How To Become A Demanding Machine Learning Engineer in 2021| The Most Comprehensive Learning path by Learnbay
What exactly is machine learning?
Artificial intelligence (AI) is transforming every single sector, from travel (automatic fraud protection). AI is the sexiest profession in the 21st century. Machine Learning (ML) is a specialized aspect of AI.
This particular aspect of AI utilizes complex data algorithms for which the rules of engagement are set by data scientists and communicated to stakeholders.
Programming frameworks and large analytical tools associated with ML ensure that data pipelines going from raw data to optimized data models always run smoothly.
This reading helps in a precise understanding of how machine learning works in theory. Machine learning engineers ensure that their algorithms have up-to-to-date and real-time data at all times.
What does a machine learning engineer do?
ML engineers make sure that the models of algorithms are efficient in the real-time mode and remain performant for the long-term with minimal possible modifications. Development of applications to help businesses scale machine learning also falls under the responsibilities of the ML engineer. Although the roles and responsibilities of an ML engineer vary with the company and experience level, below are the few key job roles for the same.
Research on several prototypes of data models, algorithms, and real-time problems to develop dedicated applications as the solutions.
Identification and evaluation of appropriate tools and algorithms as per business necessities.
Designing and developing unique machine learning systems based on specific scenarios.
Collection and storing of data set followed by identifying and evaluating the most appropriate data for analysis as per the scenario under consideration.
The best-fit data representation strategy can solve the maximum possible queries and provide all the possible and relevant insights.
Running several Ml tests and remaining indulged in the reinforced learning of the same.
Lastly, you need to train the systems to carry out all the programmed tasks with a superlative degree of accuracy.
Now, before you step into the first stop of the learning path, you must become well-aware of the skills you need to acquire to ensure your machine learning career transition success because the entire learning path will be associated with the goal of mastering these skills. Below is the list of those skills set.
Moderate to the expertise level of programming knowledge (Python, R, C/C++).
In-depth knowledge of probability and statistical approach.
Tools and techniques for data modeling and evaluation, along with the ability to solve problems with core programming without the help of tools and applications.
Comprehensive knowledge of ML algorithms and libraries.
Competency in system designing and software development.
Good grip over natural language processing (NLP) and rudimentary ideas about neural network concepts.
The adequate level of proficiency in critical thinking, problem-solving, and communication skills.
8 Steps To Build Up a Successful Machine Learning Career
Step#1: Build a strong foundation of programming
The number of languages that are widely used in the MLB field are quite high. But the most widely used two are ‘R’ and 'Python.' So at the initiation point, it's best to choose one of these two masters. Later learn the other one, followed by gaining knowledge with the rest of the programming language as you start working.
In case you are coming from the non-programming background, it's better to choose python first because around 75% of ML projects run on python.
So, what to learn in programming to become an ML engineer?
Well, it's always better to learn from scratch, but both the world of Python and R is vast. And you don't have years after years for your career transition. Hence, you need to focus on the following programming aspects for comprehensive and time-bound learning for strategy.
Know about the strings, compilers, interpreter, bytecodes, modular programming
Learn working with Anaconda and Jupyter Notebook flavors (for both R and Python).
Start working with different types of loops.
Evaluate functions and modules for both R and python (e.g. 'asynchat,' 'array,' etc.)
Earn skill of decision controlling through coding.
Learn the commands and string for installing and executing different libraries.
Be proficient with database query language like SQL (Know the basics), MongoDB, and scripting language like Javascript (mainly JSON).
To have a basic Idea about Python programming, you can read the blog Introduction To Python Programming.
Apart from the above, spare a few hours on the following programming and applications.
GitHub (controlling process, authentication of SSH, source controlling of system, methods of locally working with Git, etc.)
Basic knowledge of C/C++, Java
If you are already familiar with programming them at stop1, your focus should be on the following.
Working with stack and ques under Data structures
Exploring several algorithmic models through programming
Learn the programming code and strings for filtering and searching algorithmic models
Step#2: Be able to play with applied mathematics and statistics
Although I have considered programming knowledge as stop1, mathematical and statistical proficiency is the top priority for a sustainable career in machine learning. But here, you need to expertise your statistical knowledge in such a way that you can implement those through programming. So it's best to acquire some basic ideas on programming then dive into several statistical models and techniques. Concentrate on the following modules.
Linear algebra
Multiplicative Calculus
Theorems of probabilities like Gaussian distribution, Bayes’ theorem, Bernoulli Distribution, etc.
Multiplicative calculus
Matrix and coordinate geometries.
T-test, Z-distribution, P-value, regression analysis using ANOVA
Here your key goal should be the understanding of scopes for statistical theories and techniques to real-life problems.
Step3: Learn the techniques of data cleaning/exploration/ visualization
Now, you have arrived at the most crucial step of your machine learning career path. At these stages, you need to apply your mathematical and statistical apprehension to your programming practices.
To land with a unique and promising result, you need to filter and explore your data with a higher degree of expertise. At this stage, you need to concentrate more on learning several ML tools, applications, and relevant programming libraries as follows.
Pandas
Numpy
SciKit-Learn
NLTK
SparkML
Hadoop
No matter how cool your data analysis and exploration techniques, or how interesting your interpretation, your entire effort becomes null if the visual aspect of your result is not worthy enough.
The ability to present conclusions with the maximum possible number of insights and scopes of solving most of the relevant queries determines the success rate of a data scientist/ ML engineer. So you should make yourself adequately proficient in handling top-notch data visualization tools. Below is a small list of such tools and libraries.
These are the most widely used tools only. Lots of other tools and libraries are there that you need to learn on the go through your working experience. The selection of tools and libraries for ML projects are completely dependent on organizational business needs. So, for a sustainable ML career, there is no limitation of learning tools.
If you belong to the non-programming background, you should read the blog 7 Data Science Tools For Non-Programmers.
Step4: Acquire knowledge about machine learning algorithms
According to the scenario and upgradation of the same with sequential needs, identifying the best-fit machine learning algorithm is the most crucial job role of an ML engineer. Below are the most popular ML algorithms that can be applied to different types of analytic data scenarios.
SVM
Random Forest
Linear regression
KNN
K-means techniques
Regression tree
Apriori algorithm
Logistic regression
As you enter the ML career with ongoing working experience, you will learn other relevant algorithms like gradient boosting, dimensionality reduction algorithm, etc.
To know in details, read the blog Top 10 Machine Learning Algorithms: The Key to Successful ML Career in 2021
Step#5: Learn the fundamentals of deep learning
Deep learning (DL) is the most complex branch of ML and AI. Although core deep learning projects need additional knowledge of artificial neural network for a sustainable ML career growth, you should be aware of the basics of this most promising data science branch (DL).so, once you become adequately proficient with general ML algorithm, programming, and statistical aspects gather knowledge based on Artificial neural network algorithm, Artificial neural network algorithm, computer vision, text mining, pattern recognizing, etc. To make yourself eligible for basic deep learning, mainly automation projects, earn comprehensive knowledge on the following two tools.
Pytorch
Tensorflow
Step#6: Practise ML problems to skill up your ability
Theoretically, you are now knowledgeable enough about machine learning, but still, you are not eligible.
Yes, to grab your first job in the ML field, you need to earn highly credible project experience and practice more in practical fields.
But where to do that?
You can participate in data science knowledge competitions arranged by Kaggle.
Besides, you can explore several online sources for collecting interesting data sets and do a project of your own. Some of such reliable data sources are Knoema, UCI machine learning repository, visual data, etc. In addition, Kaggle itself is a popular source in this regard.
If you are interested in doing self-paced data science project, read this blog, The Secrets of Completing Highly Creditable Hand-on Project in 2021| A Data Science Job Seeker Guide by Learnbay
Stop#7: Prepare for the ML job interviews
Being the most demanding job position of the century, the interview for a different ML engineer position will be super tough. So, revitalizing your CV with a maximum number of tools and hands-on projects is not going to work as a trick.
Instead, you need to prepare yourself for handling the toughest question asked by the interview panel. All you answer should be innovative, conceptual, and unique. Conventional answers are not appreciated anymore during the interview.
Participate in mock interview sessions, study machine learning interview questions, listen to podcasts and videos related to interview questions.
Check top 10 interview question on ML 2021
Step#8: Keep learning and growing
What techniques and tools you are using today will become obsolete in the next few years. So if you want to make your career grow upward, don't stop upgrading your knowledge every day.
Focus on the top-notch tools, technologies, and algorithms initially, but once you secure your first machine learning engineer job position, start learning on the rest of the tools and techniques gradually.
The best decision would be to take your learning a few steps forward to enter the core of deep learning because deep learning is going to be the future of every working domain in the next 10 years.
What to do next?
Maybe you have already planned your self-paced learning, and you have a complete step-by-step guide now. But for landing and winning over the highly competitive job market of data science, you need professional training.
To avail the most beneficial learning guidance and secure your ML career growth, you can join Learbay's IBM certified Masters Program in AI and ML.
Here, you will get the complete guide for learning statistics, programming from scratch and avail the benefits of pursuing real-time industrial projects. Check the projects already completed by former and present students here.
Each of the Learnbay courses offers job assistance with interview guidance and mock interviews.
Read the blog Why should you join Learnbay to learn Data Science if you are a working professional?
Want to join Learnbay Machine Learning Courses? Register for a profile review at www.learnbay.co.
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