At Different Points Of Time, Some Jobs Are Attracted By A Wide Range Of Users, Educational Institutions, And Companies, And Everyone Wants To Achieve The Highest Expertise And Skill In The Shortest Time.

For example, at one point, everyone was looking to learn web programming and website building, and some schools were thinking of making money with titles that mainly were advertising (learn web programming and design professional websites in less than a week).

After a while, the SEO job market became hot, and almost everyone claimed to be an SEO and could give expert advice to companies in this field, while some of these people were using robots to send floods of traffic to sites that made Google rank these sites. Remove from your results page.

For some time now, this fever has entered the world of artificial intelligence and machine learning, and we have seen that some schools have started to advertise in different ways; interestingly, some companies also publish rather strange advertisements in this field. For example, an airline ticketing agency publishes a job posting for a deep learning engineer.

This issue led us to examine in this article what kind of road map a person interested in the world of machine learning (supervised, unsupervised, and reinforced) should learn the required skills, how long it will take to be able to Jobs in this field should send resumes and what is the state of the job market in this field.

Labor market situation

Statistics show that from 2015 to today, the number of machine learning engineers has grown upward, and the average salary for these professionals globally is $146,085.

Suppose you are passionate about data, automation, and algorithms. In that case, machine learning is a good career choice because, in a working day, you will be dealing with vast amounts of raw data, implementing algorithms to process data, and automating processes and optimizing algorithms. Another reason entering the field of machine learning is attractive is the various career paths in front of you. With a machine learning background, you can have a lucrative career as a machine learning engineer, data scientist, natural language processing specialist, business intelligence developer, interactive chatbot designer, and similar examples.

One of the main reasons for the profitability of machine learning businesses is that the skills of this technology meet the needs of companies. Machine learning helps companies overcome economic problems and earn more profit by providing accurate solutions. For example, during the coronavirus epidemic, companies like Microsoft and Amazon achieved huge profits due to cloud-based platforms and services based on machine learning.

In addition, there is always fierce competition among companies to attract machine learning specialists; the latest in this field is hiring machine learning specialists.

Google-owned Apple. Ian Goodfellow, one of Apple’s former executives who resigned due to the company’s strict policy to return employees to the workplace, was recently hired by the DeepMind team (a subsidiary of Alphabet). While working at Apple, Goodfellow was responsible for overseeing the performance of the company’s machines.

Data-driven jobs, more job opportunities

Beyond Iran, small and large companies publish daily advertisements about recruiting artificial intelligence specialists and people who can work with data.

Every year various conventions, seminars, and conferences are held focusing on artificial intelligence and its subcategories. Some companies and startups are seriously engaged in this field, but compared to the global standards of this field in the world of technology in Iran. However, in Iran, some companies still use traditional mechanisms to conduct business activities.

There is still a lot of work to do. Therefore, the labor market for artificial intelligence and machine learning specialists in Iran is not saturated; Therefore, in the next few years, we will see the growth of job advertisements related to data-driven jobs.

Globally, we have seen a 74% annual growth in job advertisements related to machine learning and artificial intelligence. One of the reasons for this problem comes back to the nature of this science.

Compared to other skills in the IT industry, machine learning is not limited to learning to work with tools, libraries, or programming languages, and people must be able to analyze and formulate a problem.

However, a significant part of the required skills of a machine learning (reinforcement) specialist is related to algebra and statistics. More precisely, you cannot claim to have the highest level of mastery in machine learning programming while not having the ability to analyze and implement mathematical formulas. Especially the recipes we mentioned in the reinforcement learning algorithms article.

Therefore, few private schools can hold programming training classesMachine learning is reinforcement. Familiarity with mathematics and statistics, mastering programming languages, ​​and popular libraries in this field will help you find good job opportunities in machine learning or data science. Another essential thing to mention concerning machine learning is that some job positions in this field are less known.

One such job is MLOps engineer. In the following, we will take a brief look at this job title, and then we will go to the skills that a reinforcement learning engineer needs.

What are MLOps?

Most people looking to enter the data industry will have to learn machine learning skills. MLS is at the intersection of data science, dumps, and data engineering. An MLOps engineer brings machine learning models from testing to production using software engineering and data science skills. MLOps tops LinkedIn’s emerging job rankings with 9.8% growth.

However, most data scientists fail to take the models they build into production. This issue has caused companies to face the problem of the gap between the design of models and their implementation and machine learning models. They cannot use because they will never be available to the end user. MLOps engineering aims to fill this gap and allow companies to operationalize and profit from data science models commercially.

MLOps engineering should consider a rapidly growing field as companies gradually realize that data scientists alone cannot create a valuable machine learning model.

No matter how accurate a machine learning model is, it isn’t constructive if it is unusable in a production environment. Most people looking to enter the data industry tend to focus on data science. Therefore, it is a good idea to learn the skills required for MLOps, as it is a lucrative career and the job market is not saturated outside of Iran.

How to become a Reinforcement Machine Learning Engineer?

In the not-so-distant past, knowing the pivot tables option in Excel was considered one of the high-level skills, and mastering the Python programming language was a great advantage because few programmers got this language.

Today, a wide range of professionals whose field of work has little to do with artificial intelligence and machine learning are fluent in this programming language, so one of the essential prerequisites for entering jobs related to machine learning (reinforcement) is mastering Python programming language or options.

Suppose you are looking for a transformation in your field of work and want to enter the world of artificial intelligence, particularly reinforcement machine learning. In that case, we will introduce you to a 9-step roadmap that shows where you should start and how to start from one step. Go to the next step.

Figure 1 shows the 9-step path to becoming a machine learning engineer. Fortunately, there are good online and offline resources to help you achieve a high level of proficiency in each of these steps. On average, if you focus full-time and intensively on the nine steps in this article, you’ll have the opportunity to submit resumes for AI-enhanced jobs in ten months.

It is necessary to explain that the skills and tools used in machine learning with supervisors, unsupervised, and reinforcement have a lot of convergence. In some cases, they have differences.

figure 1

Step 1. Seek to understand the basics

Spend the first few weeks building up your general knowledge of data science and machine learning. You may already have ideas and know what machine learning is, but if you want to become an expert, you need to focus on learning as much detail as possible. My recommendation is to spend the first few weeks learning the following skills:

•  What is analysis?
•  What is data science?
•  What is big data?
•  What is machine learning?
•  What is artificial intelligence?
•  What is the difference between machine learning and artificial intelligence, and how are they related?
•  How are these sciences used in the real world?

Next, write a blog post and explain these concepts to others in your own words so they can understand them properly.

Step 2. Focus on learning statistical topics

I have to admit the truth. Although I have some knowledge in machine learning, I don’t feel I have much ability in statistics. Of course, the root of this problem goes back to the pre-university levels, where mathematics, statistics, and geometry are not taught eloquently. Therefore, you have to spend time learning these topics yourself. As a result, this problem will accompany you upon entering the university.

You can be a data scientist without becoming a professional statistician, but the reality is that you cannot ignore the statistical concepts surrounding machine learning and data science. Hence, what you need to do is to understand the basic concepts so that you know when to use them. If you can understand the concepts of statistical science, you will not face any particular problems in the future regarding the analysis of models. In short, I suggest you focus on learning the following concepts.

•  Data structures and collections.
•  Sampling.
•  Basic principles of probability.
•  Distribution of random variables.
•  The inference is related to numerical data and classification.
•  Its Linear, multiple and logistic regression (you need these topics in almost all branches of machine learning).

Make a list of resources that teach the above topics most easily. Statistics and Probability in Computer Engineering by Parviz Nasiri, the book Introductory Statistics and Probability by Javad Behbodian, and Statistics and Probability by Narges Abbasi are good sources in this field. There are many online resources available in Persian and English on the Internet.

Step 3. Learn Python or R (or both) for data analysis

Learning to code is more accessible, fun, and rewarding than most people realize. While mastering a programming language is a continuous learning process, at this point, you should be familiar with the process of learning a language, which is not that difficult. Python and R are popular options, and mastering one can make learning the other easier. I started with the R programming language and moved on to Python.

In both cases, you should think about learning the following concepts:

•  Getting to know data structures and data structures and how to define them.
•  Its How to interact with data and files in the target programming language.
•  Qualitative data analysis.
•  Data cleaning and preparation.
•  Its Data manipulation (sorting, filtering, aggregation, etc.).
•  Data visualization.

Step 4. Complete an exploratory data analysis project

Exploratory data analysis is the study of data to understand the information hidden in it and share it with users. This stage of learning is the most exciting part of the story, which provides you with many valuable tips related to data analysis. Among the critical topics that you should focus on learning are the following:

•  Univariate discoveries.
•  Paired and multivariate explorations.
•  Its Visualization and the ability to work with Tableau. It is necessary to explain that the software is used for data visualization in data science and business intelligence.

Step 5. Create unsupervised learning models

Suppose we have data related to all the countries of the world and information related to population, income level, health status, primary industries, and other things. Now we want to determine which countries are similar in the above parameters. How do we do this? If we intend to compare different countries based on the parameters that sometimes exceed 50 cases, based on what automatic and intelligent solution should we do this task in the right way?

It is where unsupervised machine learning algorithms come into play. Now is not the time to bore you with the details, but the good news is that if you’ve made it this far, you’ve entered the world of machine learning.

Among the topics that you should focus on learning at this stage, the following should mention:

• K-means clustering.
•  Association rules.
•  Its Commonly used algorithms are used in unsupervised and reinforcement learning.

Step 6. Create supervised learning models

If you have information about millions of loan applicants and their past repayment history, how can you identify applicants eligible to receive a loan?

In another example, given past data, can you predict the response rate of users to a digital advertising campaign? Are there people?

Supervised learning algorithms are used to provide a solution for the above problems. While there are many algorithms to master, some are more popular and offer practical capabilities for solving real-world problems. Among the critical topics that you should think about learning are the following:

•  Logistic regression.
•  Classification trees.
•  Its Ensemble models such as random forest and bagging.
•  She supervised vector machines.

To get started, download a dataset and create models using your learning algorithms. Training, testing, and tuning each model to improve performance are important issues.

Step 7. Upgrade your knowledge of big data-related technologies

Most of the machine learning models used today have been around for decades. These algorithms are still used because they have access to a good data set, and this big data allows the algorithms to provide valuable and reliable outputs.

Data engineering and architecture is a specialized field, but every machine learning professional should know how to work with big data, regardless of the industry they are planning to enter.

Understanding how large amounts of data can be stored, processed, and accessed most easily is essential to implementing solutions that can be designed in practice. Among the critical topics that you should think about learning at this stage, the following should mention:

•  An overview of big data and its ecosystem.
•  Familiarity with Hadoop, HDFS, MapReduce, Pig, and Hive technologies.
•  Getting to know Spark.

Step 8. Learn how deep learning models work

Deep learning models have helped companies like Apple and Google to create software like Siri or Google Assistant. They help global giants test driverless cars and recommend the best treatment courses to doctors.

Machines can see, listen, read, write, and speak thanks to deep learning models that want to transform the world in many ways. For example, start by building a model that can distinguish an image of a flower from a fruit. The above approach may not help you develop your driverless car, but it certainly helps you understand its path.

Among the critical topics that you should focus on at this stage, you should mention the following :

•  Artificial Neural Networks.
•  Natural Language Processing.
•  Convolutional neural networks.
•  Tensorflow framework.
•  Open CV.

As an initial exercise, build a model that can correctly identify images of two of your friends or family members.

Step 9. Create and complete a data project

You’re almost ready to introduce yourself to companies as a machine learning professional, but before others can validate your skills, you must objectively demonstrate all the topics you’ve learned.

If you’ve been diligent about the previous eight steps, chances are you know how to find a project, or more precisely an idea, that excites you, is helpful to people and helps you demonstrate your knowledge and skills. The Internet has provided us with rare opportunities to find such projects. At this stage, you should focus on learning the following topics:

•  Its Data collection, quality control, cleaning, and preparation.
•  Exploratory data analysis.
•  Create and select a model.
•  Prepare documentation related to the project.

last word

Machine learning and artificial intelligence are the skills needed now and in the future. Machine learning is a field where learning never stops, and often you may have to spend some of your time learning to maintain your position in a competitive market. However, if you start the journey well, you can understand how to take the next step on the learning path.