The last few years launched a surge of interest in artificial intelligence. That includes an uptick in AI-related graduate applications and the debut of a plethora of consumer-facing products from OpenAI, Google, and Apple.
So how do you break into this industry? I'll show you how to get started and, using user-submitted resources from the community right here at Hackr.io, I'll give you a roadmap to build an artificial intelligence career that goes beyond the everpresent generative AI.
Learn from Stanford's Artificial Intelligence Programs
For a professional education on the subject, learn about the artificial intelligence programs at Stanford. They provide professional certificates and fully-online courses.
What is Artificial Intelligence?
Experts look at artificial intelligence as a collection of algorithms designed to generate results without constant instruction.
- An intelligent entity created by us (humans).
- It is capable of performing and carrying out tasks intelligently and we don’t even have to instruct it to do so.
- It is capable of acting and thinking humanely and rationally.
It’s an integral branch of CS/IT that aims to clone human intelligence in robotic machines. Robots, machines, or more commonly, AI systems are powered with advanced machine learning and deep learning algorithms that make machines intelligent. However, the definition of AI has certainly altered in recent years.
Earlier, even a simple function to perform calculations inside a calculator would have been considered to be an AI component. However, now, it’s just a mere computer program. Let’s understand how the various levels of AI exist.
Predominantly, we can categorize AI systems into 3 different levels. They are -
- Artificial Narrow Intelligence - This is also known as weak or narrow AI because it’s goal-oriented and designed to perform low-level simple tasks. Technologies such as ChatGPT, Alexa, etc. fall under this category. It’s carried out through machine learning which specializes in only a particular area and solves that particular problem. When you hear about large language models (LLMs), think about narrow AI.
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Artificial General Intelligence - Also referred to as deep or strong AI, where machines can mimic human intelligence. A few of its properties include recognition, hypothesis testing, analogy, etc. Speech and facial recognition systems generally fall under this category. However, this category is still under heavy research and is not completely developed yet.
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Artificial Super Intelligence - It is just a vague concept. It’s supposed to develop in the coming future. It should be able to create and formulate its own set of emotions, do tasks more efficiently than humans in fields such as calculations, sports, art, etc.
Here’s a detailed guide explaining the different types of AI.
Why learn Artificial Intelligence?
From a technical standpoint, the primary aim of AI is to assist human abilities and allow us to make exceptional choices with unimaginable outcomes. AI is used in almost all the popular automated tools that we created decades ago to improve their performance and make calculated decisions to produce better outcomes.
Let’s consider a few reasons why so many students are considering artificial intelligence as a career choice.
1. Bright Career
It’s been said that Artificial Intelligence is going to replace a whopping 40% of white-collar and blue-collar jobs. The market demand for employees with AI skills has doubled over the last 5-6 years.
However, there is still a deficiency of skilled experts and professionals in this field. Moreover, in most companies, AI jobs count for around 15%-20% of all the jobs. AI development is possible in two different categories - Engineering and Programming.
Currently, AI is in its growing phase, and pursuing a career in AI right now will ensure a decent salary as well as promising opportunities to grow in the future.
Here’s a guide that will explain to you the future of Artificial Intelligence.
You can work as an ML Engineer, BI Develper, Data Science Engineer, etc. The average salary of an AI developer ranges between USD $100,000 to $150,000 in the USA, whereas it ranges between INR 13 Lacs to 16 Lacs in India.
Top companies that recruit for AI engineers are:
- Google DeepMind
- Baidu
- IBM
- Uber
- Apple
- Meta
- Microsoft
- OpenAI
2. AI Ingests a huge amount of Data
It’s a fact that we generate over 2.5 quintillions (10^18) bytes of unstructured data per day. Social Media, data generated through smartphones, etc. all contributes to this huge number. And it will continue to multiply exponentially in the coming years.
With the availability of such crucial data, companies will surely want to improve their services to their customers. This can be done by properly structuring the data, and gaining useful insights from them. This process is called data engineering.
Hence, there will be a huge demand for data engineers who can churn out useful insights for their companies.
3. Improved User Experience
AI is not a separate technology, but it is a concept that can be ingested in existing technologies or applications to serve customers in a better and personalized way.
As an instance, you must have seen apps such as Amazon, Youtube, Netflix, etc. personalizes your recommendations based on your past browsing history. This is a fitting instance of how AI can be adopted to enhance UI/UX.
Technologies such as chatbots, assistants like Google, automation, etc. combine together to add multiple useful features to the existing products.
Needless to say, due to a plethora of benefits and uses provided by AI, it is rightfully considered as the skill of the future. It is not just limited to the computer industry, it also plays a significant role in healthcare, space, automobile, and even the banking industry.
For these reasons, it's time to learn about neural networks, Python, statistics, and data manipulation.
Applications of Artificial Intelligence
As discussed previously, Artificial Intelligence has its root buried in almost all the prominent fields prevalent today. With the evolution of big data, AI and ML have started to evolve.
Working with AI and ML requires a ton of data to perform training on. In the earlier days, it was difficult to store such a huge volume of data, let alone perform analysis on such large datasets. But with the evolution of Big Data, it’s now possible to churn fruitful insights from large datasets.
AI and ML are also largely being used in cybersecurity to filter information and provide real-time insights on any aberrations. Some popular applications of Artificial Intelligence are:
- Facial Recognition
- Detection of Cancers and Tumors in Healthcare
- Industrial Robots
- Self-driving automobiles
- Guidance of Missiles in defense systems
- Machine Learning and analytics
Take a look at this guide to understand the applications of Artificial Intelligence.
Prerequisite Skills
At the core of Artificial Intelligence you'll find math subjects we have already learned in high school. They give you a good foundation to stand upon when you start learning the basics of Artificial Intelligence and Machine Learning.
Some of these foundation skills include:
- Computer Science fundamentals
- Basic Statistics and Probability
- Linear Algebra including 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 there are tons of existing libraries and toolkits that 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
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 in their way when they search for resources to learn Artificial Intelligence.
Essential AI tools are now rolling out in every industry. They simplify many jobs, but that doesn't mean they're easy to build. Here's a roadmap if you want to get started:
1. AI 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 upon.
- 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
- How to 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 handle data in a seamless manner. 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 learn Python for Artificial Intelligence.
- Python basics - Expressions, variables, data structures, functions, packages such as pip, etc. (Check out the Python cheat sheet for a quick refresher)
- 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.
2. Data Preprocessing
Now that you are able to 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.
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 to - Machine Learning, Data Scientist, and Data Engineer.
Machine Learning Engineer
Machine Learning is the application of algorithms that are trained on data to find certain patterns and features that would help us to make predictions and decisions based on new data. The better and accurate the algorithm, the better are the results.
Hence, it’s of utmost importance that you select the most fitting algorithm for any problem statement.
The general roadmap to learn Machine Learning is:
- Learn concepts such as types of input parameters and variables (categorical, ordinal, and numerical).
- Concepts such as cost functions and gradient descents.
- Overfitting, underfitting, training, testing, and validation data sets.
- Precision, recall, bias, and variance.
Post that, you can move ahead and learn the different categories of Machine Learning algorithms that will help you to solve specific problems.
The main categories are -
- Supervised Learning - Algorithms that are used to classify objects and also for regression problems.
- Unsupervised Learning - Clustering Algorithms.
- Ensemble Learning - Boosting, Bagging, and Stacking.
- Reinforcement Learning - Reward-based learning algorithms.
It’s not difficult to learn these algorithms and the intuition behind them, what’s difficult is to find out which algorithms will be best suited for a particular kind of problem. You can brush up on your skills through comprehensive practice.
Interested in deep learning? You'll want to give it some practice. Check out these deep learning projects.
Data Scientist
This is another career path that you can adopt in the field of AI. It involves playing with data and mathematical models to find out the right information hidden inside the data. The two main components in a data science course are - Statistics and Data Visualization.
The topics that you need to be well-aware of in statistics include -
- Probability theory
- Continuous and Discrete Distributions
- Hypothesis Testing
- Summary Statistics and some important laws such as (LLN, CLT)
- Estimations such as MLE, KDE, etc
- Confidence Intervals
For visualizations, you can use tools such as:
- Python - Matplotlib, seaborn, plotnine, etc.
- Web - Vega-lite, D3.js, etc.
- Dashboards - Tableau, Dash, etc.
Data Engineer
Data Engineering is all about exploring the different dimensions of data. Relatable components include:
- Summary of Data Formats
- Data Discovery
- Data Source and Acquisition
- Data Integration
- Data Fusion
- Transformation and Enrichment
- Survey and OpenRefine
- Data lake and Data Warehousing
Apart from these career paths, you can also choose advanced subjects such as Deep Learning Engineer and Big Data Engineer. However, these paths require in-depth experience and knowledge on prerequisites such as Machine Learning and Data Engineering. This is the best and easiest pathway that you can adopt to start your journey in Artificial Intelligence.
Apart from these, there are other subfields of AI that you can explore.
These include:
- Object Recognition
- Robotics
- Speech Processing
- Expert Systems
- Natural Language Processing (NLP)
Here’s an in-depth insight on top Artificial Intelligence technologies.
No matter what field or subfield they are working in, all of them are said to be AI engineers provided they are working in the above-mentioned fields. Their ultimate goal is to develop better artificially intelligent systems.
Artificial Intelligence Courses
There are a plethora of online courses and tutorials available in the market today, most of them from reputed industries, that help you to understand AI and its applications.
You can expect to learn the following things from these courses:
- Understanding fundamentals and theory
- ML Algorithms
- Mathematical Concepts
- AI apps like games, real-time issues, self-driven cars, etc.
- AI agents like search-engines, constraint-satisfaction problems, etc.
- Deep Learning
- Data Processing
Benefits that you can get by learning AI from tutorials are -
- You will acquire ace-level coding skills in AI.
- You will get downloadable templates for reusable codes.
- It will help you to build intuition.
- You will apply theoretical knowledge to real-time solutions.
Here’s a list of top certification courses that you can enroll in to start your journey with Artificial Intelligence.
1. Artificial Intelligence A-Z™: Learn How To Build An AI
It’s a Paid Course provided by Udemy.
Key features -
- Complete Beginner to expert level AI skills.
- Code templates that you can copy and use for your own projects.
- Intuitive tutorials with hands-on examples.
- Solutions to real-world problems.
- In-Course support.
This course includes -
- 16.5 hours of on-demand video.
- 21 articles with an in-depth explanation.
- 1 resource that is downloadable.
- Full lifetime access to all videos.
- Certificate of completion.
2. Artificial Intelligence Certification Program by Stanford University
This is a beginners course on AI which is suitable for candidates with a background in CS and professional software engineers who want to choose a career path in AI. You can get certified in AI by Stanford Univ. and taught under Professor Andrew.
Key features -
- Machine learning fundamentals and knowledge representation.
- Useful Logic and probabilistic models.
- Robotics, NLP, and Visual Learning.
3. Intro to Artificial Intelligence
It’s a free course provided by Udacity.
Key features -
- Fundamentals of AI such as Statistics, Bayes Network, Uncertainty Principle, etc.
- Machine Learning, Logic, and Planning.
- Applications such as Image Processing, Computer Vision, NLP, Information Retrieval, Robotics, etc.
4. Artificial Intelligence
This course is free provided by MITOpenCourseware.
Key features -
- AI algorithms such as DFS, BFS, Hill Climbing, etc.
- Constraints.
- Machine Learning and Deep Learning.
- Inference models.
5. Artificial Intelligence (Course Bundle) - LinkedIn
This is a paid course that comes with LinkedIn Premium.
Key features -
- Foundational Courses
- Machine Learning
- Deep Learning
- Practical Projects
- Business Essential Training
- Predictive Analysis
- AI for Cyber Security
Here’s a list of the 10 best Artificial Intelligence courses in 2024.
Apart from these courses, there are free websites or blogs that you can leverage to learn AI in your own self-paced way. It covers almost all the topics and algorithms with hands-on practical examples.
Apart from that, you can also learn AI in the most complete and in-depth manner through some excellent books mentioned below.
- Artificial Intelligence: A Modern Approach
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Prefer to read at your own pace? Here’s a list of the top 10 Artificial Intelligence books for beginners.
Projects
Artificial Intelligence is a broad topic that contains a variety of sub-topics. Covering the theory in itself is a mammoth task. However, only grabbing the theory is not enough. So, if you are a beginner and have just started with AI, it’s best for you to work on small projects on the go.
Doing projects in AI can be very beneficial and can impart diverse technical knowledge and experience. Moreover, having AI projects in your resume and portfolio can lend you high-end jobs and give you an upper-hand over your competitor candidates. You can showcase your talent with these projects. Each project poses a different challenge and the best way to explain them to your recruiter is by actually doing the project.
Moreover, doing projects on a regular basis will help you to track your progress and in case something goes wrong, you will find new and innovative solutions to solve the problem. Moreover, if you are a final year student, performing a project in the AI domain can give you good grades because of the wide array and range of domains it covers.
Finding the right project in the AI domain can be a bit tricky. You need to make sure that you don’t take on a tough project as a beginner because in this case, you might lose interest in the middle of the project. Some projects for all levels are enlisted below.
Basic and Intermediate Level Projects
1. Housing Price Prediction
Here, you will have to predict the selling price of houses from a Boston-based dataset that contains a few sample house prices. You can easily get the dataset from Kaggle or UCI ML repository. It’s a great project for beginners.
2. Stock Price Prediction
Share market is filled with data. You can get a variety of datasets here and start immediately as a beginner. You can try to predict the stock price of a particular stock at a given timestamp. You can also perform time-series analysis in any of these datasets.
3. Customer Recommendation
You might have observed in online eCommerce portals that below a product, you will find a list of products that you are very likely to purchase. Or, if you are trying to purchase a product, it will recommend you a few more products stating that these products are usually bought together. These are called recommender systems and are one of the perfect machine learning projects that you can create as an intermediate-level ML enthusiast.
4. Chatbots
It’s an advanced-level project. You can start by creating niche-based customer service chatbots. Once created, you can improve it by adding more questions and instances.
5. Spam Classifier
You can create a spam classifier as your first project in machine learning. Here, we classify emails as spam or non-spam using classification models.
Some other advanced level projects that you can work on include -
- Lane Line Detection.
- Optimal Path.
- Game of Chess.
- Next word predictor.
- Learn to Drive with Reinforcement Learning.
- Price Negotiator Ecommerce Chatbot System.
- Self-Driving Car.
- Music Recommendation App.
- Facial Emotion Recognition and Detection.
Certifications
Needless to say, professionals with AI skills honed by certifications are bound to get countless job opportunities in the next few years. In fact, as predicted by Gartner, AI is expected to create around 2.3 million jobs in the upcoming years.
Hence, it becomes very necessary to get yourself equipped with all the important and valuable certifications so that recruiters can hire you without a hint of doubt on your skills. Here’s a list of popular certifications that you must have in your portfolio.
1. Professional Certificate Program in Machine Learning and AI
It enables you to learn from renowned MIT faculties and industrial experts, in-person and you will learn all the essential concepts, skills required to develop efficient AI systems. It will help you to solve the challenges modeled by AI in the workplace and apply cutting-edge knowledge in ML and AI. It will allow you to connect with an expert group of peers from around the world.
2. Artificial Intelligence Engineer Certification Program
It is an excellent AI certification program ideal for those candidates who are seeking to prioritize their careers on the AI front. The ARTIBA provides sturdy coursework. This certification is unique and offers exceptional value for enthusiastic AI engineers. The top-notch curriculum covers topics linked to natural language processing (NLP) topics, supervised and unsupervised ML, regression models, deep learning topics, and cognitive computing flooring the appropriate pathway to begin a career in AI.
Wrapping Up
- To sum up, in this article, we have put forward a step-by-step guide that will help you learn Artificial Intelligence the modern way. We started with a basic introduction of Artificial Intelligence and discussed the broad categories of AI.
- We then skimmed through the reasons why you should learn Artificial Intelligence for a prospective career. We discussed several real-life scenarios where Artificial Intelligence is heavily used. Moving on, we saw the prerequisites that you would need to have before you start with the core of Artificial Intelligence.
- We then discussed a detailed roadmap that you can adopt to smoothly slide through the journey of learning Artificial Intelligence. We started with the fundamentals of AI enlisting all the important mathematical concepts, database basics, and programming language topics that you must learn to confirm a smooth journey.
- We then discussed the different data preprocessing techniques that you must learn to organize unstructured data that will help you to move forward. After that, we discussed the 3 different career paths that you can take namely - Machine Learning, Data Science, and Data Engineering. Only after that, you should start with advanced topics like Big Data and Deep Learning.
- We also explored other subfields of AI that you can adopt as a career. We then listed the most popular paid and free AI certifications, courses, and tutorials that you can follow as a guide.
- Moving on, we discussed some of the most common and beginner-friendly projects as well as some advanced projects that you can make on-the-go to get hands-on experience with Artificial Intelligence.
No doubt, Artificial Intelligence is a vast and confusing domain due to all the technical jargon that you come across, however, with the help of this simple step-by-step guide, we hope you now have a clear pathway paved for you.
