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Tag: career in Machine Learning

Career advice for machine learning engineers: From an industry expert

Posted on by Imarticus Learning

If you’re currently a machine learning engineer or aspire to be one, then you know how necessary it is to advance on the latest industry trends. That’s why we’re thrilled to bring you this career advice from an expert in the field! 

This post will teach you about what a Certificate in Data Science and Machine Learning can do for your career advancement. You will also hear about the different opportunities available for machine learning engineers and get some tips on how to make yourself stand out in today’s competitive job market.

We hope this post will provide you with valuable insight that can help you make the most of your career as a machine learning engineer. So grab a cup of coffee, and let’s get started! 

How Can Certificate Programs in Data Science and Machine Learning Help Boost Your Career?

Certificate programs in data science and machine learning can open up a wide range of opportunities for professional advancement. By completing a program, you will get the chance to gain specialized knowledge and skills that will let you to stand out from other job applicants. Certificate programs also provide an opportunity to network with other experts in the field, which can be essential for career advancement.

What Are the Opportunities for Machine Learning Engineers?

 

As a machine learning engineer, there are a variety of opportunities available to you. You could pursue further education or specialize in one specific area, such as artificial intelligence (AI) and deep learning. Additionally, you could take on roles related to data engineering, software development, analytics, visualization, or modeling.

Tips from Industry Experts on How to Stand Out as a Machine Learning Engineer

In today’s competitive job market, it is significant for machine learning engineers to stand out from other applicants. Here are a few tips from industry experts on how to do this:

  • Utilize Certificate Programs: Certificate programs in data science and machine learning can give you a competitive edge.
  • Dont miss practical experience: It’s important to have a good theoretical foundation, but don’t forget to get hands-on experience.
  • Build Your Technical Skillset: Being a successful machine learning engineer requires staying current with technological trends and honing your technical abilities.
  • Network with Other Professionals in Your Field: Connecting with other professionals in the field will help you gain more insight into the industry and can open up new opportunities.
  • Get Involved in the Community: Participating in hackathons, conferences, or other community events can help showcase your talent and skills to potential employers.

We hope this post has provided you with some useful insight that can help you advance your career as a machine learning engineer. Certificate programs in data science and machine learning can open up many doors for professional advancement, so don’t miss out on this valuable opportunity.

Learn Data Science and Machine Learning with Imarticus Learning.

At Imarticus Learning, we offer a machine learning certification course to ensure that professionals have the most up-to-date tools and techniques for data science. Our courses are designed with international standards in mind, so you can be confident in what you’re learning and how it will help your data scientist career.

 Course Benefits For Learners:

  • Learners will gain hands-on experience with the latest data science and machine learning techniques and tools.
  • This Certificate Program in Data Science and Machine Learning is taught by subject matter experts and provides students with the opportunity to learn machine learning algorithms.
  • Learners will develop the necessary skills required to apply for jobs in the industry and be successful.

 Visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad, or get in touch with us via chat support.

Building a solid career in machine learning: Skills, jobs and opportunities

Posted on by Imarticus Learning

Building a solid career in machine learning: Skills, jobs and opportunities

In recent years, machine learning has become one of the most sought-after fields in tech. As data becomes increasingly central to businesses and organizations, it’s used to make sense-identify invisible patterns in humans, and make decisions based on them.

With the growing popularity of machine learning, there has been an increase in demand for machine learning experts. So, If you’re interested in a career in machine learning, this post will explore some of the skills you need to develop, the different types of jobs available, and the opportunities in this field.

Skills you need to develop

The skills desired for a career in machine learning are constantly evolving, but a few key areas that all machine learning professionals should focus on include:

  • Statistical analysis: Machine learning is all about working with data, so a strong foundation in statistical analysis is essential.
  • Programming: To be able to build and work with complex machine learning models, you need to be an expert programmer.
  • Data mining: Finding and extracting the relevant data from large data sets is a crucial skill for any machine learning engineer.
  • Machine learning algorithms: A good understanding of different machine learning algorithms is necessary to design effective models.

Job prospects of Machine learning

It is the best time to enter the field of machine learning. Job prospects for machine learning professionals are excellent, with salaries rising and more and more companies deciding to include machine learning capabilities in their organizations.

Also, the demand for machine learning professionals is growing faster than the supply, meaning that there will be an array of opportunities for those with the right skills and abilities. So, make sure that you are well-prepared to enter this exciting field. To do so, you can consider taking a machine learning course.

Career Opportunities in the Machine Learning field

There are numerous career opportunities in the machine learning field. Some of these career paths include:

  • Data Scientist: Data scientists use machine learning algorithms to make predictions and recommendations from data. They may also develop new machine-learning algorithms.

  • Machine Learning Engineer: Machine learning engineers build and deploy machine learning models.
  • Research Scientist: Research scientists develop new machine-learning algorithms and applications. They may also conduct research to improve existing methods.

  • Machine learning architect: Machine learning architects are responsible for designing, building, and maintaining machine learning systems.

  • Artificial intelligence Engineer: Artificial intelligent engineers are responsible for implementing machine learning systems.

Explore a Machine learning course with Imarticus Learning.

This machine learning course with placement will prepare you for a career in the field. Once you’ve learned how to build complex models that generate crucial business forecasts and insights, put your new knowledge into practice.

Course Benefits for Students:

  • Students will learn data analytics—including various methodologies and tools, analytics, and machine learning basics.

  • The data analytics certification course will include 25 real-world projects, case studies, and hands-on experience with machine learning tools.

  • The ability to become a data analyst with data analytics basics and machine learning.

Visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad, or get in touch via the chat support system.

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https://imarticus.org/building-a-data-analytics-project-with-python-here-are-a-few-tips/

What’s The Quickest Way To Learn Math For Machine Learning And Deep Learning?

Posted on by Imarticus Learning

In modern times we have everything from developments like smartphones, robots, driver-less cars, medical instruments like CAT scans and MRI machines, smart traffic lights, and a host of animated games. Even payments have gone digital and cashless! And all this has emerged over the last decade due to AI, ML, and data analytics.

The future holds great promise for development in these fields and to make a high-paid scope-filled career in any of these fields, mathematics is the key ingredient that you must learn if you want to learn machine learning. ML runs on algorithms and the algorithm is dependent on knowledge of mathematics and coding.

Why mathematics is so important in ML:

Some of the many reasons are :

  1. Selecting the apt algorithm with a mix of parameters including accuracy, model complexity, training time, number of features, number of parameters, and such.
  2. Selecting the validation of strategies and parameter-settings.
  3. Using the tradeoff of Bias-Variance in identifying under or overfitting.
  4. Estimating uncertainty and confidence intervals.

The math components required for ML:

ML algorithms require proficiency in the three topics of Linear Algebra, Probability Theory, and Multivariate Calculus.

Let us discuss the topics you need to learn machine learning under each of these heads.

A. Linear Algebra:

The use of Linear algebra notation in ML helps describe the structure of the ML algorithm and the parameters it depends on. Thus linear algebra is important in the interconnection of neural networks and their operations.

The topics that are important are : 

  • Vectors, Tensors, Scalars, Matrices,
  • Special Vectors and Matrices
  • Norms of Matrices
  • Eigenvalues and vectors

B. Multivariate Calculus:

ML learns from its experience with the data set and to supplement this we need calculus to power learning from examples, improving performance, and updating parameters of the different models.

The important topics here are : 

  • Integrals
  • Derivatives
  • Differential Operators
  • Gradients
  • Convex-Optimization

Probability Theory:

The assumptions about data use this theory to design the AI and its deep learning capabilities. The key probability distributions are crucial to algorithms.

Study these topics well.

  • Random Variables
  • Elements of Probability
  • Distributions
  • Special Random Variables
  • Variance and Expectation

Can you learn Math for ML quickly?

To learn machine learning it is not required to be an expert. Rather understand the concepts and applications of the math to ML. Doing things like math is time-consuming and laborious.

While there may be any number of resources online, Mathematics is best learned by solving problems and doing! You must undertake homework, assignments, and regular tests of your knowledge. One way of getting there quickly and easily is to do a learn machine learning course with a bootcamp for mathematics at Imarticus Learning

This will ensure the smooth transition of math and ML applications in a reputed institute for ML where they do conduct bootcamps. At the end of this course, you can build your algorithms and experiment with them in your projects. But, the main question that remains is why do a learn Machine Learning Course at Imarticus in the first place?

The Imarticus Learning course scores because: 

  • They have sufficient assignments, tests, hands-on practice, and bootcamps to help you revise and learn machine learning.
  • They use certified instructors and mentors drawn from the industry.
  • They integrate resume writing, personality development, mock interviews, and soft-skill development modules in the course.
  • They have convenient modes and timings to learn at your own pace for professionals and classroom mode for freshers and career aspirants.

Conclusion:

Mathematics is all about practice and more practice. However, it is crucial in today’s modern world where AI, ML, VR, AR, and CS rule. These sectors are where most career aspirants are seeking to make their careers, because of the ever-increasing demand for professionals and the fact that with an increase in data and the development of these core sectors, there are plentiful opportunities to land the well-paid jobs.

At the Imarticus, you can consider the Machine Learning course, you will find a variety of courses on offer for both the newbie and tech-geek wanting to go ahead in his/her career. Start today if you want to do a course in AI, ML, or Data Analytics. For more details in brief and further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Hyderabad, Delhi, and Gurgaon.

A Complete Guide For Deep Learning!

Posted on by Imarticus Learning

Deep Learning is known as neurally organized or as learning of various levels. It is one piece of an even more extensive type of group of the techniques used for machine learning in the aspect of learning and retrieving information, instead of undertaking the particular calculations. Also, learning could be directed, or semi-managed or even unsupervised.

Hence, careers in the field of Deep Learning renders organizations with different kinds of arrangements for systems in order to look after the issues of complex explanatory and also drives rapid developments in the counterfeit consciousness.

Complex undertakings such as- picture examination and discourse, can be performed with the help of models prepared by fostering calculations of deep learning amidst an immense amount of information.

These models of Deep learning are generally identified with the data preparing as well as with correspondence designs that are in a system of organic sensory, for example, the neural coding that attempts to characterize one connection between distinct data and the related reactions of neurons inside the brain. Therefore, a career in deep learning looks prospering.

What are the job positions that one can expect in the field of Deep Learning?

Mentioned below are the job positions that a person who specializes in deep learning can look out for:

  1. Research Analyst
  2. Data Scientist
  3. Neuroinformatics
  4. Image Recognition
  5. Research Scientist
  6. Deep Learning Instructor
  7. Full-stack deep learning web developer
  8. Process Engineer for Natural Language
  9. Software Engineer
  10. Data Analyst
  11. Data Engineer
  12. Bioinformatician
  13. Software Developer
  14. Research Fellow
  15. Applied Scientist
  16. A lead manager in Deep Learning

This shows that a career in Deep Learning has lots of options to make a future in.

Career Outlook

The information researcher hunts through enormous measures of unstructured as well as organized information in order to give fractions of knowledge; plus, it also helps to meet the particular business requirements or needs and objectives. Similar work needs to be done if you have pursued the Machine-Learning courses.

From where should you pursue the deep learning course?

Imarticus Learning is one of the best platforms to learn and help yourself make a future in the field of Deep Learning. Here, you will get to learn all the skills that are essential to becoming an expert in the field of Deep Learning. Because there are a number of skills and academic study required, Imarticus offers a ‘Machine Learning & Deep Learning Prodegree’, in association with the edtech partner, IBM.

It is the first-of-its-kind certification course of more than 145+ hours of training. This provides in-depth data science exposure, as well as, big data, machine, and deep learning as well. The meticulous curriculum-aligned as per the industry provides a comprehensive knowledge of Python as well as data science for a flourishing future and career in machine learning and big data as well. This program also stars seven projects of industry, various case studies as well as periodic interaction with industry leaders inside the ecosystem of machine learning.

Ways To Use Artificial Intelligence In Education

Posted on by Imarticus Learning

Ways To Use Artificial Intelligence In Education

Do you know that AI is very present in all our lives and has pervaded almost every space? Not just the imaginary humans with chips portrayed by fiction writers and science fiction movie makers but just look around.

Google searches, automatic sensors for reversing your car, automatic lens adjustments and light settings for those perfectly timed selfies, Google maps to take you straight to your destination, MRIs to detect those illnesses you never thought you had, multiple-choice answer sheets scored automatically on online learning sites, paying bills online, that favorite app you just downloaded and everything I between. They all run on the artificial intelligence courses of the self-learning algorithms of machine learning help make machines truly aid to human thinking through deep learning and neural networks.

Though AI has actually taken over most of the human tasks, they are still a long way off from replacing human beings and the one area where they have tremendous application potential is in education. Let’s reiterate that the basic aim of artificial intelligence courses and neural thinking is not to replace humans but to help them with repetitive tasks and data sifting far beyond the limits of the best human brains. So, in the future, AI and humanoid robots will not replace teachers. But they will transform how we learn, and what to learn and go a step further by helping us learn. That includes the teachers too who are constantly learning too!

Why AI matters in education:

Let us explore how AI is going to bring its benefits to the education experience of the future. The class sizes keep increasing with compulsory education and teachers are often facing many challenges in giving attention and help to the large numbers of students. A big challenge like this has been simplified by incorporating computer programs that allow each student to follow his own pace and learning curve. Individualized learning modules can help find knowledge gaps and personalize the learning materials to fill in the gaps.

Teachers can now get truly involved in teaching and rectifying the lacunae in the learning process. Besides, the teachers can also get recommendations on how to rectify the issues, what learning materials to use for personalizing the process and much more to help herd the students towards the right levels of comprehension and skills required. This could also be used for learning processes of differently challenged students.

The newer methods of experiential learning at educational institutions use advanced techniques of AI, machine learning and deep learning in instructing and teaching like chatbots and learning bots. A differentiated AI style of learning deals with the most effective style to help the student learn. Adaptive artificial intelligence courses based learning curates the learning exercises matching them to the student’s needs and knowledge gaps. Competency-based AI tests aid the students to gauge their learning levels and progress from thereon. Using all these three types of learning AI can test how well the students can adapt their learning to applications of it and thus promote the progress of students based on individual interests.

The benefits:

Some of the benefits of artificial intelligence courses that can be harnessed are: 

1. Grading, scoring, and such repetitive tasks can easily be handled by AI.

2. Personalization of educational software can be need-based and adapted to individual learning curves.

3. Lacunae and learning gaps can be predicted and rectified with suggestions for learning materials and courses needed to improve.

4. Tutoring through subject-specific learning bots, online self-paced courses etc can support students.

5. The feedback route is almost instantaneous and can be gainfully harnessed by both educators and learners.

6. AI has changed the way we search for and interact with data. Just Google for information on anything and everything is what 95% of the people do to find information.

7. AI will make teachers more effective and ever-learning educators.

8. AI will develop human skills and make trial-testing-and-error learning the norm.

9. Data harnessing and empowerment will change the learning experience using AI to find, support and teach students.

10. AI can offer both offline and online resources which will alter where we learn, how and who teaches them and help apply to learn to basic implicational skills.

Conclusions: 

What do you think would be the results of AI in education and the learning process? Yes, the education field is going to be very different from what we now see it as. Skills in learning applications will count for more. Jobs will be linked to skills and not degrees. Certification will emerge as a measurable tool of skills. And, if you want to explore more, why not do artificial intelligence courses at the reputed Imarticus Learning institute?

10 Interesting Facts About Artificial Intelligence!

Posted on by Imarticus Learning

Artificial Intelligence has received a lot of focus and attention in the last couple of years. There has been a boom in the innovations that have artificial intelligence at its base. Obviously, the internet has played a crucial role in the development of artificial intelligence-enabled services.

Machine learning essentially an artificial intelligence technique, has been stirring new developments by creating new algorithms that mimic or support human behavior or decision-making capabilities, which are already in use, like Apple’s Siri, or the email servers which eliminate junk or spam emails. You can also see the use of machine learning in e-commerce websites that use it to personalize the search or use of the web experience of their customers.

It is interesting to comprehend the capabilities of machines. Very soon machines will have the capability to perform advanced cognitive functions, processing language, human emotions, the machines will be proficient in learning, planning, or performing a task as intelligent systems.

There is also a definite possibility that the tasks performed will be or can be more accurate than humans, thus artificial intelligence can boost productivity and accuracy, and impact economic growth. Imagine the impact it can have on medical procedures, the continued support it could lend to the disabled, increasing their life expectancy.

Artificial intelligence is a technology that can improve the world for the better, however, it also comes along with some challenges such as machine accountability, security, displacement of human workers, etc.


But right now before the possible alarming impact of artificial intelligence, we could in the today, the now, enjoy learning about some interesting facts.

 

Interesting Facts About Artificial Intelligence

  1. It is interesting to note that research on artificial intelligence is not only a few years ago, but the inception of AI also goes back to the 1950s. Alan Turning is coined as the father of AI, back in the day he invested a test based on natural language conversation with a machine.
  2. Did you know that a lot of video games that engage humans over time are based on a technique of artificial intelligence and is called Expert System? This technique is knowledge-based and can imitate areas of intelligent behavior, with a goal to mimic the human ability of senses, perception, and reasoning.
  3. Autonomous vehicles are no longer a thing of the far future. The knight rider might actually become a reality in as close as the next 2-3years or less. These cars are based on artificial intelligence to recognize the driving conditions and adapt the behavior. These cars are in the test phase, already developed and almost ready to hit the road.
  4. There is a race that is warming up between social media corporations over perfecting the use of artificial intelligence to enhance the customer experience. Facebook and Twitter are two companies essentially applying AI to match relevant content to the people. Leading this race is Google, coming across as one of the most preferred and reliable search engines.
  5. IBM has created a supercomputer based on AI, called Watson. One of the major challenges of creating Watson was the programming that needed to be done so that it could understand questions in most of the common languages and the ability to attend to those questions in real-time. The development is such that currently Watson is not only applied in various industries but was recently successful in teaching people how to cook.
  6. Sony created a robotic dog called Aibo, one of its first toys that could be bought and played with. It could express emotions and could also recognize its owner. This was the first of its kind, however, today you will find more expensive and evolved versions of the same.
  7. At the rate at which Artificial Intelligence is being adopted in various areas of our lives, it is predicted that it will replace 16% of our jobs over the next decade.
  1. Artificial Intelligence Training CoursesIt is a fact that with increased intelligence and ability to perform tasks with accuracy, over the next few years it is predicted that close to three million workers will be reporting to or will be supervised by “Robot-bosses”.

    With Machine learning and language recognition, it is no surprise that 85% of telephonic customer service jobs will be performed by computers and will not need human interaction.  By the dawn of 2020, it will be possible for all customer digital assistant to recognize people by face and voice.

Organizations and private sectors have recognized the opportunity that AI investments can have on the future of their businesses. Hence have set up major investments in the development of the same.

Finally, one must remember the anticipated impact of AI is on calculated assumptions and predictions.
However, one thing is clear, that AI in the future will impact the internet, its citizens, and economies.

Read More 
The Promises of Artificial Intelligence: Introduction

What Are Prerequisites to Start Learning Machine Learning?

Posted on by Imarticus Learning

What Are Prerequisites to Start Learning Machine Learning?

There are few fields in technology which have risen as much as machine learning and data science have, in the past few years. The demand for professionals well versed in data science has more than tripled, while the field is also now one of the most lucrative profession options for any interested person.

Machine learning does require the user to have a modicum of understanding over mathematical concepts. Apart from the requisite programming skills, you will need to know some basic mathematical concepts in order to understand how various algorithms function in the backdrop. Here are some of the main topics that you need to know before you get into machine learning.

• Basic Maths
 The importance of mathematics in machine learning cannot be overstated, but the extent to which it is used depends upon the project at hand, really. Entry-level users may not need to understand a lot, because you may only have to learn how to implement the algorithms well using the tools at hand.

However, you would not understand the deeper workings of algorithms or libraries without knowledge about linear algebra or multivariable calculus. If you are serious about machine learning and want to explore how to start learning Machine Learning, there is no doubt that you will have to customize and build your own algorithms as you progress. This means that mathematics, especially linear algebra, and multivariable calculus is important.


• Statistics and Probability
Machine learning algorithms are all based on statistics and probability, at heart. Therefore, you would definitely have to have a deep understanding of statistical theory, like Bayes rule, independence, and the likes. Analysis models and distributions in statistics should also be covered, and you will have to be comfortable working with them for a long time.

Bayesian concepts to be covered while covering the basics include maximum likelihood, priors, posteriors and the entire concept of conditional probability. The frequentist way of thinking commonly used with datasets are discarded in this case – the statistical model is followed. You need to have statistical knowledge if you are planning to make a long, successful career in this.

• Data Modeling
 Data modeling refers to the process of estimating the structure of a data set, and this is done so that you can find out any variations or patterns within this. A lot of machine learning is also based on predictive modeling, so you would have to know how to predict the various properties of the data you have at hand. Iterative learning algorithms may result in errors being magnified in the set and the model, so a deep understanding of how data modeling functions is also a necessity.

If all of this seems intimidating in your quest to getting a machine learning certification India, make sure to remember that becoming a machine learning professional is not an overnight thing – it would require a certain amount of practice and experience. If you want to know more about how to learn machine learning, check out the machine learning courses available on Imarticus Learning!

However, you would not understand the deeper workings of algorithms or libraries without knowledge about linear algebra or multivariable calculus. If you are serious about machine learning and want to make a career in machine learning, there is no doubt that you will have to customize and build your own algorithms as you progress. This means that mathematics, especially linear algebra, and multivariable calculus is important.


• Statistics and Probability
Prerequisites for Machine learning are all based on statistics and probability, at heart. Therefore, you would definitely have to have a deep understanding of statistical theory, like Bayes rule, independence, and the likes. Analysis models and distributions in statistics should also be covered, and you will have to be comfortable working with them for a long time.

Bayesian concepts to be covered while covering the basics include maximum likelihood, priors, posteriors and the entire concept of conditional probability. The frequentist way of thinking commonly used with datasets are discarded in this case – the statistical model is followed. You need to have statistical knowledge if you are planning to make a long, successful career in this.

• Data Modeling
 Data modeling refers to the process of estimating the structure of a data set, and this is done so that you can find out any variations or patterns within this. A lot of machine learning is also based on predictive modeling, so you would have to know how to predict the various properties of the data you have at hand. Iterative learning algorithms may result in errors being magnified in the set and the model, so a deep understanding of how data modeling functions is also a necessity.

If all of this seems intimidating in your quest to getting a machine learning certification India, make sure to remember that becoming a machine learning professional is not an overnight thing – it would require a certain amount of practice and experience. If you want to know more about how to learn machine learning, check out the machine learning courses available on Imarticus Learning!