Since the 50s when AI first came into the limelight thanks to two computer scientists, Marvin Minsky and John McCarthy who believed in its future, AI’s application has made huge impacts on the way we live our lives and run businesses. Today, most if not all sectors, from IT, manufacturing, retail, finance, to supply chain and more are leveraging AI to anticipate and address issues and opportunities for profitability and growth.
Today, AI is a broad field encompassing deep learning which is a subset of AI and machine learning course in London, artificial neural networks, natural language processing, computer vision, robotics, and others. Whether you are receiving targeted ads on Google search engine or social media, getting recommendations on Spotify, or taking advantage of the autocorrect function on Word or Google docs, this is AI being applied. The AI industry will be worth more than $126 billion by 2025. The world can no longer overlook the influence of AI in its industries.
Artificial intelligence integrates intelligence comparable to that of humans into computers to enable the machines to perform certain tasks previously only undertaken by humans such as problem-solving and learning. Machines are programmed to automatically learn from input data and make different kinds of predictions and classifications.
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Top AI skills
Much is being done today to advance AI and evolve it into a matured technology. This field demands a demonstration of practical skills that can be acquired by undertaking practical AI certification courses and building a strong projects portfolio. The latter also helps professionals to narrow down to a specific specialization. Some top skills that recruiters look for in talented AI professionals include:
- Programming languages like R, Python, C++, c#, Prolog, MATLAB
- Data analysis and data analysis tools like Spark
- Natural language processing
- Machine learning algorithms like linear and logistic regression, decision trees, KNN, K-means, and Naive Bayes
- Artificial neural networks (ANN)
- Knowledge of UNIX and UNIX tools
- Convolution neural networks for image classification, segmentation, and processing
- Domain knowledge
- Soft skills such as communication, teamwork, and analytical thinking
Top Artificial Intelligence Projects for Beginners
Companies are on the lookout for AI professionals who can develop and deploy scalable AI solutions. Starting out in the AI field may seem difficult but it is not if one works on acquiring the necessary skills. You may not start by building ML models from scratch as a beginner, but there are some beginner projects you can undertake to hone your skills. All you need is a good grasp of the popular AI libraries and frameworks.
Here are a few suggestions of projects you can consider working on to build a great portfolio.
- Recommender system
AI specialization: Deep learning
Requirements: Pre-trained machine learning models
Skills: Programming (R, Python, or Java), Artificial Neural Networks, data mining, machine learning
Applications: online eCommerce stores, streaming platforms, search engines,
Examples: Amazon, Spotify, Netflix, YouTube
Are you intrigued by the workings of online user interfaces and how they collect user information and use this to make future personalized recommendations to improve users’ experiences?
To build a recommendation system you will need user browsing history, understand customer behavior, and pre-trained machine learning models. Recommender systems use machine learning models that ingest vast volumes of user data such as clicks, purchases, browsing history, and ratings. This data reveals patterns relating to users’ interests and preferences.
- Lane line detection
AI specialization: Computer vision
Requirements: Deep learning algorithms, OpenCV library
Skills: Computer vision techniques such as color thresholding for lane detection, Python programming language
Applications: Autonomous cars, line-following robots, racing car online games
This project involves developing a system to be deployed for self-driving cars or line-following robots to enable them to detect lane lines in real-time to prevent them from going off in the wrong lane and colliding with other vehicles or robots.
- Handwritten digit recognition
AI specialization: Deep Learning, Convoluted Neural Networks (CNN)
Requirements: Deep learning with Keras library, Python programming, Tkinter library for building GUI, MNIST dataset
Skills: Convoluted Neural Network (CNN) or support vector machine algorithms, Optical character recognition (OCR) technology
Applications: Authentication of bank cheques, reading postal addresses, reading filled forms, number plate recognition, and handwritten note-taking on tablets and iPads.
Handwritten digit recognition is the process by which computers are empowered to read and convert handwritten digits from various sources including images, documents, and screens. These digits are then classified into predefined classes between 0 and 9. This project thus involves building a system that can undertake this process.
- Fake News Detector
AI specialization: Machine learning
Requirements: Fake news dataset on Kaggle, Python programming language
Skills: Natural language processing
Applications: Media companies, Fake news detection on social media sites
The proliferation of fake news is one of the greatest challenges that the digital age has to contend with. Fake news refers to false or misleading information that is distributed as genuine with the aim of causing damage or generating AD revenue. The challenge presented here is recognizing fake from genuine news, particularly during critical times such as during a pandemic or elections. This project involves building a fake news detector using both genuine and fake news datasets on Kaggle.
- Fake Product Reviews Identifier
AI specialization: Machine Learning
Requirements: Deceptive Opinion Spam Corpus dataset,
Skills: Natural Language Processing. Programming language
Applications: Detection of fake reviews in social media platforms and online eCommerce sites.
As it is, fake news is not the only tragedy associated with the digital age. Unscrupulous sellers have misled innocent clients to purchase products based on fake reviews. Fake reviews influence a product’s ranking either positively or negatively. More like the Instagram or email spam detection projects, the fake product reviews identifier project involves building a system that identifies fake reviews using the Deceptive Opinion Spam Corpus dataset available on Kaggle.
- Pneumonia Detection System
AI specialization: Deep Learning
Requirements: Chest X-Ray image dataset (Pneumonia detection)
Skills: Python libraries (FastAI), Neural Networks, computer vision, natural language processing
Applications: Disease diagnosis in healthcare
The healthcare sector stands a great beneficiary of the power of AI. Today it is possible to detect cancerous tumors, pneumonia, and other diseases in their early stage and save lives. This project involves building a model that categorizes pneumonia patients using X-ray images of their lungs.
How to Launch a Career in AI?
We have given examples of a few projects that a beginner can undertake to build a powerful project portfolio to demonstrate their AI skills and specializations. As we had mentioned earlier, AI is quite a broad field. Thus, there are many more projects that a beginner can choose to launch their career in AI. of importance is acquiring AI technical skills to develop and deploy innovative solutions for organizations. There are numerous open-source libraries that one can use to practice building AI models to solve business problems in various sectors. At the very basic, have a grasp of programming and machine learning concepts.
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