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Essential skills required to become a Machine Learning Engineer
Let's get to know how machine learning works.
Machine
learning is the subfield of Artificial intelligence and it enables computers to
learn on their own without being explicitly programmed. Let's get to know how
machine learning works. Machine learning refers to an algorithm that is being
ingested with a massive amount of data, extracts patterns out of data, and
finally converts those patterns into actions. Machine learning started as a
very small concept in the beginning, and eventually, it has become part of
every industry to embrace new opportunities. Let’s consider a scenario here on
how machine learning helps organizations. Have you ever come across the
products, appearing in front of your screen or as an advertisement while you are
browsing through the internet that is being liked by you or visited by you
earlier? Probably you may have. You will see or receive product or service
suggestions just because of the machine learning algorithms that work behind
the scenes. The ultimate goal of these algorithms is to fulfill the final goal
of any organization. That's how machine learning induces people to buy the
products and services of an organization and helps the organizations in making
profits. Your Facebook will show you the news feed that suits your requirement,
isn’t it? It is just because of the machine learning algorithms, not only it
happens with Facebook but also with all other social media platforms and
e-commerce sites. We have come to know how machine learning application works,
and along the way, it is also creating a ton of employment opportunities in the
field of IT for data scientists and software engineers. To kick start your
career in Machine Learning Certification or to be a part of this futuristic technology, you
need to learn some specific skills Required for Machine Learning, and the pay
scale for the machine learning developer is also very high compared to other
jobs.
Who
is a Machine Learning Engineer?
Since
Machine Learning is deeply linked with Data Science, there are some overlaps in
the job description of a Data Scientist, Data Analyst, or a Machine Learning
Engineer. However, the main differences are what they focus on. Data Scientists
and Analysts are mainly focused on obtaining insights from the data and
presenting them to the higher-ups in the organization to make important
decisions. This involves having some knowledge of ML algorithms. However,
Machine Learning Engineers are excluding focused on Machine Learning. Their aim
is to create software components that can work with minimal human supervision
and help in obtaining insights from the data they are provided with. This is
why there is so much focus on Computer fundamentals and software development
for a Machine Learning Engineer because that is their specialization.
Now
let’s see the skills you need to become a Machine Learning Engineer. While the
first two of these are quite basic that you may have even learned in your high
school or bachelor’s, they become more complex and domain-specific as you move
through the list.
·
Maths
is quite an important skill in the arsenal of a Machine Learning engineer. It
is also one of the basic subjects that are taught right from school and that’s
why it is the first skill on our list. But are you wondering why you need maths
at all? (Especially if you don’t like it?!!) Well, maths can have many uses in
ML. You can apply various mathematical formulas in selecting the correct ML
algorithm for your data, you can use maths to set parameters, approximate
confidence levels, Many of the ML algorithms are applications derived from
statistical modeling procedures and so it’s very easy to understand them if you
have a strong foundation in Maths. Some of the important topics of maths that
you need to know include linear algebra, probability, statistics, multivariate
calculus, distributions like Poisson, normal, binomial, etc. Apart from Maths,
having some knowledge of Physics concepts can also be beneficial if you want to
become a Machine Learning engineer.
·
This
is another basic requirement for becoming a good machine learning engineer. You
need to be familiar with different CS concepts like data structures (stack,
queue, tree, graph), algorithms (searching, sorting, dynamic and greedy
programming), space and time complexity, etc. The good thing is you probably
know all of this if you have done your bachelor’s in computer science! You
should be well versed in different programming languages like Python and R for
ML and statistics, Spark and Hadoop for distributed computing, SQL for database
management, Apache Kafka for data pre-processing, etc. Python is a very popular
programming language especially for Machine Learning and Data Science so it’s
great if you are well versed in its libraries like NumPy, Pandas, Matplotlib,
Scikit-learn, TensorFlow, etc.
·
Computer
Science fundamentals are vital for the Machine Learning engineer; A good grasp
of Data Structures & Algorithms such as Multi-dimensional arrays, arrays,
stacks, queues, trees, etc. ML engineers should also be able to write
algorithms that are capable of searching, sorting, and optimizing code. To
further add, an understanding of computability, complexity, and computer
architecture are all necessary. Since the final output from a Machine Learning
engineer is typically deliverable software, ML engineers must have a good idea
of how each of the different pieces of software works and communicates to build
suitable interfaces for your component.
·
What
is a very important skill in becoming a Machine Learning Engineer? Obviously,
it’s very important to know all the common machine learning algorithms so that
you know where to apply what algorithms. Mostly ML algorithms are divided into
3 common types namely, Supervised, Unsupervised, and Reinforcement Machine
Learning Algorithms. In detail, some of the common ones include Naïve Bayes
Classifier, K Means Clustering, Support Vector Machine, Apriori Algorithm,
Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc. So
it’s good if you have a sound knowledge of all these algorithms before
beginning your journey as an ML engineer.
·
As
a machine learning engineer, you should be skilled in data modeling and
evaluation. After all, data is your bread and butter! Data modeling involves
understanding the underlying structure of the data and then finding patterns
that are not obvious to the naked eye. You also need to evaluate the data using
an algorithm that is suitable for the data. For example, the type of machine
learning algorithms to use such as regression, classification, clustering, dimension
reduction, etc. depends on the data. A classification algorithm well suited to
large data and speed may be naive beyes, or a regression algorithm for accuracy
might be a random forest. Similarly, a clustering algorithm for categorical
variables is k mode while for probability is k means. You need to know all
these details about various algorithms to contribute to data modeling and
evaluation effectively. Data modeling is the process of estimating the
underlying structure of a given dataset, with the goal of finding useful
patterns (correlations, clusters, eigenvectors, etc.) and/or predicting
properties of previously unseen instances (classification, regression, anomaly
detection, etc.). A key part of this estimation process is continually
evaluating how good a given model is. Depending on the task at hand, you will
need to choose an appropriate accuracy/error measure (e.g. log-loss for
classification, sum-of-squared-errors for regression, etc.) and an evaluation
strategy (training-testing split, sequential vs. randomized cross-validation,
etc.). Iterative learning algorithms often directly utilize resulting errors to
tweak the model (e.g. backpropagation for neural networks), so understanding
these measures is very important even for just applying standard algorithms.
·
Standard
implementations of machine learning algorithms are widely available through
libraries/packages/APIs (e.g. scikit-learn, Theano, Spark MLlib, H2O,
TensorFlow etc.), but applying them effectively involves choosing a suitable
model (decision tree, nearest neighbor, neural net, support vector machine,
ensemble of multiple models, etc.), a learning procedure to fit the data
(linear regression, gradient descent, genetic algorithms, bagging, boosting,
and other model-specific methods), as well as understanding how hyperparameters
affect learning. You also need to be aware of the relative advantages and
disadvantages of different approaches, and the numerous gotchas that can trip
you (bias and variance, overfitting and underfitting, missing data, data
leakage, etc.). Data science and machine learning challenges such as those on
Kaggle are a great way to get exposed to different kinds of problems and their
nuances.
·
Nobody
can forget the importance of Neural Networks in the life of an ML engineer!
These Neural Networks are modeled after the neurons in the human brain. They
have multiple layers that include an input layer that receives data from the
outside world which then passes through multiple hidden layers that transform
the input into data that is valuable for the output layer. These demonstrate a
deep insight into parallel and sequential computations that are used to analyze
or learn from the data. There are many different types of neural networks like
Feedforward Neural Network, Recurrent Neural Network, Convolutional Neural
Network, Modular Neural Network, Radial basis function Neural Network, etc.
While it’s not necessary that you understand all these neural networks in
detail to become an ML engineer, it’s important that you know the core
fundamentals. And you can always learn the rest on the way!
·
Natural
Language Processing is naturally quite important and a fundamental part of
Machine Learning. In essence, NLP aims to teach the human language with all its
complexities to computers. This is so that machines can understand and
interpret the human language to eventually understand human communication in a
better way. There are many different libraries that provide the foundation of
Natural Language Processing. These libraries have various functions that can be
used to make computers understand natural language by breaking the text
according to its syntax, extracting the important phrases, removing extraneous
words, etc. You can be familiar with some or even one of these libraries like
the Natural Language Toolkit which is the most popular platform for creating
applications relating to NLP.
·
And
finally, we come to a skill that is a soft skill and may not be considered that
important. However, if you are good at communication skills, it can make a
world of difference in your career trajectory. That’s because while you
understand the data and the insights obtained using machine learning better
than anyone else, it is equally important that you can convey these insights to
a non-technical team, your shareholders, or clients. This can also involve data
storytelling where you should be able to present your data in a storytelling
format with a beginning and ending at concrete results that you have obtained
from the data using machine learning. That’s because eventually, the data
analysis is less important to a company than the actionable insights that can
be obtained from the data. And if you are able to convey these insights, then
the sky’s your limit in your profession!
·
A
formal characterization of probability (conditional probability, Bayes rule,
likelihood, independence, etc.) and techniques derived from it (Bayes Nets,
Markov Decision Processes, Hidden Markov Models, etc.) are at the heart of many
machine learning algorithms; these are a means to deal with uncertainty in the
real world. Closely related to this is the field of statistics, which provides
various measures (mean, median, variance, etc.), distributions (uniform,
normal, binomial, Poisson, etc.) and analysis methods (ANOVA, hypothesis
testing, etc.) that are necessary for building and validating models from
observed data. Many machine learning algorithms are essentially extensions of
statistical modeling procedures. Probability helps in predicting future
consequences, and the majority of the algorithms in machine learning work based
on uncertain conditions and have to make reliable decisions. Mathematical equations in probability such as
derivative techniques, Bayes Nets, and Markov decisions would help machine
learning in predicting the future. Tools and tables are very essential in
machine learning to create models from data. Statistics and its branches such
as analysis of variance and hypothesis testing are crucial for building
algorithms. As machine learning algorithms are built upon statistical models,
we can understand how important statistics is for machine learning is. That's
how statistics play an essential role in developing the algorithms. So it is very vital to gain knowledge on
statistical tools to speed up your career in machine learning.
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