Machine Learning
Pranav Dobhal | Consultant | Calliope DataWhat is Machine Learning?
A Career in Machine Learning is very interesting. Understanding Why one wants to choose a Career in Machine Learning is phenomenally more important than figuring out How to get into Machine Learning. The most authoritative source of information on Machine Learning is someone with real experience in it.
Consultant Pranav Dobhal has worked in Machine Learning for 4 years & 5 months. According to Consultant Pranav Dobhal, Machine Learning is:
Data Engineering is the gathering, collecting and storing of data, conducting batch processing or real-time processing on it, and evaluating data solutions within organisations.
How Consultant Pranav Dobhal got into Machine Learning?
I did Schooling from The Doon School, Dehradun after which I pursued an Undergraduate Degree in Information Systems from Brock University in Canada. I did a Masters degree in Information System Management from Carnegie Mellon University in Pittsburgh, USA.
Consultant Pranav Dobhal's Talk on Machine Learning |
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The Journey of a Machine Learning Engineer: Pranav Topal's Insights into a Thriving Field Hi, I’m Pranav Topal, and I welcome you to the world of machine learning. My journey began in Dehradun, evolved through my academic and professional experiences in Canada, and ultimately led me to Carnegie Mellon University for my master’s degree. In this article, I will share my career path in machine learning, explore the requisite educational background, skills involved, the positives and challenges of the field, and provide a glimpse into a typical day as a machine learning engineer. What Is Machine Learning? Machine learning is a branch of artificial intelligence that focuses on building systems that learn from data to improve their performance. It involves using various algorithms to analyze input data and predict outcomes, making it essential in applications ranging from weather forecasting to autonomous vehicles. The core strength of machine learning lies in its ability to identify patterns and leverage historical information to inform decision-making processes. Essentially, machine learning transforms vast amounts of data into actionable insights, significantly enhancing efficiency and effectiveness across a variety of industries. Education A strong foundation in mathematics is paramount for anyone pursuing a career in machine learning. Mathematical concepts such as statistics, linear algebra, and calculus are crucial for analyzing and interpreting data. For instance, understanding variance and clustering techniques can aid in normalizing data sets to yield accurate results. As I navigated through my studies, I found that the mathematical principles underpinning machine learning models directly influenced my ability to extract meaningful insights from complex data. Computer science knowledge complements the mathematical foundations necessary for machine learning. To develop and implement algorithms effectively, familiarity with programming languages such as Python, Java, and Scala is essential. During my undergraduate degree, I learned that these languages provide extensive libraries that support machine learning tasks. This technical expertise enables engineers to create robust models and systems that execute desired functions efficiently. Statistics forms the bedrock of data analysis in machine learning. Advanced knowledge of statistical methods is critical for ensuring that the chosen analyses align with the dataset at hand. In my career, I have relied heavily on Bayesian probabilities and regression techniques to derive valid conclusions from experiments. A deep understanding of statistics enables practitioners to draw accurate inferences and validate their models effectively. Domain expertise refers to the specialized knowledge that one acquires through experience in a particular field. It plays an indispensable role in machine learning, as understanding the nuances of the industry can significantly enhance the modeling process. For instance, my experience in the sales sector has helped me identify trends and patterns within the data that are crucial for developing predictive models, thus adding substantial value to my work. Skills Analytical skills are vital for machine learning professionals, as we continually work with extensive datasets and strive to make sense of them. This includes not only breaking down complex data but also understanding their implications in the context of different experiments. My ability to analyze data from multiple perspectives has been instrumental in refining my machine learning models. Curiosity drives machine learning innovation. A natural inclination to delve into data and uncover underlying patterns can set an engineer apart in this field. I’ve often found myself sifting through various datasets, piecing together information that can lead to new insights. This innate desire to explore and understand data intricacies is essential for generating effective experiments. Experimentation is at the heart of machine learning. The process involves repeatedly testing hypotheses using different data and methodologies to identify successful outcomes. My daily work revolves around formulating new approaches based on previous results, and the ability to explore various avenues ensures that I continue to grow as a machine learning engineer. The road to machine learning success is often paved with failures, necessitating dedication and persistence in the face of setbacks. Most experiments yield limited results, and remaining driven through repeated failures is crucial. Learning from unsuccessful attempts has shaped my approach, allowing me to adapt and eventually achieve favorable outcomes. In machine learning, embracing failure as a learning opportunity is a vital skill. Each unsuccessful attempt offers invaluable insights into what does not work, guiding the next steps in my experimental process. This mindset of resilience transforms challenges into stepping stones toward success, particularly in an unpredictable field like machine learning. Effective communication skills are essential for translating complex machine learning concepts and results to clients, colleagues, and stakeholders. My experience has taught me that being able to succinctly describe the implications of an experiment, supported by strong visualizations, is key to achieving buy-in and fostering collaboration in data-driven projects. Positives The demand for machine learning professionals has surged in recent years as companies recognize the transformative potential of data-driven decision-making. Each industry, whether healthcare, sales, or customer service, is eager to harness machine learning to optimize their operations. The continuous hunger for expertise in this area underscores the career prospects available to aspiring professionals like myself. Working in machine learning places engineers at the forefront of technological advancement. Engaging with cutting-edge tools and frameworks like Apache Spark and Kafka allows for innovation that shapes the future of artificial intelligence. This aspect of my career excites me as I deal with developing systems that push the envelope of what technology can achieve. Machine learning serves as a stepping stone toward advanced artificial intelligence applications. Mastering this field positions engineers like myself to delve deeper into creating sophisticated neural networks and AI solutions. The promise of contributing to the future of AI motivates me to continually refine my skills and knowledge. The ability to quantify real-life experiences into actionable insights is a unique aspect of machine learning. By utilizing data from platforms like social media, I can analyze trends and behaviors, which can drive better marketing strategies. This practical application of data knowledge makes my work immensely rewarding and relevant. Challenges The high failure rate in machine learning can be disheartening. Most of the experiments I conduct may not yield the expected results, leading to frustration and self-doubt. However, I have learned that dealing with failure is an integral part of the process; resilience and adaptability are crucial in navigating this challenging aspect of the field. Data cleansing is a particularly tedious task that can consume much of a machine learning engineer's time. Ensuring that the dataset is in proper form for analysis requires meticulous attention to detail. Throughout my journey, I have discovered that patience and careful scrutiny are essential when preparing data for meaningful experiments. The turn-around time for experiments in machine learning can vary significantly, sometimes stretching for months depending on data volume and project complexity. This unpredictability necessitates strategic planning and efficient time management. My ability to gauge the right moments to pivot or pause experiments has become crucial in dealing with this challenge. The lack of standardized job descriptions in the field can lead to confusion when entering the job market. Many positions may blur the lines between machine learning, data analysis, and business intelligence, making it essential for candidates to showcase a diverse skill set. Through my experiences, I have navigated this crossover effectively by honing my capabilities in multiple areas. A Day Of A typical day in my life as a machine learning engineer comprises several steps focused on optimal data handling and experimentation. Much of my time is dedicated to data cleansing, ensuring that any missing values are addressed and outliers managed. Once the dataset is refined, I choose the best statistical analyses to apply, followed by programming the necessary algorithms to facilitate processing. I then run the initial experiment, thoroughly testing my code and collaborating with my testing team to validate the results. As I review the outputs, I often discover inconsistencies caused by missing data or incorrect analysis choices, which leads me back to the drawing board. This iterative process of trial, refinement, and re-execution defines much of my daily work, contributing to the dynamic and often challenging nature of the machine learning field. The dedication I bring to my work each day nurtures a passion for continuous learning, ensuring that I remain engaged in this evolving and impactful profession. As I reflect on my journey, I can confidently say that a career in machine learning offers both significant rewards and notable challenges. The potential for innovation and impact within various industries is immense, making this field not only relevant but vital in our technology-driven world. Whether you're considering a career transition or just starting your professional journey, machine learning presents immense opportunities for those willing to embrace its dynamic challenges and embrace their curiosity. | |
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How to get into
Machine Learning?
If you are want to get into Machine Learning, start by investing in a Career Plan.
The 14 hour process, guided by a LifePage Career Advisor, will help you introspect and check whether your interest in Machine Learning is merely an infatuation or is it truly something you wish to do for the rest of your life.
Next, your Career Advisor will help you document how you can get into Machine Learning, what education and skills you need to succeed in Machine Learning, and what positives and challenges you will face in Machine Learning.
Finally, you will get a Career Plan stating which Courses, Certifications, Trainings and other Items you need to do in the next 7 years to become world’s best in Machine Learning.
LifePage Career Plan
14 hour personalized guidance program


Your LifePage Career Advisor facilitates your guided introspection so that you systematically explore various Career options to arrive at a well thought out Career choice.
Next: your Advisor helps you figure out how you will get into your chosen Career and how will you develop the skills needed for success in your Chosen Career.
LifePage Plan will not stop at saying "to become an Architect study Architecture". It will guide you on which Certifications, Trainings and Other items you need to do along with your Architecture education to become the world's best Architect.
Links for this Talk
LifePage Career Talk on Machine Learning

[Career]
https://www.lifepage.in/careers/machine-learning

[Full Talk]
https://lifepage.app.link/20171103-0002

[Trailer]
https://www.youtube.com/watch?v=VjPDuJnYuAw
(Machine Learning, Pranav Dobhal, Calliope Data, Programming, Computer Science, Technology)
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Raj Kumar Saini
Mentor & Central Head, Dehradun | Coding Blocks
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Monit Kapoor
Associate Professor & HoD Cybernetics | School of Computer Science, UPES
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Sagar Gurung
Full Stack Developer | Ifadvertisings
Full Stack Developer | Ifadvertisings
[ 7 years & 1 month Experience ]
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Application development is the process of creating a computer program or a set of programs to perform the different tasks that a business requires. From calculating monthly expenses to scheduling sales reports, applications help businesses automate processes and increase efficiency.
"After completing my BCA & MCA from R V college, Bangalore in 2014, I began working as an Associate Software Consultant at Muraai Information Technology. I moved to Adnate IT Solutions as a Consultant in 2015. In 2017 I joined OpenText, where I am working as a Senior Consultant."
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Software Development
Gargi Gautam
IT Analyst | Tata Consultancy Services
IT Analyst | Tata Consultancy Services
[ 3 years & 9 months Experience ]
Software Development is a process followed for a software project, within a software organization. It consists of a detailed plan describing how to develop, maintain, replace and alter or enhance specific software. The life cycle defines a methodology for improving the quality of software and the overall development process
"After completing my graduation from Uttar Pradesh Technical University in Computer Science (GNIT) in 2012, I started working with Sears IT & Management Services private limited where I worked till 2015 while I was a System Engineer. After that I have joined Tata Consultancy Services where my designation is IT Analyst. Meanwhile in 2017 I had completed my Post Graduation Diploma in Management in Operations Management from All India Management Association."
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Teaching & Research in Data Science
Dr Kingshuk Srivastava
Assistant Professor | UPES
Assistant Professor | UPES
[ 9 years Experience ]
Teaching and Research in Data science of three words teaching, research and data science so first of all what is teaching so teaching is the art and science of helping others to grow in their knowledge and understanding. It is holding the hand of a young one and saying it's going to be OK. Teaching is being careful that you acknowledge every student ever. On the other hand research is a careful and detailed study into a specific problem, concern, or issue using the scientific method. It's the adult form of the science fair projects back in elementary school, where you try and learn something by performing an experiment. Data science is a multidisciplinary blend of data inference, algorithmm development, and technology in order to solve analytically complex problems. Data Science can be applied to a data set with one thousand lines, there is no problem with this.
"After completing my B Sc in Physics & M Sc in Electronics from Lalit Narayan Mithila University, I did M Tech in Information Technology & Ph D in Computer Science Engineering from University of Petroleum & Energy Studies (UPES). I am an Assistant Professor Selection Grade at University of Petroleum & Energy Studies, Dehradun."
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Software Engineering
Vikas Nokhwal
Founder & Senior Software Developer | Nokhwal Technologies
Founder & Senior Software Developer | Nokhwal Technologies
[ 5 years & 6 months Experience ]
Software engineering is a detailed study of engineering to the design, development and maintenance of software.
"I did B.Tech in Computer Science from Om group of Institutions, Hisar. I started working in SSN Ventures as Software Developer. After working for four years with the company, I started my own firm, Nokhwal Technologies in 2017. I am Senior Software Developer at Nokhwal Technologies."
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Nature Inspired Computing
Deepshikha Bhargava
Professor & HoD | UPES
Professor & HoD | UPES
[ 21 years & 2 months Experience ]
Nature-inspired computing is a very new discipline that strives to develop new computing techniques through observing how naturally occurring phenomena behave to solve complex problems in various environmental situations. This has produced groundbreaking research that has created new branches, like neural networks, swarm intelligence, evolutionary computation and artificial immune systems.
"After graduating from University of Rajasthan, I went on to do M Sc, MCA & M Tech (CS). I also hold a Ph D (Computer Science) with specialisation in Artificial Intelligence. I have been working in this area for over 20 years. I am Professor & HoD - Visualisation Dept at UPES."
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Cloud computing is shared pools of configurable computer system resources and higher-level services that can be rapidly provisioned with minimal management effort, often over the Internet. Cloud computing relies on sharing of resources to achieve coherence and economies of scale, similar to a public utility.
"I did bachelors and masters in Computer Science. Post that, I pursued a Ph D in Computer Science. I am aslo AWS certified. I have served as an Assistant Professor at Maharishi Markandeshwar University, Mullana & at HEC, Jagadhri. I am an Associate Professor at University of Petroleum & Energy Studies, Dehradun."
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Information technology is the use of computers to store, retrieve, transmit, and manipulate data, or information, often in the context of a business or other enterprise.
"After completing my B Tech & M Tech in Computer Science Engineering from KURSK State Technical University, Russia, I did Ph D in Robotics. I joined Tirupati Enterprises as IT Manager in 2001. After working there for 4 years, I moved to academia with GRIET, Hyderabad. In 2010, I joined UPES. I am a Professor & Dean at UPES."
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Software Engineering
Devendra Dutt Bijalwan
Senior Analyst | Accenture
Senior Analyst | Accenture
[ 9 years & 3 months Experience ]
Software Engineering is the process of analyzing user needs and designing, constructing, and testing end user applications that will satisfy these needs through the use of software programming languages. It is the application of engineering principles to software development. In contrast to simple programming, software engineering is used for larger and more complex software systems, which are used as critical systems for businesses and organizations.
"After completing my MCA, I started my Career as a Software Engineer by joining Avalon Information Systems. There, I worked with many NGOs. After spending 3 years with Avalon, I joined FIS Global Business Solutions in 2015 and worked on a lot of banking projects with them. After another 3 years, I joined Accenture as Senior Analyst in 2019."
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Teaching Computer Science
Rajan Gupta
Assistant Professor | University of Delhi
Assistant Professor | University of Delhi
[ 6 years & 2 months Experience ]
Computer Science is an academic program that integrates the fields of computer engineering and computer science. It covers the digital aspects of electronics engineering, specializing in hardware-system areas like computer architecture, processor design, high-performance computing, parallel processing, computer networks and software aspects.
"I am a Research & Data Analytics professional, learning new ways to deal with various business and research problems through technological and analytical tools. With the help of past experiences and academic exposure, following are the highlights of current capabilities. I have a substantial experience of working on research & analytics projects in the area of Information Systems, E-Governance and Public Schemes Assessment. I have working knowledge of application of various data science concepts through statistical techniques, operation research & optimization, data mining and machine learning across multiple industries (healthcare, retail, education, etc.) and domains. I have hands-on experience of using various analytical & development tools like SPSS, R, Tableau/MS Power BI and MATLAB and exposure of training and teaching in specialized areas of IT & Analytics. I am UGC NET-JRF Qualified and also possess management consulting certification from Government of India. I am one of the few Analytics professional in India to have CAP (INFORMS) and GStat (ASA) certification & accreditation, respectively and also serving as the Brand Ambassador of CAP in Asia Region for INFORMS. I am efficient in report preparation and article writing. I can prepare research proposals, research frameworks, data source identification, data collection framework and data preparations. I have experience of handling teams up to 50 people for project execution."
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DevOps is the combination of cultural philosophies, practices, and tools that increases an organization's ability to deliver applications and services at high velocity: evolving and improving products at a faster pace than organizations using traditional software development and infrastructure management processes.
"I did BTech in Mechanical Engineering from Nagarjuna University and M Tech in Mechanical Engineering from IIT Delhi. After that I joined TCS as product manager and worked there for 8 years. I have also worked with Infosys, CSC, Virtusa Polaris, Kogentix, Pyramid Consulting Inc and CTO for a startup, I was author at Packt and written books on Devops, Bigdata and Reality of Relations. I am currently working with Sunera Tech as VP and Practice Head of Data Monetization ( Analytics, Machine Learning, Devops on Cloud)."
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Software Engineering
Amit Ved
Application Development Manager | ST Microelectronics
Application Development Manager | ST Microelectronics
[ 15 years & 7 months Experience ]
Software engineering is the application of engineering to the development of software in a systematic method.
"I did B Sc & M Sc in Statistics from Kanpur University. I then did an MCA from UPTU and started working at India Worldwide Software System as Software Tester. In 2004 I joined ST Microelectronics as Software Support Professional. I am now an Application Development Manager at ST Microelectronics."
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Technology Sales
Ankkit
Sales & Strategic Alliance Manager | Tejas Networks
Sales & Strategic Alliance Manager | Tejas Networks
[ 10 years & 1 month Experience ]
Technology sales professionals sell advanced devices such as computers to individuals and businesses. While many jobs exist in retail stores, some technology sales professionals travel to offices, workshops, and sales events such as conventions and expos.
"After completing BE & MBA, I started with Bharti Airtel and continued working with various MNC's. Post that, I joined Tejas Networks as the Sales & Strategic Alliance Manager."
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DevOps & Unix Administration
Rahul Parsai
Team Lead | STMicroelectronics
Team Lead | STMicroelectronics
[ 10 years & 1 month Experience ]
DevOps is a set of software development practices that combines software development and information technology operations to shorten the systems development life cycle while delivering features, fixes, and updates frequently in close alignment with business objectives.
"After completing my B Tech from NIT Bhopal, I worked at HCL as Engineer Trainee. I then worked at Supra Group of Industries as a Software Engineer and then at Steria Consulting Pvt Ltd as Senior Software Engineer. I am working at STMicroelectronics as Team Lead."
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Emerging Technologies
Munish Dhiman
CEO | Gesture Research International Ltd
CEO | Gesture Research International Ltd
[ 8 years & 6 months Experience ]
Technology entrepreneurship is an investment in a project that assembles and deploys specialized individuals and heterogeneous assets that are intricately related to advances in scientific and technological knowledge for the purpose of creating and capturing value for a firm.
"After completing my education, I worked as an Analyst at THEIKOS INDIA Pvt Ltd for 6 months. Later I worked as a Project Manager at V- Microsoft Crop United States. In 2010, I worked as Strategic Advisor and Consultant at Multiverse. In 2012, I started my own venture called Gesture Research International Ltd and I am also the Managing Partner with CivilCops Dubai, United Arab Emirates."
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