Do you want to start a new career as a Machine Learning Engineer? If so, you’re not the only one! Here’s How To Become A Machine Learning Engineer and 6 essential skills required to Become A Machine Learning Engineer. More and more people are using technologies like Artificial Intelligence, Machine Learning, Information Science, etc. But these technologies are also buzzwords, and many people don’t know what they mean or what skills they need to learn. This article can help you get your dream job as a Machine Learning Engineer by listing all the different skills you need.
Machine Learning is a technology that focuses on how machines can learn from data independently, without much help from humans or explicit programming. This complicated field combines Artificial Intelligence with other technologies, such as Data Science. But this raises the question, Who is a Machine Learning Engineer? What’s the difference between him and a Data Scientist or a Data Analyst? First, let’s get this straight.
How to Become a Machine Learning Engineer: Skills Needed
1. Using mathematics
Math is a very important skill for a Machine Learning engineer to have. It’s also one of the first things kids learn in school, which is why it’s the first skill on our list. But do you wonder why you have to learn math? Well, math can be used in ML in many ways.
You can use different mathematical formulas to determine which ML algorithm is best for your data. You can also use math to set parameters and estimate confidence levels. Many ML algorithms are based on statistical modeling techniques, so it’s easy to understand them if you’re good at math.
You should know about linear algebra, probability, statistics, multivariate calculus, and distributions like Poisson, normal, binomial, etc. If you require to become a Machine Learning engineer, it can help you know a little about physics and math.
You must read the book “Machine Learning for Engineers” by Ryan G. Mc Clarren, and Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka (Author), Yuxi (Hayden) Liu (Author), Vahid Mirjalili (Author), Dmytro Dzhulgakov (Foreword)
Related Books for How To Become A Machine Learning Engineer
2. Computer Science Foundations and Programming
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This is another basic skill that a good machine-learning engineer needs to have. You must be familiar with several computer science topics such as data structures (stack, queue, tree, and graph), algorithms (searching, sorting, dynamic and greedy programming), space and time complexity, and so on.
You probably already know all of this if you have a bachelor’s degree in computer science. You should know a lot about programming languages, such as Python and R for machine learning and statistics, Spark and Hadoop for distributed computing, SQL for database systems, Apache Kafka for data pre-processing, etc.
Python is a popular programming language, especially for Machine Learning and Data Science. If you know how to use its libraries, like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc., you’ll be able to do a lot of cool things.
Recommended books: Foundation Mathematics for Computer Science: A Visual Approach by John Vince (Author), A Programmer’s Guide to Computer Science: A virtual degree for the self-taught developer by Dr. William M Springer II (Author), Nicholas R Allgood (Editor), Brit Springer (Illustrator)
3. Machine Learning Algorithms
What is an important skill for someone who wants to become a Machine Learning Engineer? It’s very crucial to recognize all the common machine learning algorithms so you know where to use which ones. Most machine learning algorithms fall into supervised, unsupervised, and reinforcement.
Some common ones are the Naive Bayes Classifier, K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc. So, to become an ML engineer, you should know a lot about these algorithms.
Recommended Books:
- Advanced Algorithms and Data Structures
- A Common-Sense Guide to Data Structures and Algorithms, Second Edition
- Machine Learning with R
- Understanding Machine Learning
4. Data Modeling and Evaluation
To be a machine learning engineer, you should know how to model and evaluate data. After all, data is your main source of income. Data modeling means understanding how the data is put together and looking for patterns that aren’t obvious at first glance.
It would help if you also used an algorithm that works well with the data to figure out what it means. For example, the form of machine learning algorithms, like regression, classification, grouping, dimension reduction, etc., depends on the data. A classification algorithm that works well with large amounts of data and is fast could be inexperienced eyes.
A regression algorithm that works well in terms of accuracy could be a random forest. In the same way, k mode is a clustering algorithm for categorical variables, while k means is one for probability. To help with data modeling and evaluation, you need to know all these details about different algorithms.
Most recommended books:
- The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling
- Data Modeling Made Simple, 2nd Edition
5. Neural Network
No one will ever forget how important Neural Networks are to ML engineers. The neurons in the human brain serve as a model for these Neural Networks. They have many layers, including an input layer that gets data from the outside world. This data then goes through several hidden layers that change it into data that the output layer can use.
These show a deep understanding of how sequential and parallel computations are used to analyze or learn from data. There are many different kinds of neural networks, such as the Feedforward Neural Network, the Recurrent Neural Network, the Convolutional Neural Network, the Modular Neural Network, the Radial basis component Neural Network, and so on. To become an ML engineer, you don’t have to know everything about neural networks in great detail.
Recommended Books:
6. Natural Language Processing (NLP)
Natural Language Processing is, of course, very important, and it is one of the most important parts of Machine Learning. NLP’s main goal is to teach computers all the different ways that people talk. This is done so that one day, machines can understand and interpret human language so they can understand how people talk better. Natural Language Processing is built on a lot of different libraries.
These libraries have different functions that can be used to help computers understand natural language. For example, the text can be broken up based on its syntax. Important phrases can be pulled out, and unnecessary words can be taken out, etc. You might already know some of these libraries, or even just one, like the Natural Language Toolkit, which is the most popular platform for making NLP-related apps.
Recommend Books:
- Getting Started with Natural Language Processing
- Natural Language Processing with Transformers
- Natural Language Processing with Python
Conclusion Machine learning is becoming more common and is used in almost every field. Whether it’s medicine, cybersecurity, cars, or other things, these fields look into what machine learning can do. It’s clear that learning more about ML and becoming a Deep Learning Engineer is a good idea and may even be a very smart career move!