Machine Learning Career Path: How to make a career in ML & AI

“What we want is a machine that can learn from experience”

Alan Turing made this statement in 1947 in a lecture he gave at the London Mathematical Society. That was the birth of Machine Learning.

Nearly a decade later, Machine Learning has become one of the most sought-after career choices. According to Indeed, ‘Machine Learning Engineer’ was the best job of 2019 with a 344% growth with a yearly average salary of $146,085.

During the period between 2018 and 2024, the worldwide market for machine learning is forecasted to grow at a CAGR of 42.08%. The way humans interact with machines, technology, and data has been revolutionized by machine learning (ML). What was once a small subset of the economy is now worth billions of dollars. Machine learning has become crucial for businesses since it aids them in understanding customer behavior and operational patterns, and it helps with the creation of brand-new items. Facebook, Google, and Uber are just a few of the industry's most prominent examples of modern titans that have made machine learning integral to their business models. Numerous businesses now use machine learning as a key differentiator in the market. So it is no surprise that companies are actively looking to hire more machine learning professionals.

Fun facts about machine learning

• With its machine learning system for personalization and content recommendations, Netflix was able to save $1 billion.
• Machine learning achieves a 62% success rate in forecasting stock market peaks and troughs.
• There was a 60% drop in mistake rates when using GNMT, a translation method driven by machine learning, instead of Google Translate.
• 91% of market leaders are now investing in AI.
• As much as 44% of businesses that have implemented AI have seen cost savings as a result.
• There has been a 650% increase in the number of jobs advertised for data science on LinkedIn.

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What are the different types of machine learning?

The method by which an algorithm is taught to improve the precision of its predictions is frequently used as a classification scheme for classical machine learning. Learning can be accomplished in a number of ways, the most fundamental of which are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The kind of data that scientists working with data want to predict determines the algorithm that they go with to make that prediction.

Supervised learning:

During the process of supervised learning, data scientists provide algorithms with labeled training data and describe the variables they want the algorithm to evaluate for correlations. The inputs and outputs of the algorithm are both laid out in the description. The following are some applications that benefit from supervised learning algorithms:

• Ensembling: Combining the predictions of multiple machine learning models to produce accurate predictions.
• Binary classification: Dividing data into two categories.
• Multi-class classification: Choosing between more than two types of answers.
• Regression modeling: Predicting continuous values.

Unsupervised learning: This sort of machine learning occurs when the methods used to train the model are trained on data that is not labeled. The data sets are combined through the algorithm so that it may find any important connections. Both the data that algorithms learn from and the predictions they make are predetermined. Unsupervised learning algorithms are great for the following tasks:

• Clustering: Splitting the dataset into groups based on similarity.
• Anomaly detection: Identifying unusual data points in a data set.
• Dimensionality reduction: Reducing the number of variables in a data set.
• Association mining: Identifying sets of items in a data set that frequently occur together.

Semi-supervision learning: This method of machine learning combines elements of the supervised and unsupervised learning approaches. Data scientists may provide an algorithm with primarily labeled training data, but the model is allowed to explore the data on its own and create its knowledge of the data set regardless of what the data scientists feed it. Some areas where semi-supervised learning is used include:

• Machine translation: Teaching algorithms to translate language based on less than a full dictionary of words.
• Labeling data: Algorithms trained on small data sets can learn to apply data labels to larger sets automatically.
• Fraud detection: Identifying cases of fraud when you only have a few positive examples.

Reinforcement learning: This technique is often utilized by data scientists to instruct a computer on how to successfully finish a multi-step process for which there are established criteria. Data scientists will construct an algorithm to finish a job, and then they will provide the algorithm with either positive or negative cues as it figures out how to finish the task. However, the algorithm determines on its own, for the most part, which steps to take along the route as it progresses. Reinforcement learning is often used in areas such as:

• Robotics: Robots can learn to perform tasks using this technique.
• Resource management: Given finite resources and a defined goal, reinforcement learning can help enterprises plan out the allocation of resources.
• Videogame play: Reinforcement learning has been used to teach bots to play several video games.

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Career paths for professional growth in machine learning

Because it enables computers to learn on their own and minimizes the amount of work required by humans, machine learning has gained a lot of traction recently. This is because it helps machines perform better. As a consequence of this, the field of machine learning offers a wide variety of lucrative and in-demand employment options, such as machine learning engineer, data scientist, and NLP scientist, amongst many others.

1. Machine Learning Engineer

Machine Learning Engineers specialize in machine learning that executes various machine learning experiments utilizing programming languages such as Python, Java, Scala, etc. together with the required machine learning libraries.Probability, statistics, data modeling, machine learning algorithms, system design, and a variety of other talents are some of the most important ones that are necessary for this. A machine learning engineer also performs data analysis to develop a variety of machine learning algorithms that can operate independently with minimal supervision. To put it simply machine learning engineer is responsible for producing the necessary outputs for computers.

2. Data Scientist

The job of a data scientist was named the "Sexiest Job of the 21st Century" in an article published in the Harvard Business Review.A Data Scientist is someone who collects, analyzes, and interprets vast volumes of data to provide meaningful insights and employs sophisticated analytics technologies, such as machine learning and predictive modeling. After doing so, the firm’s management will utilize these to make judgments on the company's business. In light of this, machine learning is a very valuable talent for a Data Scientist to possess, in addition to other abilities such as understanding statistical research methods, data mining, and so on. In addition to this, a Data Scientist has to be familiar with several big data platforms and tools, such as Hadoop, Pig, Hive, and Spark, as well as many programming languages, such as SQL, Python, Scala, and Perl, among others.

3. NLP Scientist

The term "natural language processing" (NLP) refers to the process of teaching computers to comprehend spoken languages like English and other languages. This indicates that one day, computers will be able to communicate with us using our native tongue. An NLP Scientist is essentially someone who contributes to the development of a system that can recognize recurring patterns in speech and also translate words spoken into other languages. For a computer to gain the same abilities as a human, an NLP Scientist has to be proficient in the syntax, spelling, and grammar of at least one language in addition to machine learning.

4. Business Intelligence Developer

A Business Intelligence Developer is responsible for the collection, analysis, and interpretation of large amounts of data, as well as the production of actionable insights that can be used by company executives in the process of making business decisions. These tasks are accomplished through the use of Data Analytics and Machine Learning. To accomplish this task effectively, a Business Intelligence Developer has to be familiar with relational and multidimensional databases, in addition to programming languages such as SQL, Python, Scala, and Perl, among others. Additionally, familiarity with a variety of business analytics technologies, like Power BI, would be extremely beneficial.

5. Human-Centered Machine Learning Designer

Human-centered machine learning refers to those machine learning algorithms that are centered around humans. An illustration of this would be streaming services such as Netflix, which offers its users a selection of films to watch based on the viewers' interests to provide a "smarter" viewing experience. This suggests that a human-centered machine learning designer is responsible for the creation of a variety of systems that, when combined with information processing and pattern recognition, are capable of doing human-centered machine learning. This enables the machine to "learn" the preferences of individual people without the need for complex algorithms that manually account for every possible user circumstance. This eliminates the requirement for the machine to "remember" the preferences of particular humans.

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What certifications does a good machine learning professional need?

Now that you have all the information you need about machine learning as a career, it’s time to begin upgrading your skill sets and resumes with some certification courses. Not only do these teach you the nitty-gritty of your chosen area, but they also make you far more desirable to recruiters. It can be daunting to give assessments and exams, but platforms like Readynez are here to help you out. We built Readynez to close the information gap through our variety of certification courses. Each course is designed to help you ace your exams while also upgrading your knowledge base. Here are some machine learning courses that would be a great asset to your career.

1) Machine learning pipeline on AWS

The AWS Certified Machine Learning course is intended for individuals who perform a development or data science role. Successfully passing the exam validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

2) Microsoft Certified Azure data scientist (DP-100)

During the Microsoft Azure Certified Data Scientist course you will learn how to operate machine learning solutions at a cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

3) Microsoft MCSA: Machine Learning

The main purpose of the Microsoft MSCA course is to give students the ability to use Microsoft R Server to create and run an analysis on large data sets, and show how to utilize it in Big Data environments, such as a Hadoop or Spark cluster, or a SQL Server database.

4) Perform cloud data science with azure machine learning

The main purpose of the Cloud data science with Azure machine learning course is to give you the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning with big data tools such as HDInsight and R Services.

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In Conclusion

Machine learning is an essential component of artificial intelligence; it quite literally trains itself, allowing business processes and companies as a whole to become more intelligent. Statistics and numbers have made it clear that machine learning is transforming the globe to create a brighter future. And the demand for ML is not going anywhere in the upcoming future. So, choosing machine learning as a career path is sure to yield great success for you.

If you wish to begin your career in machine learning by diving into a certification course in ML & AI, get in touch with us! We are available to chat and can’t wait to hear from you!