Artificial Intelligence (AI) is dramaticly changing our lives. Giants such as IBM, Accenture, & Apple are investing heavily in the field, & AI engineers make a median salary of $171,715 – some even up to $250,000. You don’t need extensive knowledge of math or machine learning to get started in this industry.
Instead, you can work with frameworks or libraries tailored to AI engineering. To prove your expertise in AI & catch the attention of employers, create projects for your portfolio or contribute to open-source AI projects; this will help land you an exciting job!

Careers in Artificial Intelligence Today

Data Science, Machine Learning Engineering & Research Science are all in-demand AI careers. To do well in these fields, you need to be familiar with various AI tools & have certain skills. We will go into more detail about the necessary tools later.

Data scientist

Data Scientists use their analytical, statistical & programming skills to investigate data from an organization & find meaningful insights. They can then explain these insights in a way that is understandable to people who are not familiar with technical terms.

To gain new knowledge from large amounts of information, Data Scientists use coding tools & big data technologies.

Machine learning algorithms are being developed to add more value & automate business processes.


  • Proficiency in Python, R, & SQL.
  • Understanding of concepts related to Machine Learning & AI.
  • Skill in using statistical analysis, quantitative analytics, & predictive modeling.
  • Techniques for Visualizing & Reporting Data
  • Ability to communicate & present effectively.


  • Data analysis, such as Pandas & NumPy.
  • Machine learning libraries include Scikit-learn.
  • Data visualization tools include Matplotlib & Tableau.
  • Big data frameworks include Airflow & Spark.
  • Command line tools (EX: Git & Bash).

Machine learning engineer

Machine learning engineers use coding-based tools to create systems that analyze data & make predictions.
These systems help predict customer retention & long-term value, & can be used by organizations.


  • Proficiency in Python, Java, & Scala.
  • Machine learning frameworks (EX: Scikit-learn, Keras, or PyTorch).
  • Understanding data organized, modeled, & software design.
  • Proficiency in linear algebra, calculus, &statistics.
  • Ability to collaborate & excellent problem-solving abilities.


  • Machine learning libraries & algorithms include Scikit-learn & TensorFlow.
  • Data science libraries include Pandas & NumPy.
  • Cloud platforms (EX: Amazon Web Services (AWS) & Google Cloud Platform).
  • Version control systems (EX: Git), used to track changes to files.

Research scientists

Research scientists are the driving force behind the development of Artificial Intelligence (AI). They are responsible for inventing new algorithms & improving existing ones to move AI forward. They also attend conferences & publish their findings in academic journals so that their work can be shared with others.


  • knowledge of machine learning & deep learning.
  • Skilled in Python & Programming languages.
  • Knowledge of mathematical theory related to AI (EX: Statistical Learning Theory).
  • Create & Check new AI models.
  • Strong writing & public speaking skills


  • Frameworks for deep learning (EX: TensorFlow & PyTorch).
  • Scientific computation tools (EX: MatLab & Mathematica).
  • Software for creating documents & presentations (EX: LaTeX & Google Slides).
  • Cloud computing resources (EX: Amazon Web Services & Google Cloud Platform)

Choosing a career in AI is an exciting decision.

Depending on your interests, skills, & ambitions, you may decide to pursue one of the following roles:

  • Machine Learning Engineer
  • Data Scientist
  • AI Researcher
  • Robotics Engineer or Computer Vision Scientist

Each of these roles offers its own unique opportunities & challenges.

What are some good Artificial Intelligence projects for beginners?

– An object Tracking System is a system that can detect multiple objects in an image. Kaggle’s Open Images Object Detection dataset can be used for this project. SSD (Single Shot Detector) is an open-source object detection tool that can be employed for this task.

– A Fake News Detector can be created using the Real & Fake News dataset on Kaggle. This classification can be done using a pre-trained machine learning model called BERT, which is a free & open-source Natural Language Processing (NLP) model. To use this model for text categorization, you need to load BERT into Python & add an additional output layer.

– Predicting an animal’s species is an AI computer vision project that can be done using the Animals-10 dataset on Kaggle. The VGG-16 pre-trained model & the Keras library can be used to import this model into Python for a multi-class classification task.

Why do AI Projects fail?

Reasons for the Failure of AI Projects:

  • Companies should make sure that their data is reliable, accurately labeled, & suitable for the AI tool they are using before beginning an AI project.
  • For a successful AI project, it is essential for data scientists, data engineers, IT professionals, designers, & line of business workers to collaborate as a team.
  • Companies need a team with the right training & business expertise to make the most of AI.

Projects with AI: Tools & Resources for Beginners

Let’s explore 20 Artificial Intelligence projects you can build to showcase on your resume. These projects vary in difficulty – from beginner to intermediate to advanced. If you’re new to AI, it’s recommended to start with simple projects & work your way up as your skills progress. Here are some project ideas for those interested in learning AI concepts.

Resume Parser AI Project

Recruiting can be automated by scanning resumes for keywords that help recruiters identify the best candidate. However, this process has its drawbacks as many candidates insert as many keywords in their resumes to get shortlisted.

To make resume screening more efficient, an AI-based resume parser can be built using the Resume Dataset from Kaggle which contains job title & resume information.

Using NLTK Python library, the data can be pre-processed & a clustering algorithm used to group similar words & skills associated with each domain. This will generate a score for each resume from 0 (least favorable) to 10 (most favorable). Building this model is a great beginner project to learn about AI.

A Model for Generating Text

In this project, you can use OpenAI’s GPT-2 model to create a deep learning model that can generate text. It can complete a sentence given the first few words as a writing prompt. You can use this model to write stories or funny text messages, & access it in Python by cloning their GitHub repository.

The output generated by GPT-2 may not always make sense, but you can use it to recreate your favorite stories or articles. You can also turn this into an app quickly with just some lines of code -the user enters a prompt & an article generated by GPT-2 is displayed.




What is the best way to learn Artificial Intelligence?

Learning AI requires a foundation of knowledge in the fields of mathematics & computer science such as linear algebra, calculus, probability, statistics, coding & data structures. Once these basics are mastered, individuals can delve into more advanced topics like machine learning mechanics, deep learning architectures & natural language processing. To stay up to date with the latest developments in AI technology it is important to actively work on projects, engage with online communities & read research papers.


Is it possible for me to study Artificial Intelligence on my own?

Self-studying AI is possible with the help of online resources, such as courses, tutorials, books, & YouTube channels. It requires dedication & discipline in order to succeed since it involves a difficult learning process that necessitates a strong knowledge of mathematics & programming.


Is it possible to learn AI in three months?

Learning Artificial Intelligence is a long & challenging process. It requires dedication, patience & hard work in order to stay up-to-date with the latest developments & technologies in the field. It is important to continually practice, learn, & evolve over time in order to succeed.


Should I start with Artificial Intelligence or Machine Learning?

It is recommended to start with Machine Learning before progressing to Artificial Intelligence, as Machine Learning is a component of AI. ML provides the basis for understanding more complex AI topics such as deep learning, natural language processing, & more.


Do you need to know a lot of math for Artificial Intelligence?

AI involves a lot of math, including linear algebra, calculus, probability, & statistics. It is important to have a good understanding of these concepts in order to use AI effectively.

Project to Detect Fake News Using AI

Fake news is false or misleading information circulated as if it were true. It can be difficult to recognize the difference between authentic & fabricated stories, which can create dangerous situations during voting cycles or pandemics.

To avoid harm to people & society, fake news must be identified & addressed quickly before it spreads widely.
This project will focus on building a fake news detector using the Kaggle Real & Fake News dataset with the BERT pre-trained machine learning model. BERT is an open-source Natural Language Processing (NLP) model that requires only one additional output layer to classify text.

Creating a Telegram Bot

The Telegram API can be used to create a Telegram bot with Python. To get started, you need to get an API key from the BotFather Telegram account.

After obtaining the API key, you must install the telegram package & create a “Hello World” program for your bot. When activated, your chatbot will reply with “Hello, World” when given this command.

An App for Translating

If you are looking to get started in Natural Language Processing, try building a translator app with the help of a transformer. A transformer is an AI model that extracts features from sentences & determines the importance of each word. It has an encoding & decoding component which both can be trained end-to-end.
To create your translator app, you need to load a pre-trained transformer model into Python & transform the text you wish to translate into tokens. The GluonNLP library can be used for this task, where datasets such as train & test data can also be found.

Identifying Fake Product Reviews

This project involves developing an AI system to detect fake reviews for products. Kaggle offers a dataset called Deceptive Opinion Spam Corpus that contains 1600 hotel reviews — 800 positive & 800 negative — which have already been labelled.

To make use of the data, some data pre-processing & tokenization is required before training a model using transfer learning with pre-trained models like BERT, RoBERTa & XLNet.

Detecting Spam on Instagram

Many Instagram posts have comments filled with bots, which can be annoying or even dangerous. To help identify spam comments from legitimate comments, an AI-based spam detection model can be built. A dataset such as Kaggle’s YouTube Spam Collection can be used to train the model.

Keywords & algorithms like N-Gram & Cosine Similarity can then be used to compare scraped web comments & weigh words that tend to appear in spam messages. For better results, using a pre-trained model such as ALBERT is recommended, since it takes sentence context into consideration when making comparisons.

Predicting Animal Species

You can use a pre-trained model called VGG-16 to predict the species of an animal based on its picture, using the Animals-10 dataset from Kaggle. This is a multi-class classification problem & VGG-16 is a Convolution Neural Net (CNN) architecture trained on ImageNet, with over 14 million images.

Using Keras library, you can load the model into Python & then train it using the labelled images from Kaggle so that it can classify among ten different types of animals.

Auto-Correction Tool

Autocorrect is an AI application we use everyday to help us with typos & grammatical errors.
To build this into a Python program, you can use the TextBlob library which has a ‘correct()’ function to identify wrong words & replace them with the closest correct word.

This algorithm may miss certain words that are spelled correctly but don’t fit in the context of the sentence.
For example, if you wrote ‘I like your short’ instead of ‘I like your shirt’, it won’t pick up on this mistake.
To improve this model’s accuracy, you can create your own using pre-trained NLP models such as BERT that are able to recognize words fitting a specific context more easily.

Using Python to Detect Pneumonia

Many diseases such as cancer, tumors & pneumonia can be detected using computer-aided diagnosis that is powered by AI models. A great dataset for disease prediction is Kaggle’s Chest X-Ray Images (Pneumonia Detection) dataset. This dataset contains labels of Normal, Bacterial Pneumonia & Viral Pneumonia lung X-ray images.

To build a program to categorize a patient’s health condition into one of these three categories based on the X-Ray image, FastAI can be used to create & train deep learning models quickly. The ResNet50 pre-trained model from FastAI allows us to train very deep neural networks with over 150 layers, giving good results when trained on top of it.

A System that Detects Objects

An object detection system can use computer vision techniques to identify different objects in an image. For example, if there is a picture of you working on a laptop, it should be able to label you (human) & the laptop, as well as your position in the image.

Kaggle’s Open Images Object Detection dataset can be used for this project, along with an open-source pre-trained object detecting model called SSD which was trained on COCO dataset. By further training this model’s output layer with Kaggle Open Images dataset, an accurate object detection system can be built.

Teachable Machine

Google’s Teachable Machine is a web-based tool designed to make machine learning accessible to everyone. To use it, you can upload roughly 100 pictures of different classes (e.g. yourself & your cat), label them, then click “Train Model” so that the machine can learn how to distinguish between them. This way, non-technical people can get comfortable with AI technology & even build their own version of Google’s Teachable Machine.

To take the necessary steps, do the following:

  • Create an application that lets users upload images which belong to different categories.
  • Collect images, transform them using JavaScript, & train them on a pre-trained model. You can use libraries like ml5.js & tensorflow.js to access pre-trained machine learning models in JavaScript.
  • Once the model has been trained, a notification will be displayed on the screen to let the user know. Afterwards, the user must upload images of each class in order to make predictions for new images.


Learning AI can be rewarding and help you gain new career opportunities. To get the most out of it, it is recommended to take courses & do AI projects to build problem-solving, critical thinking, & creative skills.
If you are just starting to learn about Artificial Intelligence, there are many resources available that can help guide your learning. A suggested plan is also provided that covers key topics for learning AI.