Learning Path: Artificial Intelligence Machine Learning (AIML) Engineer Roadmap

Artificial Intelligence and Machine Learning are making the world go round, and they have transformed how people work these days. Everything has become smart and swift because of these two technologies, which have opened up new career paths. Due to these reasons, one can certainly say that Artificial Intelligence and Machine Learning are currently the hottest jobs in the world. If you aspire to be a part of this domain, what should your AI ML learning path be?

What is the roadmap to learning AI and ML engineers?

This makes artificial intelligence and machine learning jobs among the hottest in the world today!! Artificial intelligence and machine learning job opportunities have been growing at a constant pace of 32% since 2019. LinkedIn’s Rising Jobs list shows that every company wants AI and machine learning experts. They are in high demand because organizations seek to improve their workflows and simultaneously lower their expenses.

Multiple technological reports have revealed that all the prominent tech businesses like Google and IBM hire multiple employees for roles in Artificial Intelligence and Machine Learning. As per the finding by the Wall Street Journal, AI jobs in the USA approximately became twice what it was in 2021.

Furthermore, AI has been driving innovative solutions across multiple industries. But the wheel does not stop there only. With the power of ML, there has been a paradigm shift in the approach to how to build newer products and services. Every organization is exploring new possibilities and newer ways to manufacture and deliver. As a result, these companies’ employees are constantly searching for how to expand their current skill sets. They are compelled to learn new working methods by learning AI and ML.

But this has thrown every person in a tizzy. They are trying to figure out the AI and ML learning path. To achieve mastery of these skills, they are trying to learn them as easily and quickly as possible.

However, to do this successfully, every organization needs a clear roadmap for getting its employees en route to AI and ML learning path. So, they are searching for whatever support they can get from experts. So for the sake of the benefit of the general masses, specifically those looking to learn AI and ML from scratch, we have come up with a sort of ‘Developers Guide to AI and ML’.

By using this path to learn machine learning, programmers, even those who have begun to code for the first time, can use this guide or path to achieve the goals they have been chasing for a long time without any clue.

Where should programmers start?

We have divided the learning path into two sections – AI and ML. We have further divided each section of the path into various stages, giving you individual knowledge of embarking on this journey in both sections. Remember, there is something for everybody, both AI and ML engineers. Every stage of the AI ML learning path contains various checkpoints, whether you want to learn and upgrade your AI or ML technological skills.

These checkpoints will help you climb each AI and ML Ladder rung, gradually increasing your technical knowledge of AI and ML theory and practical skills. In this manner, you will not get overwhelmed with all the information, and you will be able to build your foundation brick by brick, level by level, advancing by completely comprehending each level.

You will begin with learning to code in Python and will be taught all the mathematical concepts that come in handy in building customized ML models. Slowly, you will graduate to build micro and macro projects independently. But your story does not end here only. You will also be taught the best DevOps practices to apply to your ML projects for building them at an end-to-end pipeline.

AI and ML learning path: The roadmap

Given below is the AI ML learning path clearly and concisely. It will help you focus on areas where you should build up your machine learning and artificial intelligence skills.

Machine Learning  Fundamentals

Python programming skills

Learning R or Python will enable you to build Machine Learning Models. Programming in these two languages will teach you big data handling and fine-tuning the model to manipulate the data. You have to learn how to use libraries, especially graphical ones. This will help you create ML algorithms without using heavy programming skills. Learning Python will benefit you as every company dealing with big data needs machine learning. Hence, the demand for Python is so great. Google, Facebook, Dropbox, YouTube, and Netflix use Python heavily, especially for building recommendation systems. 

Python gives a lot of flexibility to Machine Learning engineers and helps in faster code development. You can also recompile the source code to view the developer’s changes. But which libraries of Python should you know how to use? We provide you with a list below:

• Matplotlib and Seaborn for spotting and visualizing data trends.
• Pandas help ML developers in data manipulation and analysis. 
• Scikit-learn helps implement supervised and unsupervised algorithms for regression and classification.
• Keras and TensorFlow frameworks aid in creating, training, and deploying deep neural networks.

Python has a large community. So you can find a lot of documentation and teaching methods online. So you have excellent learning aids, whether a beginner or an expert.

R programming

R finds much usage in Machine Learning and Data science. With 18000+ packages and a community of 2 million users in the CRAN open-source repository, R is one of the widely-used programming languages in this domain. It aids in ML areas like visualization and computation of statistical data statistical computing. It is also handy for the analysis of this type of data. It’s also great for generating statistical graphics and is popularly used in Machine Learning techniques like regression and classification. It is an important part of a toolkit of a machine learning engineer.

Some important R programming language libraries are: 

•   Xgboost helps implement gradient boosting.

• Mlr aids in clustering, classification, and regression through s3 inheritance.
• PARTY supports recursive partitioning and generates decision trees.
• CARET helps in the integration of model training and prediction and aids in the selection of an ML algorithm.

Applied Mathematics/Statistics and Probability

It is not compulsory to possess in-depth knowledge of mathematics, and R and Python have libraries that include different frameworks for that. But you need to learn the following:

• Calculus helps in ML methods like gradient descent to identify optimum values. Partial derivatives help you to comprehend ML models.
• Probability and statistics help us understand random variables, conditional probability, and statistical independence. You can also calculate a dataset’s mean, median variance, and standard deviation. You will also be able to understand the differences between Gaussian and Binomial distribution and also distinguish between confidence intervals and p-values.  
• Fundamental Linear Algebra will help you to build a base of matrices and vectors and how basic operations can be performed on them.

Data Cleaning

It is one of the important tasks of Machine Learning algorithm processing. Removing unwanted and faulty data will result in the building of an erroneous ML model. The performance of the cleaned-up dataset is a better-performing model. Performing cleansing on a huge dataset can be a cumbersome process, so you, as an ML engineer, should be able to accomplish this task. 

You must be fully trained in the four main steps of cleaning data:

• Removal of unwanted data,
• Resolution of structural errors,
• Capable of handling unwanted outliers,
• Be able to handle missing data.

Once you have obtained the required machine learning skills and followed the ML learning path, you must perform some practical projects to get hands-on experience and enhance your expertise and knowledge in this domain.

Artificial Intelligence 

To become an AI engineer, one needs to have the skill and follow the learning path for AI outlined below. This section will entirely focus on how to acquire the skills of an AI Engineer:

Machine Learning and Statistics

We have explained that in detail in the previous sections. So we will skip that part and move on to the other useful techniques for becoming an AI engineer.  

Deep Learning

In deep learning, you will need to master the art of building a neural network and training it for tasks like computer vision and NLP (natural language processing). The three most important deep learning algorithms that every AI engineer needs to know are:

• Artificial Neural Networks or ANNs
• Convolutional Neural Networks or CNNs, and,
• Recurrent Neural Networks

You should also be able to work with packages like Tensorflow, Keras, Tensorflow and Pytorch. Spend some time learning the deep learning frameworks.

Model Deployment

Now that you have learnt how to build AI applications from scratch, you now should be able to deploy these AI apps and scale them. You need to be able to deploy these ML models on popular cloud platforms like Microsoft Azure, Google Cloud or Amazon AWS. Cloud computing is an essential skill that AI engineers should possess.

Other miscellaneous AI Skills

• You need to know object-oriented programming (OOP) in C++, Python and Java.
• You need to know how to use and handle shell scripting or virtual machines like Ubuntu.
• You also need to know how to operate a Linux OS.
• Your duties as an AI or ML engineer will require you to deal with large databases, so you need to know about SQL and Hadoop.

What are the best ways to learn AIML?

Are you a beginner? Then you can start your journey with some introductory courses in Python. After that, you can head towards data science, machine learning and AI confidently. There are many great boot camps for AI ML beginners where Python is taught in such a way that data science and machine learning are the main focus of the course.

Another good way to begin your AI ML journey is you can either enroll in an AI and ML course or a boot camp. Multiple global boot camps provide international standard teaching both theoretically and practically. Spend six months to a year training yourself, and then you will be fit enough for an internship or your first job. Slowly, you can advance from that level and move ahead in your career and become an AI or ML engineer. 

Developers’ Guide to AI

Since AI includes ML, we will include all the relevant information for developers to progress into the field of Artificial Intelligence and Machine Learning. We are assuming here that as a developer, you will be well-versed in the language that you are using, and it could be anything, C++, Python, or Java. 

As an AI engineer or an AI application developer, you need to be able to understand and develop the following:

• The Intersection of Data, AI, and the Cloud
• Bot Framework Ecosystem
• Microsoft Azure AI/Google Cloud AI/Amazon AWS AI
• Virtual Assistant’s core features
• Conversational AI
• Language Understanding
• Prebuilt and reusable AI ML skills
• Adaptive Cards
• Business Insights and Analytics
• Flexible Integration and Contextual Awareness
• Multimodal Input

Parting thoughts

Hopefully, this piece has given you a basic idea of the AI and ML learning path. And, if you have managed to comprehend the information easily, it means you are already on the way. However, if you have not understood anything, do not be demotivated; you will need the right guidance to get there.