Roadmap to a Machine Learning Career for Web Developers
Lately, many web developers have shifted to Machine Learning, considering its growing demand and lucrative career prospects. However, a career shift in Machine Learning can be challenging for developers as they need to acquire several new skills from scratch, such as programming languages, algorithms, mathematics concepts, etc. In this blog, we are providing a path for building a Machine Learning career. If you’re also thinking of stepping into Machine Learning coding as a web developer, we got you covered.
What does a Machine Learning Engineer Do?
A Machine Learning Engineer is a programmer who designs and develops self-running systems that can make smart business decisions based on fed data with little or no human intervention. As a Machine Learning Engineer, your responsibility is beyond programming machines to perform a particular task. Instead, you need to create smart programs that allow machines to take actions automatically without explicit programming.
Being an interdisciplinary field, Machine Learning demands a solid understanding of both data structures and data models to deploy those models in software. Thus, Machine Learning Engineers must possess competency in Data Science and software engineering.
Top skills needed to start a Machine Learning Career
Web Developers with a keen interest in Machine Learning coding can upscale their careers by learning different skill sets.
Technical skills:
- CS Fundamentals & Programming
- Natural Language Processing (NLP)
- ML Algorithms and Libraries
- Mathematics and Statistics
- Reinforcement Learning
- Artificial Intelligence
- Data Manipulation
- Model Deployment
- Signal Processing
- Deep Leaning
- Data Analysis
- Tableau
Soft skills:
- Problem-Solving
- Good Communication
- Critical Thinking
- Time Management
- Teamwork
- Decision Making
- Leadership
- Work Ethic
Learning such a large variety of skills can be a daunting task for web developers. Thus, it is prudent to take training from the Machine Learning Coding Bootcamp to gain expertise.
Different Career Options in Machine Learning
Once you acquire these Machine Learning coding skills, you can explore many rewarding job opportunities, such as:
- Machine Learning Engineers
- Human-Centered AI Designer
- Business Intelligence Developers
- Cybersecurity Analysts
- Research Scientists
- Robotics Engineers
- Product Designers
- AI Programmers
- Data Scientists
- NLP Scientists
Step by step guide to becoming a Machine Learning Engineer
Machine learning is a relatively new technology that keeps evolving faster. There are several ways to get into Machine Learning, depending on your areas of interest, technical skills, and educational background. Follow these steps to build a Machine Learning Engineering career:
- Learn the right coding languages: To build a solid foundation in Machine learning, you should first gain programming expertise. Learning popular languages like Python, Java, R, JavaScript can be a safe bet.
- Start big data analysis: As a Machine Learning Engineer, you need to be skilled in analyzing, gathering, and processing large sets of structured and unstructured data. Two effective tools for this are Spark and Hadoop. Learn to utilize relational databases such as MySQL, SQL, and SQLite.
- Understand the Machine Learning workflow: Acquaint yourself with the Machine Learning workflow that includes processing, importation, evaluation, modeling, and visualization. It will give you a sheer idea of how and where to apply Machine Learning algorithms.
- Enroll in a Machine Learning Coding Bootcamp: It would be difficult to break into the Machine Learning industry without completing professional-led training. So, look for a trusted camp that provides immersive ML training to gain a comprehensive understanding of Machine Learning in a short period.
- Start job hunting: Now that you already have the skills to be a Machine Learning professional, you can start applying to jobs. However, you need to convince the employers of your worth. Attending a certification training program can boost your professional value and help you stand out in the competition.
How enrolling in a Coding Bootcamp can fast-track your ML Career?
Signing up with the best coding bootcamp can reward you in many ways:
- Industry-relevant curriculum: A Machine Learning Bootcamp prepares candidates according to industry standards, so you will learn in-demand Machine Learning skills that employers seek.
- Hands-on experience: Learning ML in professional settings will expose you to the real-work challenges that most Machine Learning practitioners often face. Thus, it prepares you for the future and makes you career-ready.
- Career coaching and placement: When you join a trusted Machine Learning Coding Bootcamp like SynergisticIT, you will get job assistance and guaranteed placement. Their placement team prepares candidates for tech job interviews through regular mock tests, psychometric tests, and soft skills training. They also market their candidate’s skills to top tech giants like Google, Microsoft, Apple, Cisco, IBM, etc.
Get your feet off the ground and move into the blooming Machine Learning industry with the help of SynergisticIT, the best coding bootcamp. Let’s accompany you in your professional endeavors.
Roadmap to a Machine Learning Career for Web Developers
Lately, many web developers have shifted to Machine Learning, considering its growing demand and lucrative career prospects. However, a career shift in Machine Learning can be challenging for developers as they need to acquire several new skills from scratch, such as programming languages, algorithms, mathematics concepts, etc. In this blog, we are providing a path for building a Machine Learning career. If you’re also thinking of stepping into Machine Learning coding as a web developer, we got you covered.
What does a Machine Learning Engineer Do?
A Machine Learning Engineer is a programmer who designs and develops self-running systems that can make smart business decisions based on fed data with little or no human intervention. As a Machine Learning Engineer, your responsibility is beyond programming machines to perform a particular task. Instead, you need to create smart programs that allow machines to take actions automatically without explicit programming.
Being an interdisciplinary field, Machine Learning demands a solid understanding of both data structures and data models to deploy those models in software. Thus, Machine Learning Engineers must possess competency in Data Science and software engineering.
Top skills needed to start a Machine Learning Career
Web Developers with a keen interest in Machine Learning coding can upscale their careers by learning different skill sets.
Technical skills:
- CS Fundamentals & Programming
- Natural Language Processing (NLP)
- ML Algorithms and Libraries
- Mathematics and Statistics
- Reinforcement Learning
- Artificial Intelligence
- Data Manipulation
- Model Deployment
- Signal Processing
- Deep Leaning
- Data Analysis
- Tableau
Soft skills:
- Problem-Solving
- Good Communication
- Critical Thinking
- Time Management
- Teamwork
- Decision Making
- Leadership
- Work Ethic
Learning such a large variety of skills can be a daunting task for web developers. Thus, it is prudent to take training from the Machine Learning Coding Bootcamp to gain expertise.
Different Career Options in Machine Learning
Once you acquire these Machine Learning coding skills, you can explore many rewarding job opportunities, such as:
- Machine Learning Engineers
- Human-Centered AI Designer
- Business Intelligence Developers
- Cybersecurity Analysts
- Research Scientists
- Robotics Engineers
- Product Designers
- AI Programmers
- Data Scientists
- NLP Scientists
Step by step guide to becoming a Machine Learning Engineer
Machine learning is a relatively new technology that keeps evolving faster. There are several ways to get into Machine Learning, depending on your areas of interest, technical skills, and educational background. Follow these steps to build a Machine Learning Engineering career:
- Learn the right coding languages: To build a solid foundation in Machine learning, you should first gain programming expertise. Learning popular languages like Python, Java, R, JavaScript can be a safe bet.
- Start big data analysis: As a Machine Learning Engineer, you need to be skilled in analyzing, gathering, and processing large sets of structured and unstructured data. Two effective tools for this are Spark and Hadoop. Learn to utilize relational databases such as MySQL, SQL, and SQLite.
- Understand the Machine Learning workflow: Acquaint yourself with the Machine Learning workflow that includes processing, importation, evaluation, modeling, and visualization. It will give you a sheer idea of how and where to apply Machine Learning algorithms.
- Enroll in a Machine Learning Coding Bootcamp: It would be difficult to break into the Machine Learning industry without completing professional-led training. So, look for a trusted camp that provides immersive ML training to gain a comprehensive understanding of Machine Learning in a short period.
- Start job hunting: Now that you already have the skills to be a Machine Learning professional, you can start applying to jobs. However, you need to convince the employers of your worth. Attending a certification training program can boost your professional value and help you stand out in the competition.
How enrolling in a Coding Bootcamp can fast-track your ML Career?
Signing up with the best coding bootcamp can reward you in many ways:
- Industry-relevant curriculum: A Machine Learning Bootcamp prepares candidates according to industry standards, so you will learn in-demand Machine Learning skills that employers seek.
- Hands-on experience: Learning ML in professional settings will expose you to the real-work challenges that most Machine Learning practitioners often face. Thus, it prepares you for the future and makes you career-ready.
- Career coaching and placement: When you join a trusted Machine Learning Coding Bootcamp like SynergisticIT, you will get job assistance and guaranteed placement. Their placement team prepares candidates for tech job interviews through regular mock tests, psychometric tests, and soft skills training. They also market their candidate’s skills to top tech giants like Google, Microsoft, Apple, Cisco, IBM, etc.
Get your feet off the ground and move into the blooming Machine Learning industry with the help of SynergisticIT, the best coding bootcamp. Let’s accompany you in your professional endeavors.
Machine Learning Algorithms and Applications for Data Scientists
Data scientists are professionals with expertise in different interdisciplinary skills like Machine Learning, data mining, and statistics. Data Science professionals need to learn the application of multiple ML algorithms to solve various types of problems as only one algorithm may not be the best option for all issues. You can join a Machine Learning Bootcamp to gain competency in using frequently applied Machine Learning algorithms.
Top Machine Learning Algorithms in Data Science
Below are the most important ML algorithms that Data Scientists can learn in Machine Learning Training:
- Linear Regression: Regression analysis is a method of evaluating and determining the relationship between dependent variables and data sets. It tackles the regression problems, while logistic regression tackles the classification problems. Linear regression is an old and most popularly used ML algorithm that Data Science professionals often use.
- Decision Tree: As its name suggests, a decision tree refers to the arrangement of data in the form of a tree structure. Data gets separated at every node into different branches of the tree structure. The data separation happens according to the attributes’ values at the nodes.
- Logistics Regression: Logistic regression implies a statistical process for building ML models where the dependent variable is binary. Data Scientists leverage Logistics Regression to describe data and the relation existing amongst a dependent variable and independent variables.
- Naïve Bayes: It is a set of supervised learning algorithms based on the Bayes Theorem used in various classification problems. Naïve Bayes models are best suited for high-dimensional datasets.
- K-Means: K-Means is an unsupervised learning algorithm that resolves clustering problems. In this method, data sets are classified into clusters in a way that all the data points within a cluster are heterogeneous and homogenous from the data in the other clusters.
- SVM Algorithm: The SVM algorithm is a classification algorithm wherein you plot raw data as points in the n-dimensional space. Each feature’s value is tied to a particular coordinate that simplifies data classification. Lines called classifiers are used to split the data and plot them on the graph.
- KNN Algorithm: This algorithm can be applied to both regression and classification problems. It is a widely used algorithm in the Data Science industry. KNN Algorithm stores all available cases and splits the new ones based on its k neighbours’ majority vote.
Machine Learning training has a well-defined and structured curriculum that imparts knowledge of all these sought-after ML algorithms. You will learn to apply these algorithms while working on case studies and capstone projects under the assistance of Data Science and Machine Learning professionals.
Join SynergisticIT, the best coding bootcamp to become proficient in using Machine Learning algorithms required to start a Data Science career. They offer an immersive Machine Learning Bootcamp training centered around the core and advanced ML concepts, including Decision Tree, Linear Regression, Random Forest, Logistics Regression, Naïve Bayes, NLP, Deep Learning, data analysis, model deployment, tableau, data visualization, etc. So, kickstart your career today.
Also, Read This Blog: What is Machine Learning?
A Complete Guide To Machine Learning Bootcamp
Machine Learning is an AI-based advanced technology that enables computers and machines to learn, analyze, and interpret data to make accurate decisions with little or no human intervention. Reportedly, the annual growth rate of Machine Learning is expected to be 42.08% CAGR by 2024, creating thousands of new jobs. Most industries working with large amounts of structured and unstructured data have recognized the power of Machine Learning. For this reason, many businesses hire skilled Machine Learning engineers to operate efficiently by culling valuable insights from data. Thus, enrolling in a Machine Learning Coding Bootcamp can be considered a safe bet.
Why should you pursue Machine Learning?
Before you start hunting for the best coding bootcamp to gain Machine Learning expertise, check the reasons to learn Machine Learning:
- Better Career Prospects: With several industries applying ML algorithms like education, logistics, healthcare, e-commerce, studying Machine Learning coding has become a lucrative career path. Machine Learning opens the door to rewarding career opportunities for Machine Learning engineers, data scientists, cloud architects, data mining professionals, etc.
- Higher Paychecks: Being skilled in the most in-demand tech skill can increase your potential income. Machine Learning engineers get an average salary ranging from $75,000 to $180,000 per annum, more than other tech workers. Thus, you can secure remunerative offers if you get upskilled in a Machine Learning Coding Bootcamp.
- Jobs are peaking: A quick search for Machine Learning jobs on LinkedIn results in 170,000+ vacancies in the US. These numbers keep increasing with each passing minute, resulting in the shortage of qualified candidates in the Machine Learning industry. So, break into the Machine Learning industry before it gets too competitive.
- Boosts creativity: Unlike other mundane IT jobs, Machine Learning challenges you to think creatively. It requires excellent critical thinking abilities and strong programming knowledge to build intelligent machines that make independent decisions.
- Big companies are using Machine Learning: Many renowned companies such as Pinterest, Twitter, Yelp, Facebook, Google, IBM, Accenture harness Machine Learning solutions. Leverage the chance of working in a Fortune 500 Company by taking training in a coding bootcamp.
What will you learn in a Machine Learning Coding Bootcamp?
A coding bootcamp lets you immerse in the core Machine Learning concepts in the right. A Machine Learning coding program is a perfect blend of academic and industrial exposure that equips candidates with futuristic ML skills. It provides a structured curriculum designed by AI and ML professionals with years of experience and profound subject knowledge. The bootcamp often starts with the beginner-level topics, then dives into the advanced Machine Learning topics such as Data Manipulation, Model Deployment, NLP, Data Analysis, KNN Algorithm, Logistics Regression, Decision Tree, Deep Learning, Strings, Python, Tableau, etc. Basically, a bootcamp imparts all the essential skills required to commence a Machine Learning career.
Career outlook in Machine Learning
Once you acquire Machine Learning competency, you can explore many lucrative career options such as:
- Machine Learning Engineer
- Human-Centered AI Designer
- Business Intelligence Developer
- Robotics Engineer
- Data Scientist
- Algorithm Engineer
- NLP Scientist
- AI Programmer
- Cybersecurity Analyst
- Data Analyst
Prerequisites to join Machine Learning Bootcamp
The best coding bootcamp like SynergisticIT offers Machine Learning training from scratch, so you can sign up for the training even if you are a fresher with no technical background or experience,. However, one should get familiarized with the following concepts for a better learning experience:
- Data structures, linear equations, algorithms, histograms, variables, calculus, statistics
- Programming languages like Python, Java, and others
Who can attend Machine Learning Training?
Anyone who wants to advance their careers in Artificial Intelligence and Machine Learning can join a Machine Learning Bootcamp. An ML training is best suited for:
- Freshers
- Graduates/undergraduates
- Programming enthusiasts
- Information architects
- Business analysts
- Software engineers
- Analytics manager
- Developers
- Other tech-savvies
Key benefits of enrolling in a Machine Learning Coding Bootcamp
- A professional-led ML training enables you to connect with the leading Machine Learning practitioners and accomplished professionals.
- As Machine Learning training engages candidates in practical hands-on exercises, they become proficient in applying ML algorithms on real-world case studies and capstone projects.
- When you take training from the best coding bootcamp, you get rewarded with an industry-recognized certificate that keeps you ahead in the competition.
- Machine Learning training imparts you with the latest industry trends and best development practices.
- You develop a deep understanding of the challenges posed by ML and AI in the workplace.
- Machine Learning Bootcamps also provide career assistance and job placement services. Thus, you get prepared for technical interviews.
If you also want to make a mark in this thriving industry, consider joining the best Machine Learning Coding Bootcamp, SynergisticIT. We have a top-notch ML faculty that accompanies candidates at each step of their Machine Learning career endeavors. Our team has assisted many tech aspirants in gaining some next-level Machine Learning skills and deploying those skills to build a career. We can help you to unleash your maximum potential. So, join us today.
Also, Read This Blog: What’s the Difference Between Artificial Intelligence, Machine Learning and Deep Learning?
A Guide To The Types Of Machine Learning Algorithms And Their Applications
The term machine learning is the majority of times used incorrectly with Artificial Intelligence. However, machine learning is actually a sub type of Artificial Intelligence. It is majority of times referred as predictive analytics or modelling. First coined by Arthur Samuel in 1959, the term ‘machine learning is now defined as a “computer’s ability to learn without having to be programmed”.
At the most basic level, machine learning uses programmed algorithms that can receive and analyse input data for predicting output values within an acceptable range. As new data is fed to algorithms, they can learn and optimize their operations to improve performance and enhance intelligence over time. Numerous machine learning bootcamp and platforms can offer you guidance on selecting the right machine learning for your projects.
There are four mtypes of machine learning algorithms: supervised, semi-supervised, unsupervised, and reinforcement.
Supervised learning-Supervised learning makes use of example to teach machine. The operator offers machine learning algorithms with a known dataset which includes inputs and outputs. Then the algorithms must find a method for determining how to arrive at these inputs and outputs.
Although these operators know the correct answers to the problem, the algorithm identifies data patterns, makes predictions, and learns from observations. Under supervised learning, one has a classification, regression and forecasting. Each of these are responsible for drawing a conclusion from observed value, estimating and understanding the relation between variables, and make predictions and analyzing trends, respectively.
Semi-supervised learning- It is similar to supervised learning; however, it uses both labelled and unlabelled data. Labeled data is largely information with relevant tags so that the algorithm can understand data, whilst unlabelled data lacks that information.
Unsupervised learning: A machine learning algorithm in unsupervised learning uses data to identify patterns. There is no human operator or answer key; therefore machine determines correlations and relationship by analysing available data. The algorithm is left to decipher and decode large data sets and address the data accordingly. As it can assess more data, its ability to make decisions on data gradually improves and becomes refined.
Under unsupervised learning there are few areas such as:
Clustering- Clustering includes grouping of data sets depend on defined criteria. It is beneficial for segmenting data into several groups and analyzing each data set to find patterns.
Reinforcement learning emphasises on regimented learning processes, where the ML algorithm is offered with parameters, set of actions, and end values. By defining rules, the machine learning algorithm tries to explore several options and possibilities, evaluate, and monitor. It teaches the machine trial and error, learns from previous experiences, and begins to adapt its approach to the situation to get the best possible result.
Machine Learning to Use
Selecting the right machine algorithm depends on numerous factors that include data size, diversity, and quality also answers businesses that want to derive from data. Additional one must consider including training time, parameters, data points, accuracy, and a lot more. More importantly, it is wise to keep every aspect in mind while also looking at machine learning bootcamp that can offer you training as per your future career requirement.
Even for the most seasoned data scientists, one cannot tell which algorithm performs best before the experiment. Hence, choosing the right algorithm combines business requirements, specifications of task, experimentation opportunities and the time in hand. So, if you are just starting in the field it is best to go through the leading Machine Learning Coding Bootcamp.
The most basic and popular machine learning algorithms is Naïve Bayes Classifier Algorithm (Supervised Learning – Classification). It classifies every value as independent of others and allows for predicting a class/category based on several sets of features using probability.
So, in a nutshell, there are several aspects to consider when selecting the right machine learning algorithm based on one’s business. However, one does need to get the right machine learning training before being able to deliver problem-based solutions.
Source:
A Guide To The Types Of Machine Learning Algorithms And Their Applicationsbestmachinelearningca.wordpress.com
A Guide To The Types Of Machine Learning Algorithms And Their Applications
The term machine learning is the majority of times used incorrectly with Artificial Intelligence. However, machine learning is actually a sub type of Artificial Intelligence. It is majority of times referred as predictive analytics or modelling. First coined by Arthur Samuel in 1959, the term ‘machine learning is now defined as a “computer’s ability to learn without having to be programmed”.
At the most basic level, machine learning uses programmed algorithms that can receive and analyse input data for predicting output values within an acceptable range. As new data is fed to algorithms, they can learn and optimize their operations to improve performance and enhance intelligence over time. Numerous machine learning bootcamp and platforms can offer you guidance on selecting the right machine learning for your projects.
There are four mtypes of machine learning algorithms: supervised, semi-supervised, unsupervised, and reinforcement.
Supervised learning-Supervised learning makes use of example to teach machine. The operator offers machine learning algorithms with a known dataset which includes inputs and outputs. Then the algorithms must find a method for determining how to arrive at these inputs and outputs.
Although these operators know the correct answers to the problem, the algorithm identifies data patterns, makes predictions, and learns from observations. Under supervised learning, one has a classification, regression and forecasting. Each of these are responsible for drawing a conclusion from observed value, estimating and understanding the relation between variables, and make predictions and analyzing trends, respectively.
Semi-supervised learning- It is similar to supervised learning; however, it uses both labelled and unlabelled data. Labeled data is largely information with relevant tags so that the algorithm can understand data, whilst unlabelled data lacks that information.
Unsupervised learning: A machine learning algorithm in unsupervised learning uses data to identify patterns. There is no human operator or answer key; therefore machine determines correlations and relationship by analysing available data. The algorithm is left to decipher and decode large data sets and address the data accordingly. As it can assess more data, its ability to make decisions on data gradually improves and becomes refined.
Under unsupervised learning there are few areas such as:
Clustering- Clustering includes grouping of data sets depend on defined criteria. It is beneficial for segmenting data into several groups and analyzing each data set to find patterns.
Reinforcement learning emphasises on regimented learning processes, where the ML algorithm is offered with parameters, set of actions, and end values. By defining rules, the machine learning algorithm tries to explore several options and possibilities, evaluate, and monitor. It teaches the machine trial and error, learns from previous experiences, and begins to adapt its approach to the situation to get the best possible result.
Machine Learning to Use
Selecting the right machine algorithm depends on numerous factors that include data size, diversity, and quality also answers businesses that want to derive from data. Additional one must consider including training time, parameters, data points, accuracy, and a lot more. More importantly, it is wise to keep every aspect in mind while also looking at machine learning bootcamp that can offer you training as per your future career requirement.
Even for the most seasoned data scientists, one cannot tell which algorithm performs best before the experiment. Hence, choosing the right algorithm combines business requirements, specifications of task, experimentation opportunities and the time in hand. So, if you are just starting in the field it is best to go through the leading Machine Learning Coding Bootcamp.
The most basic and popular machine learning algorithms is Naïve Bayes Classifier Algorithm (Supervised Learning – Classification). It classifies every value as independent of others and allows for predicting a class/category based on several sets of features using probability.
So, in a nutshell, there are several aspects to consider when selecting the right machine learning algorithm based on one’s business. However, one does need to get the right machine learning training before being able to deliver problem-based solutions.
Source:
A Guide To The Types Of Machine Learning Algorithms And Their Applicationsbestmachinelearningca.wordpress.com
How To Tackle Air Quality Prediction Using Machine Learning?
Air pollution is a major concern in current times. According to WHO data, about 7 million people are at risk due to bad air quality. It is the cause of many diseases from asthma to skin infection, lung cancer to throat and eye problems. Air pollution is a threat to humankind and destructive to the environment. Harmful emissions from vehicles and industry are the prime cause for the greenhouse effect; amongst all, CO2 is the most significant contributor.
Machine Learning For Predicting Air Quality
Previously, statistical methods have been used to make estimations about air pollution. Due to the complexity and variation in data, these methods are not very helpful. However, over the last few decades, efforts have been being taken to address the issue of air quality through machine learning techniques.
Machine learning methods, such as adaptive boosting (AdaBoost), stacking ensemble, random forest, artificial neural network (ANN), stacking ensemble, and support vector machine (SVM), offer promising results for predicting air quality levels. Machine learning training in California emphasizes teaching the most useful machine learning techniques.
Challenges For Predicting Air Quality
Predicting air quality is a pretty challenging problem as the air quality of one area differs from another. In fact, even in one city, air quality can vary. For example, quiet residential areas will have different air quality than busy streets and industrial regions. Besides, several factors affect the number of pollutants in the air, such as temperature, rain, air pressure, etc. But the collected data offer numerous possibilities beyond studying the air quality. The predictive models are useful to take preventive actions for improving air quality.
Architecture Of Predicting Air Quality
The best machine learning training in California help learn the techniques to monitor the air quality. There are four layers in an air quality prediction architecture, and each layer will have a different function.
Data Gathering
In this layer, data will be collected from different heterogeneous devices linked in a smart city. The sensors deployed at various locations in the city will collect data about different pollutants such as nitrogen dioxide, Sulphur dioxide, particulate, etc. Once the data is collected, filtration and pre-processing will take place to remove the unnecessary information.
Communication
In this layer, the transferring of data to the other layers will take place. It comprises different communication technologies like 3G, 4G, Wi-Fi, LTE, etc. All the data transfer from the IoT devices to the data processing layer happens here. This layer is also useful for gateways that are capable of real-time processing. Using Fog computing can increase the latency rate.
Data Management / Storage
This layer is primarily responsible for the storage and analysis of data. As real-time processing is needed for the analysis, different third-party tools such as Spark, VoltDb, Storm can also be used here. The data is stored in the HDFS system, while other systems can be used for data query and analysis. Here, both in-memory and offline data analysis takes place. It is helpful in learning through different ML algorithms. Predictions and pattern findings also happen here.
Application
This layer is the interface for all the information as it is connected with real-time devices. Hence, the data and reports are transferred in the form of charts, dashboards to display. Mostly, the end-user of this information is the government agencies responsible for monitoring air quality. They use this data to make crucial decisions. It displays all the pollution-related information. By interacting with the pollution statistics, people can make decisions.
If you are keen to make a career in machine learning, choose SynergisticIT, the best online machine learning training in California. It will help you gain expertise in machine learning tools, techniques, and libraries to tackle real-life problems.
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