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In 2022, more people will adopt AI. Most notably, text-to-image models (AI art) have become very popular. Search engines have been replaced by advanced chatbots such as ChatGPT. Open-source alternatives such as PaLM+RLHF are on the horizon, making AI and machine learning more accessible to novice developers. However, becoming a true machine learning engineer requires more skills than just scripting and coding. He is currently one of the most in-demand technical jobs in the world.
As such, more and more people are starting to see it as a potential career path. However, machine learning and AI are evolving so rapidly that even the most experienced machine learning engineer has to keep up to date with the latest practices. is difficult to keep. So what skills will you need to become or improve as a machine learning engineer in 2023? This guide will answer these questions.
So what’s the difference between a machine learning engineer and a software developer or engineer? Machine learning engineers are the next evolution of software developers.
They are highly skilled programmers who specialize in creating algorithms that facilitate software and machine automation. Basically, if you have a passion for programming or are already a software developer, you are already on your way to becoming a machine learning engineer. But what are the perks? Traditional software or why not he stays a web developer?
Leveling up to be a Machine Learning Engineer will enhance your job portfolio, open more employment opportunities, and enable you to actively participate in advances in AI and the Symbiotic Web. A very fulfilling career. You can even work freelance with individual clients, so you don’t have to go down the hiring path. But which skills will take you to the next level?
1. Deep learning
Deep learning, a subset of machine learning, is essential because it can analyze and interpret vast amounts of data. It uses artificial neural networks inspired by the structure of the human brain to detect patterns in data and learn from them to make accurate predictions.
One of the main uses of deep learning is computer vision, where it is used to analyze and classify images and videos. For example, it can be used to recognize faces, identify objects in images, and detect anomalies in medical images.
Deep learning is also used in natural language processing to analyze and interpret human language. This includes tasks such as sentiment analysis, machine translation, and language modeling. Additionally, deep learning is used in speech recognition to transcribe spoken words and recognize speech patterns.
As a machine learning engineer, it’s important to have a good understanding of deep learning and proficiency in using deep learning tools and libraries such as TensorFlow, Keras, and PyTorch.
2. Natural language processing
Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction of computers with human language. NLP aims to enable machines to understand, interpret, and generate human language, including written and spoken text. This includes developing algorithms and models that can analyze linguistic data and extract meaning from it.
Image from Data Science Dojo
NLP is important in machine learning because it allows machines to communicate with humans in a natural way. This is especially important as more and more applications are being developed that require interaction between humans and machines. Without NLP, these interactions are limited to simple commands or responses, greatly reducing their usefulness and potential impact.
3. Statistical analysis
Statistical analysis is the process of collecting, analyzing, and interpreting data to gain insights and make informed decisions. Statistical analysis is an important skill in machine learning as it provides the mathematical foundation for many machine learning algorithms. Statistical analysis is used to identify patterns in data, test hypotheses, and make predictions.
Major tools and libraries used in statistical analysis include R, Python, SAS, and SPSS. Familiarity with these tools and languages is critical to proficiency in statistical analysis.
4. Data preparation
Data preparation is the process of collecting, cleaning, and organizing data before using it to train a machine learning model. This is a critical step in any machine learning pipeline, as the quality of the data used to train the model greatly affects the accuracy of predictions. Poor quality data leads to inaccurate or unreliable predictions, while high quality data helps create more accurate models.
Data preparation includes tasks such as data cleaning, data transformation, feature engineering, and data integration.
Key tools and libraries used for data preparation include Python’s pandas, NumPy, scikit-learn, and R’s dplyr and tidyr. A good understanding of these tools and libraries and programming is important. Become proficient in data preparation using languages such as Python and R.
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Additionally, it is important to have a good understanding of statistical concepts such as probability and hypothesis testing, as well as knowledge of database systems and SQL.
5. Programming
Programming is an essential skill for machine learning engineers as it is the primary tool used to create and train machine learning models. Programming languages such as Python, R, and Java are commonly used in machine learning due to their ease of use and availability of machine learning libraries and frameworks.
One of the best ways to acquire programming skills is by doing machine learning projects. This gives you hands-on experience applying programming concepts to real problems. Working on a machine learning project also gives you the opportunity to learn from and collaborate with other developers.
Important programming concepts to learn as a machine engineer include data structures, algorithms, object-oriented programming, and software development principles such as versioning, debugging, and testing.
Becoming a machine learning engineer is a multifaceted career that takes a lot of time and effort. So you have to work on your learning and divide your time practically. Being able to do this is a skill in itself. That said, there are some skills that you shouldn’t spend too much time honing.
For example, some sources say that applied mathematics is an absolute must in your skill set to be an effective ML engineer. This is not true. There is very little mathematics involved in modern applied machine learning. However, it can still be a great skill. It teaches you how to approach problems systematically and logically. Hard-applied mathematics is mostly unnecessary, though.
Furthermore, you shouldn’t waste your time honing your modeling skills. Much of machine learning modeling has been democratized. As such, many of the required models are available and automated. However, you should be able to identify the best model to use for your particular problem. Ultimately, modeling is a solved problem and one of the easiest parts of an ML engineer’s job.
get certified
If you plan to pursue a career as a machine learning engineer, you should make sure you have the right certifications. Most companies require a bachelor’s degree in computer science or a related field such as applied mathematics or physics.
Mostly ironically, many recruiters are using automation and machine learning to identify the best candidates for the position. Again, if your goal is to get a job, you should secure as many job interviews as possible.
One of the criteria these recruitment tools focus on is certification. In some cases, it doesn’t matter who issued the certificate. As long as it’s on your resume. Certifications must be associated with skills included in the guide above. Therefore, you need specialized qualifications in all programming languages, machine learning applied statistics, how to use machine learning libraries, etc.
Build a public repository
While waiting for a response When it comes to applying for a job, you should consider opening a public GitHub repository filled with personal machine learning projects. This repository should be well maintained and committed regularly. You can even include it in your resume so potential employees can see a sample of your work.
Like most tech jobs, machine learning engineers need to be students and learners throughout their lives. You need to stay up to date with the latest frameworks, technologies and practices. Perseverance, resilience, and openness to learning are good skills to hone as an ML engineer. It’s hard work and takes time, but the journey is fulfilling. Good software developers and machine learning engineers are rarely on the verge of losing their jobs. In the guide above, she explains her 5 machine learning skills every machine learning engineer should know about her in 2023. Bookmark it and refer to it whenever you get lost or want to improve as an ML engineer.
Nahla Davis Software developer and technical writer. Interestingly, before devoting himself to his full-time technical writing job, he was the lead programmer for Inc. 5,000’s experiential branding organization, with clients such as Samsung, Time Warner, Netflix, and Sony. I was.
