How Machine Learning Became a 2022 Tech Trend?

Machine Learning (ML) has transformed our lives for the past few decades. From taking pictures with a blurry background & focused face to virtual assistants such as Alexa, Google Assistant and Siri answering our queries, we are significantly dependent on applications that execute ML at their core.

ML engineering combines data science and software engineering. A data scientist will scrutinize the obtained data and extract actionable insights; an ML engineer will develop the self-running software that leverages the extracted data and automates predictive models.

These engineers are experienced in basic data science skills like quantitative analysis methods, stats, data structures & modelling, and developing data pipelines, while also having fundamental software engineering skills.

With so much innovation around this emerging technology, it is no wonder that any enthusiast who is looking forward to advancing their career in software technology & programming would choose ML as a base to set their job.

In this article, we shall see what ML and ML engineer is? Its significance, different types, and so on.

What is Machine Learning (ML)?

ML is an AI and computer science branch that focuses on using algorithms and data to mimic how we humans learn, gradually enhancing its accuracy. Because of new computing techs, ML today is not like it used to be. Its potential to automatically apply complex mathematical calculations to big data repeatedly and faster is a recent development.

As ML is a central factor of data science, through statistical methods - algorithms are trained to make predictions, unveiling crucial information within data mining projects. These insights drive decision-making within businesses, affecting key growth metrics.

Why Machine Learning Matters?

Like the title, we will be going through the significance or benefits of ML in business and how organizations move ahead with the implementation of ML.

The main objective of ML technology is to help companies improve their overall productivity, decision-making process and workflow. As machines start learning through algorithms, it will assist ventures in resolving data patterns that help the organization make better decisions without the need for humans.

Below are a few benefits ML technology offers:

Business Transformation

ML technology has been transforming businesses with its potential to offer valuable insights. The insurance and finance sectors use the tech to determine meaningful patterns within big data sets, prevent fraud, and provide customized plans to various customers.

When considering the healthcare industry, fitness and wearable sensors powered by ML tech allow users to take charge of their health, accordingly reducing the pressure of healthcare experts.

This technology is also leveraged in the oil and gas industry to identify new energy sources, analysis of ground minerals, system failure predictions, etc.

The technologies evolve to new heights each day, and ML has been amplifying business or organizational growth. Global companies are heading towards applying ML in their sectors to augment. This futuristic trend highlights how machine learning plays a vital role in business transformation, and excelling in the adequate skills will keep you on the path where opportunities are boundless.

Prompt Analysis and Assessment

Since businesses revolve around a surplus count of data moving in and out of an organization, employees find it tedious to deal with it daily. Thanks to the innovation of ML, the algorithms can aid the workforce in conducting prompt analysis and strategical assessments.

When an employee creates a business model by browsing through many data sources, they get to see essential variables. Similarly, ML can assist you in understanding customer feedback, interaction, and behaviour, thus resulting in seamless customer acquisition and digital marketing strategies.

Instantaneous Predictions

A feature that fascinates the ML enthusiast is the rapid processing of insightful data from myriad sources. This helps in making instantaneous predictions that can be valuable for organizations.

ML algorithms offer meaningful data on various customers' buying and spending patterns, which allows businesses to devise procedures that can reduce loss and maximize profits.

It also helps determine the backlogs of marketing campaigns and customer acquisition policies. With these data, employees can adjust their business procedures and enhance overall customer satisfaction.

An additional benefit of the ML algorithm is the churn analysis, where we can identify those customer segments that are likely to leave the business brand.

What are the Different Types of ML?

Basically, machine learning is divided into 3 areas and they are as follows:

Supervised Learning

Here, labelled data is used for training the data. The input goes through the ML algorithm and is leveraged to train the model. Once it’s done, we can feed unknown data into the trained ML model and obtain a new desired response.

Top-notch algorithms that are used for supervised learning are:

• Naive Bayes

• Polynomial Regression

• Decision Trees

• Linear Regression

• K-nearest Neighbors

Unsupervised Learning

In this type of ML, the training data is unlabelled and unknown. Without labelled data, the input can't be guided to the ML algorithm, where unsupervised learning comes into action.

This data is used in the algorithm for training the model. The trained model searches for a pattern and generates the desired outcome. In this case, it is similar to the Enigma machine trying to break code without human intervention.

Top-notch algorithms that are used for unsupervised learning are:

• Principal Component Analysis

• Fuzzy Means

• Apriori

• Partial Least Squares

• K-means Clustering

• Hierarchical Clustering

Reinforcement Learning

The ML algorithm identifies data through a trial-and-error process in reinforcement learning and then decides what action yields higher benefits. 3 significant components of this ML type are - the agent, the environment, and the actions.

The agent is the decision-maker, the environment consists of everything that the agent interacts with, and lastly, the actions are what the agent does. This type of ML occurs when the decision-maker chooses activities that increase the expected profit over a given period.

What are ML Engineers?

ML engineers are programming experts who research, develop, and create self-running software to automate the predictive models. These engineers develop AI systems that use a colossal amount of data to produce and develop algorithms capable of learning and making decisions.

To develop top-notch performing ML models, the organization requires ML engineers to assess, analyze, organize data, perform tests and optimize the learning processes.

What are the Technical Skills Required for ML Engineers?

We have learned how ML application operates, followed by numerous job opportunities in the IT field for software engineers and data scientists. To be a part of ML technology, you need specific technical and soft skills.

Firstly, we will see technical skills required for an engineer, and they are:

Neural Network Architecture

Neural networks, also called Artificial Neural Network (ANN) or Simulated Neural Network (SNN), are the predefined algorithm sets used for ML task implementation.

They provide models and play a vital role in this futuristic technology. Now, ML seekers must be skilled in neural networks because it offers an understanding of how our brain works and assist in model & simulating an artificial one. It also provides in-depth knowledge about parallel and sequential computations.

Some of the neural network areas that are essential for ML are:

• Boltzmann machine network

• Convolutional neural networks

• Deep auto-encoders

• Long short-term memory network (LSTM)

• Perceptron

Natural Language Processing (NLP)

It is a branch of linguistics, AI & computer science that, when combined with ML, Deep Learning (DL), and statistical models, enables computers to process human language in the form of spoken words and text and understand its whole meaning with writer's intent.

Several techniques and libraries of NLP technology used in ML are:

• Word2vec

• Summarization

• Genism & NLTK

• Sentiment analysis

Applied Mathematics

ML is all about creating algorithms that can learn data to predict. Hence, mathematics is significant for solving data science projects DL use cases. If you wish to be an ML engineer, you must be an expert in the following math specializations.

But why math? There are several reasons why an ML engineer needs math or should depend on it. For instance, choosing appropriate algorithms to suit the final outcomes, understanding & working with parameters, deciding validation approaches, and estimating the confidence intervals.

If you are wondering about the math proficiency level one must hold to be an ML engineer, then it depends on the level at which the engineer works. The below-shown pie chart will give you an idea of how significant various math concepts are for an ML engineer.

Data Modeling & Evaluation

An ML has to work with a colossal amount of data and use them in predictive analytics. In such a scenario, data modelling & evaluation becomes beneficial in dealing with these bulks and estimating the final model's good.

Hence, the following concepts are must learn skills for an ML engineer:

• F1 Score

• Log loss

• Mean absolute error

• Confusion matrix

• Classification accuracy

• Area under curve

• Mean squared error

Video & Audio Processing

This processing concept is different from NLP because audio & video processing can only be applied to audio signals. For this, the following ideas are essential for an ML engineer:

• TensorFlow

• Fourier Transform (FT)

• Music theory

Advanced Signal Processing Techniques

Signal processing targets analyzing, modifying, and synthesizing signals to minimize noise and extract the provided signal's best features. For this, the techniques leverage certain concepts like spectral time-frequency analysis, convex optimization theory & algorithms, and algorithms (bandlets, shearlets, curvelets, wavelets, etc.)

Reinforcement Learning

Reinforcement learning is an ML area that takes suitable action by employing several machines and software to increase rewards in a particular scenario. Though it plays a vital role in understanding and learning DL & AI; however, it is beneficial for ML beginners to have an insight into the fundamental concept of reinforcement learning.

What are the Soft Skills Required for ML Engineers?

While ML engineering is a technical job, soft skills such as problem-solving, collaboration with others, communication, time management, etc., are what lead to successful completion and delivery of the project.

Here are some of the soft critical skills an ML engineer must possess:

Team Work

ML engineers are often at the core of AI initiatives within a company, so they naturally work with software engineers, product managers, data scientists, marketers and testers. The potential to work closely with others and contribute to a supportive working environment is a skill many recruiters seek in ML engineers.

Problem-solving

The potential to solve an issue is a significant skill required for both software & ML engineers and data scientists. ML focuses on solving challenges in real-time, so the potential to think creatively and critically about the problem and develop solutions accordingly is a fundamental skill.

Open to New Learning

The fields of ML, AI, DL and data science are drastically evolving, and those who have earned a degree and working as an ML engineer find ways to learn new things through workshops, boot camps and self-study.

Whether learning the latest programming languages or mastering new tools, the most effective ML engineers are open to new learning skills and constantly refreshing their learnt toolkits.

Communication

ML engineers must possess excellent communication skills when communicating with shareholders regarding the project objectives, timeline, and expected delivery. We know that ML engineers collaborate with data scientists, marketing & product teams, research scientists, and more; hence, communication skill is crucial.

Domain Knowledge

To develop self-running software and optimize solutions leveraged by end-users and businesses, ML engineers should have an insight into the requirements of business demands and the type of issues the software is solving. Without domain knowledge, an ML engineer's recommendation may lack accuracy, their task may overlook compelling aspects, and it might be strenuous to evaluate a model.

What are the Programming Skills Needed for ML Engineers?

Machine learning is all about coding and feeding the machines to carry out the tasks. ML engineers must have hands-on experience in software programming and related subjects to provide the code.

Let's see the programming skills an ML engineer is expected to have knowledge on:

ML Algorithms & Libraries

ML engineers are expected to work with myriads algorithms, packages, and libraries as part of a daily task. ML engineers must be skilled with the following ML algorithms and libraries:

• Knowledge in packages & APIs - TensorFlow, Spark MLlib, scikit-learn, etc.

• Decide and choosing of hyperparameters that impact the learning model & the result.

• Algorithm selection provides the best performance from support vector machines, Naive Bayes Classifiers and more.

• Expert in model handling like decision trees, neural net, SVMs and deciding which is suitable.

Unix

ML engineers require most servers and clusters to operate are Linux (Unix) variants. Though they can be performed on Mac & Windows, more than half of the time, they are required to run on Unix systems only. Therefore, having good knowledge of Linux & Unix is vital to being an ML engineer.

Computer Science Fundamentals & Programming

Engineers must apply the concepts of computer science and programming accurately as per the situation. The following ideas play a significant role in ML and are a must on the skillset list:

• Algorithms: search, sort, optimize, dynamic programming

• Computer architecture: memory, bandwidth, cache, distributed processing and more.

• Data structures: queues, trees, stacks, graphs and multi-dimensional arrays

• Complexity & computability: big-O notation, P vs NP, approximate algorithm, etc.

Distributed Computing

Being an ML engineer means working with massive data sets and focusing on one isolated infrastructure, and spreading among system clusters for data sharing. In such a situation, these engineers must know the concept of distributed computing.

Software Engineering & System Design

ML engineers must have sound knowledge of the following areas of software programming & system design, as all they do is code:

• Top-notch measures to circumvent bottlenecks & develop user-friendly outcomes.

• Algorithm scaling with data size.

• Interacting with different working components and modules using library calls, REST APIs and database queries.

• Fundamental software design methodologies and coding like testing, requirement analysis and version management.

What are the Key Programs for Mastering ML Engineers?

In addition to an in-depth knowledge of programming languages such as SQL, C++, Python and Java, several ML engineers are also experts in the following tools:

• AWS ML

• IBM Watson

• TensorFlow

• R

• MATLAB

• Google Cloud ML Engine

• Weka

• Hadoop

• Apache Kafka

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world.

For more information on how iCert Global can help you to achieve your ML, AI and Deep Learning (DL) Certification goals, please visit our website.

https://www.icertglobal.com/

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