10 Interesting and Impressive AI projects for absolute Beginners (with Python Source Code)

In this post, you'll find 10 popular Artificial Intelligence project ideas to get hands on AI experience for absolute beginners.

Louis Klimek
Louis Klimek

Artificial Intelligence has become certainly part of our lifes now. We knowingly or unknowingly use it in our day-to-day life as in recommended films, image recognition, speech recognition, sites-recommended products etc.

That's why you also need to start learning about it. You could start by checking out the 5 Best Artificial Intelligence Books in 2020. Yet it's not enough to understand just the Theory. That's why students are expected to try to complete some artificial intelligence projects. That is why, if you're a newbie, the best thing you can do is to spend some time on some real Artificial Intelligence projects. From trying to follow the trends of artificial intelligence to doing some of your own projects. A link to the Python Source Code will be included for each!

I will show you some fun ideas for Artificial Intelligence projects that beginners can work on to test their knowledge of Python.

These projects will help you develop your skill set while also checking your existing knowledge. Artificial intelligence can be used in a number of fields. The more you look at various Artificial Intelligence projects, the more you will learn.


1. Predict Housing Price

Housing prices are an excellent determinant of the health of an economy, and house price ranges are of great concern to both sellers and buyers. Housing prices will be determined using a number of different indicators covering several facets of residential housing.

Historical pricing data is usually used to obtain average prices. In addition to the prices of different homes, you can use extra data sets that include the rate of crime in the city, the location of non-retail businesses and the age of citizens. It's a perfect project for beginners to test their skills.

This is a good tutorial that uses Linear Regression to predict house prices, including the Python Source Code, of course!


2. Stock Price Prediction

Artificial Intelligence has been helping stock market investors for some time now. Financial institutions and investment managers have recently sought to incorporate Machine Learning and Artificial Intelligence into their operations in order to provide them with competitive advantages. AI-based trading desks are now popular for trend modelling, looking for undervalued stocks and trying to make sense of big data.

That is why this is an awesome idea for complete beginners for their first Artificial Intelligence project. You'll love the stock market, because it's full of information. You can get various types of data sets, and instantly start working on a project.

To outsiders, the change of the stock market may look like the open sea with waves going up and down. Forecasting of prices and activity on the stock market is a very complex and demanding job. This includes interpreting the course of the economy, assessing sector-specific patterns, and shifting stock prices within the financial markets themselves. This is where the Artificial Intelligence could come in handy.

Algorithmic trading has now gained a large share in global financial markets, with more than 60 percent of algorithm-based trading being carried out. Pension funds, mutual funds, hedge funds , insurance companies, retail investors and major institutional trading companies use algorithms to trade in equity markets. Therefore, students planning to work in the finance sector should definitely have their own little "Aladdin" on their curriculum vitae ("Aladdin" is BlackRock's AI for managing risk of investment decisions). The stock market feedback period is also short, and helps to support your predictions.

To get Started check out this tutorial where Python and Neural Networks are used to predict the stock price of Apple Inc.


3. Chatbot

A chatbot is an artificial intelligence-driven bit of software (Alexa, Google Assistant, etc.), application, website or other networks that attempt to assess the customers ’ needs and then help them in performing a specific task, such as a transaction, hotel booking, submission form, etc. Currently Chatbots are commonly used in industries where every firm needs a chatbot to reduce some of the processes of customer contact. Some of the ways chatbots are used by companies are to:

  1. Customer support
  2. To deliver information

That's why setting up a chatbot is one of the best AI-based projects. You should start by building a customer service chatbot. You may draw ideas from the chatbots that are on popular sites. After a basic chatbot has been developed you can upgrade it and build a more advanced version of the same.

Here's a good video of our beloved and well-known Siraj Raval where he uses Tensorflow and Recurrent neural networks to create a Chat Bot.


4. Spam E-Mails Identifier

We get hundreds of emails every day, and most of them are spam (also known as junk mail). By the way, the expression comes from the Flying Circus skit of Monty Python in 1970. All the menu items for the restaurant are SPAM through this skit. Detecting spam emails is a common topic in natural language processing (NLP), so try creating an artificial intelligence to identify emails as spam or not spam based on the email content.

To get started, check out this Video Tutorial where you can use the Python source to create your own Spam E-Mail detector.


5. Handwritten Digits Recognition

Numbers written by humans differ greatly in sizes and shapes as they are drawn by hand. Creating a algorithm for identifying digits that can recognise human-drawn numbers is a great way to begin your journey into artificial intelligence. It is even that beginner friendly that some People challenged themselves to do it in under 5 Minutes!

Check out this video to get some Source Code and a detailed explanation of what the Python Code actually does.

Furthermore, if you want to improve the Model, using more data is one of the best ways to do so. One method that has recently gained popularity is through Synthetic Data (or Artifical Data).

In a nutshell, Synthetic Data is Data that you create by applying transformations to your current Dataset, such as color changes, rotations, or similar. One tool you could use for this is NanoML, which allows you to upload your dataset, select some effects, and then download your new bigger dataset to train your model again.

(BTW: I also created this tool, so this was a minor ad placement, but that doesn't diminish the fact that it is still useful.)


6. Chrome T-rex Dino Bot

Dino is a popular Google Chrome game that you can play when you're not connected to the Internet. You should implement reinforcement-learning and it will be easy to understand how it works because of the simplicity of the game. You can build the AI, which is taught by making mistakes.

CodeBullet made a video where he did exactly that. You can check it out here, and you can find the Python Source Code here.


7. Next Word Predictor

When you type a message your phone automatically predicts the next word you want to use, you may even use it every day when you write messages without knowing it. But how does your phone's software know what you'd like to type next? Natural Language Processing (NLP)! You can create an artificial intelligence model that can predict the next word that is most likely to come next. It is currently probably one of the primary tasks of NLP and has a lot of applications.

A really good article in which the Python Code is also included and explained step by step can be found here.


8. Twitter Sentiment Analyzer

Emotional analysis, also referred to as Sentiment Mining or Emotion AI, is the method used to determine whether a type of writing is positive , negative or neutral. A typical scenario for the use of this technology is to discover how the general public feels about a specific subject. As media platforms such as twitter are an open sea of big data, mining such data can be useful for understanding user thoughts and points of view in a variety of ways. Being able to calculate this kind of feeling around a number of issues will provide an excellent idea of what will happen to it. This can be used for a variety of purposes, such as predicting stock prices.

I personally really liked this tutorial, where the code is explained better than any book could ever.


9. Cancer Detection using medical data

Artificial Intelligence thrives in the recognition of trends in vast amounts of data, the distinction between larger data characteristics and the discovery of data characteristics that can not be recognised by the human mind. Automation of metastatic cancer detection in deep neural pathology scans is a field of medical diagnosis with a promising potential for clinical use.

The integration of Artificial Intelligence into cancer treatment could improve the quality and accuracy of the diagnosis, help clinical decision-making and lead to improved patient outcomes. Artificial intelligence clinical care, especially in low-income settings, has the potential to play a significant role in increasing health inequalities.

You can find Python Source code and a good explanation in this Article which also Includes a Video if you prefer that.


10. Facial Emotion Recognition and Detection

In the 21st century, facial detection technology has become inevitable. Deep Learning systems for detecting and interpreting emotions are designed to recognise and interpret human facial expressions. They are able to detect core human emotions such as sad, happy, angry, neutral, disgust , fear and surprise. The key feature of the facial emotion detection and recognition system is that it can evaluate emotions, differentiate between good or bad feelings and classify them properly. It can also use tagged emotion information to identify a person's thinking behavior patterns.

It is currently one of the most popular artificial intelligence projects. Although facial emotion identification has been the subject of research and study for a long time, it is only now that we are seeing concrete results of this research.

You can find a good tutorial that includes the Python source code to get started here. Both Python and Keras are used in this tutorial to classify emotions.

And, as I previously noted in Point 5, you could generate some synthetic data to improve your Model with a larger Dataset by using my Tool called NanoML.org, which allows you to apply transformations to your current Dataset, such as color changes, rotations, or similar. This way, you can obtain massive amounts of data with even more Edge Cases without putting in a lot of extra effort collecting and curating the Dataset.