Skills Needed to Learn Machine Learning

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What does a Machine Learning Engineer do?


The job of a Machine Learning Engineer is quite similar to that of a DataScientist, in the sense that both roles involve working with vast volumes ofdata. Hence, both Machine Learning Engineers and Data Scientists must possessexcellent data management skills. However, that’s all the similarity thatthese two roles share.Data Scientists are mainly concerned with generating valuable insights fordriving business growth through data-oriented decision making. In contrast,Machine Learning Engineers focus on designing self-running software forpredictive model automation.Usually, Machine Learning Engineers work in close collaboration with DataScientists. While Data Scientists extract meaningful insights from largedatasets and communicate the information to business stakeholders, MachineLearning Engineers ensure that the models used by Data Scientists can ingestvast amounts of real-time data for generating more accurate results.

Skills Needed to Learn Machine Learning


Now that you know why you should choose a career in machine learning and haveperhaps made a decision as well, let us tell you a few important skills thatyou need to have to make the most of this opportunity.To make your career to grow as a machine learning engineer and earn thehighest possible machine learning salary in India, you need to have updateyourself with: * Statistics and probability: Your understanding of algorithms has a lot to do with your knowledge of theories. Hidden Markov models, Naïve Bayes, and Gaussian Mixture, amongst other models, are great examples. If you aren’t good at statistics and probability, you will find these algorithms very difficult to understand. * Data evaluation and modeling: An important part of ensuring the efficiency of the estimation process with ML is to evaluate the effectiveness of different models at regular intervals. You can use different methods, such as regression and classification, to measure the error or accuracy of a given model. You also need an evaluation strategy to go with it. * ML algorithms: Knowing how different machine learning algorithms work and which algorithm fits in a particular situation is very important. You would need to develop an understanding of subjects like partial differential equations, gradient descent, quadratic programming, convex optimization, and others. * Programming languages: For a career in machine learning, you also need to be skilled in different programming languages, such as Java, R, Python, and C++. These languages will help you at different stages of your machine learning project. * Signal processing techniques: Feature extraction is very important to machine learning. You may have to work with several advanced signal processing algorithms, including bandlets, shearlets, contourlets, and curvelets, amongst others. Learn more about machine learning required skillsets.

1. Machine Learning Engineer


Artificial Intelligence and Machine Learning are mere umbrella terms for anumber of applications that are applied across disciplines and technologies.They also involve robotics, data analytics, web development, developingchatbots, intelligent application development, and much more.Since late 2017, AI and ML have taken the nation by storm. Frameworks such asOpenCV, PyTorch, and TensorFlow have become buzzwords for today’s AI/MLenthusiasts.Responsibilities of a Machine Learning Engineer * To study and convert data science prototypes. * To design and develop Machine Learning systems and schemes. * To perform statistical analysis and fine-tune models using test results. * To find available datasets online for training purposes. * To train and re-train ML systems and models as and when necessary. * To extend and enrich existing ML frameworks and libraries. * To develop Machine Learning apps according to customer/client requirements. * To research, experiment with, and implement suitable ML algorithms and tools. * To analyze the problem-solving capabilities and use-cases of ML algorithms and rank them by their success probability.Salary of a Machine Learning Engineer in IndiaThe average annual salary of a machine learning engineer is ₹671,548. Machinelearning engineers with less than 1-year experience earns around ₹500,000 perannum which is clearly one of the highest entry-level salaries in India. Earlylevel machine learning engineers charge ₹672,106 per annum, obviously,depending on their skill set, location, and demand.The average salary of a mid-level engineer is ₹1,173,074 per annum. If theythink that’s great, you will find the salary of senior-level engineers (morethan 10 years experience) inspiring as they earn more than 2 million rupeesper annum.Source

4. Machine learning and Data Science are intricately linked


If religion ruled the masses for entire centuries before modernity, it is nowtrue that Data Science rules the masses, due to its all-explaining nature andcommercial as well as innovative viability.And Machine Learning is just a shadow of Data Science. To take your career ashigh as you can’t even imagine, you can become competent in both these fields,which will enable you to analyse a frightening amount of data, and thenproceed to extract value and provide insight on the data.Moreover, in many organisations, ML engineers and Data Scientists worktogether on products, so it is likely that you will be exposed to the DataScientists’ perspective if you’ve already become an ML engineer.

PG Diploma in Machine Learning and AI


Become a Machine Learning Engineer and learn how to train an agent to play tictac toe, train a Chatbot, and lots more Enroll Now @ upGradBest Masters Programs in Data Science & Analytics in Australia | TopAustralian Universities for Big Data AnalyticsApart from being a great holiday destination, Australia is also a popularstudy abroad destination for Indian and international students. Australiaoffers great education & teaching, along with high quality of living. With thebuzz (and overhyped to some extent) around data science and big data, a lot ofstudents are considering to pursue Masters in Data Science and Analytics inAustralia. According to the Institute of Analytics Professionals of Australia(IAPA) survey report, data scientists can earn up to AUD $200, 000 annually.The current job market in Australia is quite promising for Data Scientists andBig Data Analytics professionals. Apart from offering high-quality educationand training, the top Australian universities are also very active in terms ofindustry and Government collaborations, including in the field of datascience.The business analytics field is also gaining a lot of interest. Businessanalytics takes a data-driven approach using statistics and data modelling todevelop new business insights. This blend of technology and business makes itan ideal study option for anyone with an interest in programming or workingwith big data. Business analytics is used in most of the world’s majorcompanies and technology start-ups to exploit any data-driven advantage forgrowth hacking. Some larger businesses may not have their own in-housebusiness analysts, but there is also a notable range of business analyticscompanies which offer their services on a consulting basis. Due to the risinginterest in business analytics in the tech sector, more and more businessschools have begun to offer a Master’s in Business Analytics.As postgraduate programs, both data science and business analytics arerelatively new. I have made this list after referring to course content,industry links, university profile, alumni reviews (LinkedIn, Quora & Reddit),and job prospects. I have excluded few programs (e.g. ANU) that are not opento the international students.

Master of Data Science – RMIT University


RMIT consistently ranks among the top universities in Australia, and theirData Science program is certainly a good one. It’s a 2-year full-time Masterdegree program that combines analytics & computer science and focuses onbusiness decision-making, corporate strategy, and government planning.The first year of the program develops a solid foundation in computer scienceand statistics – core skills necessary for every data scientist. The second-year also includes a major project, which can be working on an industry orresearch project while based on campus, or off-campus as an internship workingas a data scientist in the industry.Students and graduates can join the Institute of Analytics Professionals ofAustralia (IAPA); so that’s a great incentive. Besides, the program hassubstantial industrial links, both within Australia and internationally.The program was originally launched as a joint initiative between RMITUniversity and National ICT Australia (NICTA), now Data61, the lab applies totext, user, and data analytics research to industry-driven projects that solveproblems and provide efficiencies in key areas including health, logistics,smart cities, environment, and security. Additionally, RMIT also offers aMaster of Analytics program.Program Fees: AUD $65, 280Scholarships: Scholarships are available to support fees, accommodation andliving expenses.Program Link

Graduate Certificate in Data Analytics


The Graduate Certificate in Data Analytics is offered collaboratively byVirginia Tech’s Departments of Computer Science, Electrical and ComputerEngineering, and Statistics. This certificate prepares students for technicalcareers in big data analytics and data science. Students will acquire in-depthtechnical skills that will enable them to understand the underlying technicalfundamentals of data analytics, to develop new analytical methods, and toengineer new analytical tools. Students acquire skills that integratecomputational, statistical, and engineering techniques that form the heart ofbig data analytics. This certificate provides students with formal recognitionof their skills to better support their career prospects.There is a growing need for technically trained engineers and scientists tolead the rapidly evolving field of big data analytics. The U.S. presidentialadministration has identified big data analytics as a core area of nationalneed. Data science is one of the fastest growing career paths, and demand fortechnical expertise is out-pacing supply. Technical expertise is needed todevelop new methods, tools, and infrastructures required to support novel bigdata analytics operations in industry, government, and academia. The technicalexpertise required involves a combination of computation, statistics, andengineering, such that training in any one of these individual disciplinesalone does not suffice. This certificate serves to train technical studentswith a broader view across these disciplines to support the data analyticsfield.The learning outcomes of Data Analytics certificate program: * Students will have technical depth in the fundamentals of data analytics, in terms of understanding the underlying principles and implementations of analytical methods. * Students will have broad understanding of multi-disciplinary perspectives on technical methods in data analytics, including computational, statistical, and engineering perspectives.

Statistics, Data Science & Analytics


Data science and big data analytics have been termed as the sexiest job of the21st century. Data is the new oil of the modern digital economy. There is ahuge amount of data around us, and it’s expanding at an exponential rate. Thechallenge is that this big data set (Big Data) is noisy and heterogeneous. So,it’s very important to extract knowledge or insights from the data around us.Data science consists of 3 pillars: Statistics & Machine Learning, ComputerScience, & Software Development, and Domain Knowledge.Analytics is a fast-growing field and presents exciting career opportunitiesfor students and professionals with strong quantitative and analytical skills.The analytics field combines extensive use of data, statistical andquantitative analysis, exploratory and predictive models, and fact-basedmanagement.The objectives are to investigate patterns, behavior, and trends, and to drivebusiness decisions and actions. Analytics gets used in various sectors thatinclude financial services, retail, FMCG, healthcare, travel, media etc.Recently, unconventional sectors like education, manufacturing, and sports arealso implementing the innovative use of analytics to get a competitive edge.Related Posts:Careers in Actuarial ScienceCareer Opportunities with a Degree in Statistics in India and AbroadData Science Jobs in IndiaHow to Get Data Science, Machine Learning & AI JobsMS in Data Science in the USMS in Data Science & Big Data Analytics in CanadaYou might also like the following articles:Tips for International Students to get Jobs in the UKBest Countries with Post-Study Work Opportunities for International Students

MSc Business Analytics and Big Data – IE Business School


The Master’s program in Business Analytics and Big Data (MBD) at IE BusinessSchool is a 10-month intensive program. Students will engage in threeintensive team challenges to get practical, hands-on experience workingdirectly with industry experts. The four major program areas are businesstransformation, data science, big data technologies, and professional skills.Read more about the MBD program at IE Business School.

MSc Statistics and Machine Learning – Linkoping University


Another cutting edge and innovative program that focuses on modern methodsfrom machine learning and database management that use the power of statisticsto build efficient models, make reliable predictions and optimal decisions.The program provides students with unique skills that are among the mostvalued in the labor market.The program is designed for students with a background in mathematics, appliedmathematics, statistics, computer science or engineering. Most of the coursesincluded in the program provide students with deep theoretical knowledge andpractical experience from extensive amounts of laboratory work.In the final term of the Master’s program, students will receive help from theUniversity to find a private company or a government institution where theycan work towards their Master’s thesis. So, it’s an excellent opportunity forthe students to apply the knowledge to a real problem and meet professionalswho use advanced data analytics on a daily basis. Learn more about theMaster’s program in Machine Learning & Statistics at Linkoping University.Book a 1:1 Consultation Session with a Data Science and Analytics Expert

Master’s in Machine Learning, Data Science and Artificial Intelligence –


Aalto UniversityMachine learning is one of the major strengths of Aalto University. ThisMaster’s program will give you excellent opportunities for a career inresearch institutions or in the private sector in the rapidly developingfields of machine learning, data science, and artificial intelligence.The methods of machine learning and data mining are applicable and needed in awide variety of fields ranging from process industry to data science. Recentspearhead application areas include * bioinformatics * computational astrophysics, biology, and medicine * interactive technologies * information retrieval * information visualization * neuroinformatics * social-network analysisThe major in Machine Learning, Data Science and Artificial Intelligence(Macadamia) covers a wide range of topics in modern computational dataanalysis and modeling methodologies. The major of Machine Learning, DataScience and Artificial Intelligence (Macadamia) offers also a competitivedoctoral (PhD) track where a limited number of top students can be admitted.Visit the official program page.

MSc Computer Science: Data Analytics – National University of Ireland (NUI)


GalwayIt’s a 1-year advanced MSc program delivered by the Information TechnologyDiscipline (College of Engineering & Informatics), in collaboration with theInsight Centre for Data Analytics in NUI Galway, which is Europe’s largestresearch center for Data Analytics. The program is also supported by theSchool of Mathematics, Statistics and Applied Mathematics and the School ofElectrical and Electronic Engineering at NUI Galway.The program covers areas such as large-scale data analytics, advanced machinelearning and data-mining, information retrieval, natural language processing,and web mining. It also includes foundational modules on topics such asstatistics, probability, and programming for data analytics.Students on the program further deepen their knowledge of Data Analytics byworking on a project either in conjunction with a research group or with anindustry partner. Know more about the Data Analytics program at NUI Galway.

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