Machine Learning Roadmap & Learning path

The world has changed recently, becoming faster and wiser thanks to AI and machine learning. The growth of these two industries has accelerated technological development and professional opportunities. As a result, employment in artificial intelligence and machine learning are currently among the most in-demand worldwide! According to Linkedin’s ‘Jobs on the Rise’ list for 2021, the number of job possibilities in artificial intelligence and machine learning has increased by 32% since 2019. The increased number of businesses looking to enhance their operations while cutting costs has led to a high demand for AI and machine learning professionals.

According to Randstad Sourceright, many people are hired for AI and machine learning positions by tech companies like IBM and Google. The Wall Street Journal reports that in 2021, the number of AI job listings in the US nearly doubled.

These data points suggest that in the upcoming year, a career in artificial intelligence and machine learning is worthwhile. Whether you are a recent graduate seeking for entry-level positions or a professional prepared to switch to this subject in the middle of your career, you need to have a firm understanding of it. For you, we have created the perfect artificial intelligence and machine learning path. You’ll learn everything from scratch if you follow this thorough study plan. So let’s get going!

What is Machine Learning in AI?

Every newcomer interested in a career in machine learning has the question, “Are AI and ML the same?,” on his mind.

Machine learning, a branch of artificial intelligence, combines data and algorithms to simulate how people learn and develop through time. Without specialised training, machine learning enables software applications to increase the accuracy of their predictions. Algorithms using machine learning use available data as input to forecast the desired result.

To put it another way, machine learning makes it easier to create analytical models. It uses techniques from neural networks, statistics, operations research, etc. to uncover insights in data without being explicitly directed where to look or what to infer.

Why should you learn AI and Machine Learning?

Do you have second thoughts about pursuing a profession in the artificial intelligence and machine learning fields? Here are some arguments in favour of learning machine learning right away to assist you in making your decision:

Biggies to Startups- Everyone is Adopting AI and Machine Learning

AI and machine learning are used in several applications, from Tesla’s self-driving cars to Amazon’s virtual assistant “Alexa.” Engineers and computer scientists devote a lot of effort into giving robots cognitive behaviour so they can reason and act quickly. From being a purely academic area of study to being utilised in business applications by both tech giants and start-ups, the AI and machine learning domains have advanced.

• Have you ever used Pinterest and questioned how it continuously produces relatable content? In order to provide users with pertinent information, Pinterest gathers and analyses current content using machine learning technologies.
• Another fascinating application of machine learning technology is Twitter’s customised timeline. Twitter ranks tweets that are very relevant to you using machine learning. It uses machine learning techniques to determine the relevancy of the tweets based on your interactions with them most frequently and a few other factors. Higher-ranking tweets are more visible at the top of your timeline, encouraging you to interact with them.
• Netflix consistently comes up with the most original and ideal recommendations for you. However, how can this occur? In order to forecast which streaming video series its millions of viewers will likely enjoy the most, Netflix uses machine learning to track their viewing behaviour and then makes recommendations based on those predictions.
• Machine learning technology is used by the free language-learning programme Duolingo to help its users learn a variety of languages. To predict how long you will keep a term before enrolling in a refresher course, it uses a statistical algorithm that examines the information from your responses. Duolingo knows when to suggest that you retake the course as a result.

Machine Learning Careers To Stay in Demand for Decades to Come

Did you know that by 2030, employment prospects for data scientists and mathematicians, both of whom work in the field of artificial intelligence and machine learning, are expected to increase by 31.4%? According to Forbes, the market for machine learning jobs will be valued $31 billion by 2024, a 40% growth in just six years.

Machine Learning Engineers are in great demand as machine learning achieves breakthroughs in many industries, according to Indeed’s list of the top 10 in-demand careers. For machine learning engineers in the US, Linkedin lists more than 158,000 available positions. These figures demonstrate that now is the ideal time to pursue a career in artificial intelligence and machine learning.

Some of the fastest-growing developing machine learning occupations for 2022 and beyond include NLP Scientist, ML Cloud Architect, Machine Learning Researcher, Human-centered Machine Learning Designer, Deep Learning Engineer, AI Engineer, and Full Stack Data Scientist.

How much does an ML Engineer earn? | Machine Learning Engineer Salaries

A machine learning engineer’s average annual salary is $121,446 per year in the United States, £51,487 per year in the United Kingdom. In India, the average compensation for a machine learning engineer is INR 702,361 per year.

The average compensation for an entry-level machine learning engineer, having 0–4 years of experience, is around $97,090. However, bonuses and profit-sharing can rise to $130,000 or more.

Machine learning engineers in their mid-career typically have 5 to 9 years of experience and earn an average of $112,095 which can reach $160,000 or more due to bonuses and profit-sharing.

A senior machine learning engineer would have more than ten years of experience and expect to earn an average of $132,500 a year. However, intense competition, bonuses, etc., it can rise to over $181,000 per year.

Machine Learning- one of the most in-demand skills in 2022

In the tech industry, machine learning and AI programs have helped boost productivity and efficiency, and these technologies will only grow in popularity. Businesses will increasingly require personnel with the expertise to assist meet the demand for more AI-powered services as AI becomes more widespread. Companies will need to search for candidates with machine learning, natural language processing, AI integration, etc., to keep up with the automation wave in this digital era. According to the World Economic Forum’s 2020 report, roughly 97 million new roles could arise by 2025 in the ai and machine learning industry. This rise in machine learning jobs indicates that the demand for AI and machine learning skills in individuals will keep growing in the coming years. Also, according to a report by Indeed, AI and machine learning are among the top 20 skills in demand in the current marketplace.

Let us now look at the roles and responsibilities of a Machine Learning engineer working in various industries.

What does a Machine Learning Engineer do?

It’s essential to understand what a machine learning engineer is before understanding the day-to-day activities in a machine learning role. A machine learning engineer is equivalent to a software engineer specializing in the machine learning domain and lies at the intersection of software engineers and data scientists. Machine Learning engineers are interested in programming machines to accomplish specific tasks. A machine learning engineer takes conceptual data science models and expands them up to production levels to handle a large amount of actual data.

The roles and responsibilities of machine learning professionals vary based on the industry and organization they work at.

Role of ML Engineer in E-commerce Industry (Amazon)

At Amazon, a machine learning engineer is in charge of working with massive amounts of data to construct Machine Learning and Deep Learning prediction models for a variety of clients. He or she will work with Amazon Professional Services professionals to complete projects from beginning to end and aid with model implementation. The ML engineer would be responsible for working on various Amazon projects, such as building a product recommendation system or, a retail price optimization system. A machine learning engineer at Amazon would often be required to create ML-based solutions for analyzing product purchase relationships, such as customer market basket analysis.

Role of an ML Engineer in Finance Industry (Morgan Stanley)

A Machine Learning Engineer working at Morgan Stanley is responsible for creating applications that support their machine learning platform and facilitate ML Ops adoption. They will have to curate the best banking practices that involve smooth machine learning development, collaboration, and the fast transition of projects from concept to production. The ML engineer has to determine and develop business solutions for various banking problems, e.g. predicting customer eligibility for loan sanctioning, developing customer churn prevention strategies, and so on.

Role of an ML Engineer in Social Media Industry (Twitter)

Twitter hires machine learning engineers who can improve existing search engines and recommendation systems, try out new approaches, and deliver machine learning (ML) solutions for recommendation systems. The role involves creating models and algorithms to comprehend user interest and intent better and increase content relevancy. Also, they will be responsible for developing new SEO strategies and features for the organization’s growth. The ML Engineer will need to collaborate directly with live production systems and product teams to offer ML solutions within the Twitter tech stack. He/she will be required to work on developing solutions to certain challenges such as classifying fake news across the platform, etc.

Prerequisites to Learn Machine Learning

Machine learning engineers often need a bachelor’s degree in computer science, mathematics, statistics, or a related discipline. Deep learning and computer vision-related careers may demand higher degrees. Furthermore, anyone willing to pursue a career in machine learning should be familiar with the prerequisites that will make this profession more approachable. Here’s a list of pre-requisites you are required to have as a machine learning engineer-

• To do computations and work with algorithms, you’ll need strong mathematical skills;
• fundamental coding skills in Python as well as experience with a typed language (e.g., C++ and Java);
• capacity to work with large, complex datasets;
• deep knowledge of machine learning evaluation measures;
• excellent analytical and problem-solving skills;
• meticulous attention to detail;
• good writing and verbal communication skills, since machine learning engineers often need to communicate the project details to the client, etc.;
• a creative and innovative mind as they work on building solutions for various business challenges;
• solid domain knowledge to understand the business demands and design workable machine learning solutions.

Individuals willing to pursue a career in machine learning but who do not have the required pre-requisites should consider taking a beginner-friendly machine learning learning path. The primary objective of taking a study path is to understand what subject areas you should concentrate on and how.

How to Start Machine Learning | Machine Learning Learning Path

In this section, you’ll find a clear study path that will help you understand the subject areas you need to focus on to become a successful machine learning engineer.

Fundamentals

• Programming with Python/R

The programming skills needed to understand machine learning are determined by how you intend to use it. Building machine learning models demands coding to handle big data, fine-tuning a model, etc., to test and optimize your model. Several programming languages provide built-in machine learning libraries, thus making it easier for anyone with basic programming skills to begin a career in machine learning. Graphical and scripting machine learning platforms, such as Weka, BigML, etc., help you create machine learning algorithms without heavy coding. However, programming fundamentals are still needed.

• Python

Python is the predominant programming language for machine learning at Google, Instagram, Facebook, Dropbox, Netflix, YouTube, Uber, Amazon, etc. Python’s ease of use is a massive advantage in AI and machine learning, among other programming languages. Because of its simplicity, machine learning engineers can focus on the actual business problem rather than creating code, thus resulting in faster development. Python offers a lot of flexibility in recompiling the source code to see the changes. Here are the most commonly used Python frameworks and libraries machine learning engineers must know -

1. Seaborn and Matplotlib: Machine learning engineers can use these two modules for EDA, i.e., they can use these Python modules to visualize and find trends in data.

2. Pandas: This library is used by machine learning developers to manipulate and analyze data.

3. Scikit-learn: This module allows machine learning engineers to implement supervised and unsupervised algorithms. You can find machine learning algorithms for classification, regression, etc., within this package.

4. TensorFlow and Keras: Machine learning engineers use these frameworks to create, train, and deploy deep neural networks.

The best way to start learning Python fundamentals is by referring to the Python community. It has a variety of documentation, teaching materials, and open support available for beginners and experts.

• R programming

With over 2 million users and 18000+ packages in the CRAN open-source repository, R is an incredible programming language for machine learning. R is a widely-used programming language in machine learning for statistical computing, analysis, and visualization. It’s a great graphics-based language for studying statistical data via graphs and is popularly used in machine learning techniques like regression, classification, etc. The user-friendly IDEs like RStudio and a variety of tools for generating graphs and managing libraries make R a must-have programming language in a machine learning engineer’s toolkit.

If you are seeking a career in machine learning, here are some of the R libraries you need to learn -

1. xgboost: It is a highly efficient library for implementing the gradient boosting machine learning technique.
2. mlr: This framework supports classification, regression, and clustering with a simple extension method via s3 inheritance.
3. PARTY: The Conditional Inference approach generates decision trees with this package. It supports for recursive partitioning.
4. CARET: This package was created to integrate model training and prediction for a variety of machine learning algorithms for a specific business challenge and to assist in selecting the optimal machine learning algorithm.

• Applied Mathematics/Statistics and Probability

If you are willing to pursue a career in machine learning, it is not necessary to have in-depth knowledge of math as there are libraries and frameworks for that. However, there are specific topics you need to focus on while studying mathematics for machine learning-

1. Fundamental Linear Algebra- You should start by gaining a solid knowledge of matrices and vectors and their basic operations
2. Probability and Statistics- Introduction to topics such as random variable, statistical independence, and conditional probability will help you begin your machine learning journey. You must also calculate and analyze a dataset’s mean, median variance, and standard deviation — a basic understanding of normal or Gaussian distribution and the Binomial distribution when it comes to probability distributions. You must also know the difference between p-values and confidence intervals.
3. Calculus- Calculus is used in machine learning methods like gradient descent to identify optimum values. Understanding partial derivatives can help you better understand how many machine learning models work.

• Data Cleaning

Machine learning involves training and providing data to algorithms that handle the computation of complex tasks. Businesses often experience difficulties feeding the correct data to machine learning algorithms or cleansing unwanted faulty data. In other words, when it comes to using machine learning data, one of the major tasks is cleaning data sets. If machine learning engineers work with a clean dataset, there’s a high likelihood of better model performance. This can be useful in computing, especially when there is a huge dataset. To pursue a career in machine learning, you must learn about the four main steps of data cleaning- removing unwanted data, resolving structural errors, handling unwanted outliers, and handling missing data.

Once you’ve acquired the fundamentals of machine learning, you should explore some beginner-friendly machine learning projects to put your skills to work. Hands-on experience and practical knowledge are always crucial to enhance knowledge and expertise.

Machine Learning in AI

The second step in your machine learning learning path is learning machine learning concepts that would help you understand what machine learning means and how it works. Having a good grasp of the basic concepts of the different types of machine learning algorithms(supervised learning, unsupervised learning, and reinforcement learning) is the key to success.

1. Supervised Learning

In this machine learning approach, labeled training data sets are provided to algorithms, and the variables the algorithm assesses for correlations are also specified. The algorithm’s input and output are both selected.

Algorithms such as Classification (Naive Bayes), Regression (Linear and Logistic), Decision Trees, Support Vector Machine, etc., are an integral part of supervised learning. You can apply supervised learning algorithms in medical imaging, speech recognition, image segmentation, etc.

2. Unsupervised Learning

This machine learning approach involves training algorithms on unlabeled data. The algorithm is responsible for finding relevant connections between the data sets. The data sets used to train algorithms, and the forecasts or conclusions they generate are all predefined.

The unsupervised learning category includes clustering algorithms such as K-means, hierarchical, Hidden Markov model, Gaussian model, etc. You can apply the unsupervised learning algorithms for use cases like market research and object recognition.

3. Reinforcement Learning

Reinforcement learning is a technique used by machine learning engineers to teach a machine to perform a multi-step workflow with well-defined guidelines. Machine learning engineers design an algorithm to complete a task and give it some feedback to figure out how. However, it primarily allows the algorithm to select what actions to take during the entire process on its own.

Deep Learning in AI

The third and final step in your machine learning study path involves deep learning.

Deep learning is a machine learning technique that allows computers to learn by example in the same way humans do. Deep learning was developed to handle highly complex issues, and it employs a method known as deep neural networks.

Deep neural networks or, Artificial neural networks are based on the biological brain network of humans. A deep neural network learns in the same way that a biological neural network does, that is, by accumulating experience and fixing mistakes. Machine learning engineers employ deep learning techniques for various purposes such as automated feature extraction, object classification, etc.

• Start off by learning about ‘neural networks’- Convolutional Neural Networks, Recurrent Neural Networks, etc.
• Learn about Activation Function, Forward and Backward propagation, Padding and Pooling, Gradient Descent agorithm.
• Also, familiarize yourself with Gradient problems (Vanishing and Exploding).