intelligence

"Should I go to bed, Siri?", I ask as I reach home from a busy day. "I think you should sleep on it", Siri quips back. Affective Computing, an umbrella term, encompasses systems that can recognize, interpret, process, or simulate human affects/ emotions. In this domain, we have observed textual sentiment analysis and multimodal affect analysis. The aim is to allow AI to understand others' motives and emotional states to predict their actions. It can mimic human emotion and expressions to appear sensitive and interact with humans. A robot with rudimentary social skills is Kismet, developed at MIT by Dr. Cynthia Breazeal.
Do you know expert systems in Artificial Intelligence to Solve Problems
i. General Intelligence

Lately, many AI researchers have begun working on tractable narrow AI applications like a medical diagnosis. The future could hold machines with Artificial General Intelligence(AGI) that combines such narrow skills. Google's DeepMind will be an example of this.

4. Python AI Tutorial - Approaches

We observe four different approaches to AI-

a. Cybernetics and Brain Simulation

Some machines exist that use electronic networks to display rudimentary intelligence.

This approach considers reducing human intelligence to symbolic manipulation. This includes cognitive simulation, logic-based, anti-logic or scruffy, and knowledge-based approaches.

c. Sub-Symbolic

For processes of human cognition like perception, robotics, learning, and pattern recognition, sub-symbolic approaches came into picture. This includes approaches like embodied intelligence and computational intelligence and soft computing.
Let's discuss Data Science vs AI vs Machine Learning

d. Statistical Learning

Statistical learning techniques like HMM and neural networks deliver better accuracy in practical domains like data mining. Limitations of HMM include that it cannot model the infinite possible combinations of natural language.

5. Python AI Tutorial - Artificial Intelligence Tools

In this section of the Python AI Tutorial, we will study the different tools used in Artificial Intelligence:

a. Search and Optimization

To intelligently search through possible solutions and use reasoning to do so is a tool for AI. For real-world problems, simple exhaustive searches rarely suffice. This is because these have really large search spaces. This leads to a slow search or one that never ends. To get around this, we can use heuristics.

AI research uses different forms of logic. Propositional logics use truth functions like 'or' and 'not'. The fuzzy set theory holds a degree of truth (values between 0 and 1) to vague statements. First-order logic adds quantifiers and predicates. Fuzzy logic helps with control systems to contribute vague rules.

c. Probabilistic Methods for Uncertain Reasoning

We often use tools like Bayesian networks for reasoning, learning, planning, and perception. We can also use probabilistic algorithms to filter, predict, smoothen, and explain streams of data.

d. Classifiers and Statistical Learning Methods

Classifiers and controllers work together. Consider an object. If it is shiny, the classifier knows it is a diamond. If it is shiny, the controller picks it up. But before inferring an action, a controller classifies conditions. As a function, a classifier matches patterns to find the closest match. Supervised learning puts each pattern into a predefined class.

e. Artificial Neural Networks

ANNs are collections of nodes that are interconnected- inspired by the huge network of neurons in the human brain.

Under these, we have categories like feedforward neural networks and recurrent neural networks. We will take up ANNs as a separate topic in another tutorial.

f. Evaluating Progress

Since AI is general purpose, there is no way to find out which domains it excels in. Games are a good benchmark to assess progress. Some of these include AlphaGo and StarCraft.

6. Python AI Tutorial - Applications of Artificial Intelligence

Like we said, AI is pretty general-purpose. Here are a few domains it finds use in-
Follow the link to learn more about AI Applications
So, this was all in Python AI Tutorial. Hope you like our explanation.

7. Conclusion

Hence, in this Python AI Tutorial, we learned about artificial intelligence. We talked about its problems, approaches, tools, and applications. That's all for today; we'll be back with more. Tell us about your experience of Python AI Tutorial in the comments.
Reference

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Python AI Tutorial | Artificial Intelligence Programming Python - DataFlair

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Today, in this Python AI Tutorial, we will take on an introduction to Artificial Intelligence . Moreover, in this Artificial Intelligence Programming, we will see AI Problems, Tools in AI, and Artificial Intelligence approaches. So, let's start the Python AI Tutorial. Artificial Intelligence, often dubbed AI, is the intelligence a machine demonstrates.

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Through this AI tutorial, you will learn about the Best Books on Artificial Intelligence for beginners to experts that will help you to become professional. You will learn about various artificial intelligence books that are suggested by experts. Here, you will get the best books to learn artificial intelligence.

So, let's start the Best Artificial Intelligence Books Tutorial.

Best Artificial Intelligence Books

This is the list of top recommended Artificial Intelligence book by AI experts.

1. Artificial Intelligence - A Modern Approach (3rd edition)
by Stuart Russell & Peter Norvig

The book is an introduction to AI and is one of the best Artificial Intelligence Books for beginners or non-programmers. They can easily understand the explanations and concepts. Moreover, advanced AI topics are covered but haven't been explained in depth. Further, it teaches to build intelligent systems using various real-life examples.

This non technical book provides a simple description of major concepts, detailed coverage of topics like multi-agent systems, constraint satisfaction, statistical natural language processing, local search planning methods, game theory, and not certain reasoning over time, The book also gives proper explanation of algorithms for probabilistic learning approaches including EM, fast propositional inference, and probabilistic inference etc

2. Artificial Intelligence: A Modern Approach (2nd Edition)
by Stuart Russell & Peter Norvig

If you have opted a course from Norvig to understand his style of teaching, you will long for it! This is the best book for newcomers for A.I. Also, it covers subjects from the search algorithm, working with logic to more advanced topics. Moreover, make this book your first choice for AI.

The book focuses on the generation of particular key knowledge components and the development of AI systems over time. The author focuses on concepts like text processing, statistical learning, philosophical foundations, reinforcement learning, robotics, and perception.

This edition covers many advance theories and non-technical learning content. It is made in a flexible format to promote different teaching methods.

3. Artificial Intelligence For Humans
by Jeff Heaton

This AI book will help to understand the basic artificial intelligence algorithms. Algorithms like dimensionality, distance metrics, clustering, and linear regression are explained in detail. Interesting examples and cases were used to explain these algorithms. Although, to understand this book you need a basic command of math. Otherwise, you'll require more time to learn the equations.

The book covers all algorithms using real numeric calculations that you perform on your own. The reader needs only a basic knowledge of college algebra and computer programming, topics more complex than that is nicely explained.

4. Paradigms of Artificial Intelligence Programming
by Peter Norvig

Another book by Peter Norvig!

This Artificial Intelligence book will help you to understand the advanced common lisp techniques to build major A.I systems. It is all about practical aspects. Also, it teaches readers the method to build and debug robust practical programs.

Moreover, It gives a better understanding of superior programming style and essential AI concepts. Further, if you are serious about your career, this AI book is best for you.

AI relates to machine learning, therefore read machine learning in detail.

5. Artificial Intelligence: A New Synthesis
by Nils J. Nilsson

This AI book covers topics such as Neural networks, reasoning, and Bayes networks. Also, it explains them with great ease.

The author who is a leading researcher and master expositor provides a fresh and motivating coverage of the field. The book provides the reader, a great tour of this new world of Artificial Intelligence. It gives an evolutionary and unifying theme to the book by covering important AI ideas and repeated use of explanatory diagrams and examples.

Although, I wouldn't recommend this book for a beginner. However, it's a must-read for the advanced level user.

6. The Emotion Machine: Commonsense Thinking, Artificial Intelligence and the Future of Human Mind
by Marvin Minsky

In this Artificial Intelligence book, Marvin describes a fascinating model of how our mind works. He uses tries different forms of mental activity to infer the future of the human mind by examining. Also, a book is great to develop perspective and become aware of a present to a future transition of A.I.

The book shows why we should not limit our ideas about thinking and how thinking itself may modify in the future. It also explains the working of our mind, the process to progress from simple thoughts to more complicated forms that promote us to reflect on ourselves. Many people describe it as self-awareness or consciousness.

7. Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain
by Amit Konar

Both conceptually advanced and accessible knowledge is provided by this book. The content is diverse but complete. The text is clearly written, practical, and thorough.

The text starts with the behavioral opinion of "human cognition". It focuses on the tools and techniques needed for its intelligent realization on the machines. The author covers the symbolic logic, classical aspects-search, planning, and machine learning in a detailed description and also mention the latest research in these areas.

So, this was all about the Artificial Intelligence Books Tutorial. We hope you like our explanation.

Summary

As a result, we have studied Best Artificial Intelligence Books. These Artificial Intelligence books help you choose the best. We hope these 7 books will help you to learn artificial intelligence in the best way. Furthermore, if you feel any query, feel free to ask in a comment section.

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Best Artificial Intelligence Books For Beginners to Expert (Latest) - DataFlair

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Through this AI tutorial, you will learn about the Best Books on Artificial Intelligence for beginners to experts that will help you to become professional. You will learn about various artificial intelligence books that are suggested by experts. Here, you will get the best books to learn artificial intelligence.