The Complete Guide to becoming a Machine Learning Engineer in 2022
AlphaGo, Google’s DeepMind artificial intelligence (AI) program, is the first computer program to ever beat a human Go player!
Built using machine learning techniques and a system that integrates manual craft rules, AlphaGo’s legacy is spreading across the globe.
Artificial intelligence and machine learning have started to take the technology world tremendously. In addition, job opportunities in engineering learning are increasing dramatically.
- Globally, the machine learning market is estimated to be worth $ 31 billion by 2024, according to Market Research Future.
- Key players in the market include tech giants like Microsoft Corporation. (UD), Intel Corporation (UD), Google (UD), IBM Corporation (UD), Amazon.com, Inc. (UD), Nuance Communications (UD), Facebook, Inc. (UD), Apple Inc. (USA). )), Nuance Communications (USA), Cisco Systems, Inc. (UD), and Wipro Limited (India).
Big companies adopting engineering learning and artificial intelligence across industries reflect how jobs in the field will ultimately be driven.
According to LinkedIn, about 62,267 jobs in the United States currently list engineering learning as an essential skill, and about 151,390 jobs.
So it’s no surprise to see the number of AI and machine learning jobs soaring.
With job trends increasing in both areas, this is definitely a great time to pursue a career in 2022
Machine Learning Engineer: Job Roles and Responsibilities
An engineering learning engineer is responsible for entering data into a model defined by a data scientist. Here are two possible scenarios:
As a machine learning specialist:
- Responsible for analyzing and selecting features that depend on the information process, ie develop feature vectors for each object for analysis.
- Developing algorithms for engineering learning.
As a deep learning specialist:
- Development of artificial neural networks.
- Create algorithms to analyze and identify the properties of object processes for better structuring. For example, analyzing and sorting content such as text, audio and images.
Generally, as a machine learning engineer:
Use detailed mathematical skills to work with algorithms and perform computing skills.
Present results and solve complex problems to make plans effective.
Build and model data pipelines in close synchronization with data engineers.
- Demonstrate a wide range of the basics of computing – the ability to compute, computer architecture, data structures and algorithms.
- Implementation of artificial intelligence and engineering learning algorithms.
- Build algorithms from statistical modeling procedures.
- Build and maintain scalable machine learning solutions.
- Collaborate with stakeholders to analyze business problems and find relevant solutions.
- Manage the data infrastructure and pipelines that need to be generated.
- Display end-to-end information from the developed application.
- Analyze large amounts of data to gain valuable insights critical to decision making.
- Serves as support for product managers and engineers and helps implement engineering learning in products.
- Dive in and leverage best practices to improve your existing machine learning infrastructure.
- Leverage data modeling to evaluate strategies and help identify patterns to predict unforeseen challenges.
- End-to-end communication with non-programmers, explaining complex processes to them.
What Machine Learning Engineers Have to Know
Machine learning jobs are great for technical professionals with excellent analytical skills. Detailed knowledge of the following areas is required:
- Mathematics and Statistics
- computer science
Technical skills include:
- Big Data
- Apache Spark
- natural language processing
- deep learning
- digital currency
- computer vision
However, to perform standard tasks in machine learning, you need to have the following prerequisites:
- Study discrete mathematics, statistics and probability theory
- A solid grasp of engineering learning algorithms
- Ability to use data warehouse
- Analyze and model data using Python, R or programming languages such as SPSS, SAS
- Data visualization with Matplotlib tools
Artificial intelligence and machine learning are among the hot jobs in the labor market.
With many companies investing in these technologies, the demand for machine learning and artificial intelligence engineers will increase dramatically.
Now that you know the skills you need to master, we’ll discuss further what you need to learn over the next three months.
Do you want to master engineering learning in 3 months? Here’s what you need to learn
Month 1 – Type and Algorithms
- Week 1: Linear Algebra
- Week 2: Mathematical Analysis
- Week 3: Probability Theory
- Week 4: Algorithms
Month 2 – Machine Learning
- Week 1: Python for Data Science
- Week 2: An Introduction to Machine Learning
- Weeks 3 and 4: Machine Learning Project Ideas
Month 3 – Deep Learning
- Week 1: An Introduction to Deep Learning
- Week 2: Deep Learning Course
- Weeks 3 and 4: Deep learning projects
GitHub and Kaggle are two great platforms for you to start projects and build your own applications. Projects provide an overview of your practical experience with technology. Having multiple projects in your portfolio is an added advantage as employers look for practical skills.
These three months of hard work and effort will give you practical knowledge of engineering learning.
List of Best Machine Learning Online Courses You Can’t Lose:
- Coursera Machine Learning Course: This course offered by Stanford University, gives you the opportunity to learn about engineering learning and its practical uses.
- Free Machine Learning Courses from Udacity: Free Udacity Machine Learning Courses will take you on a journey through one of the most exciting careers of this era. This course is free and will take you nearly 10 weeks to complete your journey as a Machine Learning Engineer. With this course, you will be able to learn the end-to-end process of how to research data using engineering learning.
- Nanodegree Machine Learning Engineer (MLND) Program from Udacity: The MLND program is ideal for those who wish to gain hands-on experience with AI and machine learning techniques. The program provides applicants with projects and quizzes to help them get practical.
- Udacity AI Academy: The AI Academy offers candidates interesting programs to get started in the fields of artificial intelligence and machine learning. You can choose relevant courses according to your interests.
- From the Udacity Deep Learning Nanodegree Program: Deep learning is becoming an important aspect of artificial intelligence. Therefore, candidates who can build and apply their own deep neural networks will be the most popular candidates in the tech industry right now.
List of must-attend conferences for machine learning engineers:
You may be wondering what AI and engineering learning leaders have to say, well, go to a conference.
- Here is a list of some of the meetings you need to attend:
- NVIDIA GPU Technology Conference: April 12-16, 2021
- NeurIPS: Neural Networks Virtual Conference. 31 March to 14 December 2021
- QCon Software: May 17-28, 2021
Without further information, you can still get early bird tickets and register for the conference. Hurry while you still have a chance.
Are you all ready for the interview? Here’s how you need to prepare
This may be the most challenging part of having a career in engineering learning because this is where it gets difficult. Despite the skills and knowledge, you need to tell employers what they really want to hear. Therefore, you need to approach the interview wisely and wisely.
Here’s how you can get it –
Step 1: Prepare by addressing the challenges of coding
Although difficult, you can prepare and assess yourself and assess whether you are a potential candidate. Here are some things you can start doing:
Most employers may require solving coding challenges on HackerRank even before the interview itself. Before you know it, it is recommended that you start solving challenges and working on projects on HackerRank.
Taking part in Advent of Code and Google Code Jam competitions is an added bonus.
Additionally, having a variety of engineering learning projects to show employers can differentiate you from other applicants applying for the same job role.
Step 2: Apply for a job
The next step is to start looking for related jobs. Don’t apply for jobs at random, make sure you list your areas of expertise and start your job search based on your skills.
Your resume should showcase more projects, practical applications and skills than just theoretical achievements. Remember, employers are looking for candidates with hands-on technology experience. Make sure you have enough conditions for them to hire you. As artificial intelligence and machine learning become hot topics, people are beginning to develop skills in the field.