Machine learning, as a demand, developed gradually quite rapidly in the most recent years with new methods, technology, languages, new frameworks, new things to learn, which made it very essential for people to be eager to learn. It is a field basically intended for logical minds. As a career, it combines technology, math, and business analysis into one job.
In many workplaces, many of us may be questioning whether or not our job is robot-proofed. We may also be searching for ways in which we can protect the human elements of our skillsets, such as emotional intelligence, humour and creativity. But instead of being on the defensive when it comes to robots at work, why not turn to the offence and seek opportunities?
In fact, there may be more opportunities than we think. There are a few areas in which employees and job seekers can embrace the robots, from project testing to development and engineering, too
What is machine learning?
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
Getting Started with Machine Learning How can you get there?
In order to pave a direct path to machine learning, knowledge and education rooted in computer or applied science is a must. Being well-versed in programming languages such as Python, Java and Scala is a strong asset, though you’ll want to stay informed about industry standards and emerging trends.
Once school is finished, machine learning hopefuls can keep an eye out for job postings such as Machine Learning/Data Engineer or Data Scientist. While on the search for the best fit, aspiring professionals can also further their expertise in coding and big data tools such as Hadoop.
Problem-solving and project testing
Just like humans, robots don’t have the answers to everything (or at least not yet). That’s why problem-solving skills – from people – are still needed in the workplace. As we’re still in the early stages of a ‘robot takeover,’ we don’t have the capabilities for robots to help robots just yet.
When an issue or error arises in the machine learning process, it’s up to the employee to rework and solve the problem so that the robot can complete the assigned task. This can involve trial and error and using critical thinking to come to a solution.
Being an effective problem-solver is a key skill that many employers have long looked for, but if you excel at finding solutions, you may want to consider applying your skills in a project testing role. You have to be willing to think outside the box and try, try again when things (or the robot) aren’t working in your favour.
About Machine Learning Courses
ML is all about applying statistics and computer science to data. You really do not need to be a professional programmer, mathematician to learn ML, but to master it, one has to be good at maths, programming and have some domain knowledge.
There are many programming languages that provide ML capabilities. But Python and R are the most commonly used languages. So, before entering into the world of ML, choose one of these two programming languages – Python or R.
Learn Statistics For Machine Learning: It is good to have some understanding of statistics, especially the Bayesian probability, as it is essential for many machine learning algorithms. And to learn the basics of statistics, you can sign up for descriptive statistics and inferential statistics courses offered by Udacity. Both the courses are free of cost.
ML Courses to Sharpen Your Knowledge
To build a strong machine learning foundation, soak in as much knowledge and theory as possible. There are various courses available to learn about machine learning: To build a strong machine learning foundation, soak in as much knowledge and theory as possible. There are various courses available to learn about machine learning:
Stanford’s Machine Learning Course: It is a course for beginners that provides a broad introduction to machine learning, data mining and statistical pattern recognition. This course is taught by Andrew Ng and covers all basic algorithms. Topics include:
- Supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks).
- Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- Best practices in machine learning (bias/ variance theory; innovation process in machine learning and AI).
Google’s Machine Learning Crash Course with TensorFlow APIs is a 15 hours online course that includes real-world case studies, interactive visualisation, video lectures, 40+ exercise to help teach machine learning concepts. Google originally designed this course for its employees as a part of a two-day boot camp aimed to give a practical introduction to machine learning fundamentals.
Putting theory into practice
Machine learning is more of an art; you can become good only by practising. For advanced level, you need to spend a lot of time working on various machine learning and deep learning problems. And you need datasets to practice building and tuning models. You can start with UCI Machine Learning Repo or Kaggle.
- UCI Machine Learning Repocontains 429 different datasets specially curated for practising machine learning. it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. You can search by task, industry, dataset, size and more.
- Kaggle is a great source of competitions and forums for ML hackathons and helps get one started on practical machine learning. It is an active, engaging and competitive platform and is more famous for hosting data science competition. Once you join Kaggle, don’t go on to expect to win competitions, but look at them as a way to gain real experience and mentoring from the community.
Where to study ML in India?
We list down eight places from where students and professionals can take a formal education on AI and ML:
- PG Diploma in Machine Learning and AI – Upgrad and IIIT-B.
- Foundations of Artificial Intelligence and Machine Learning – IIIT Hyderabad.
- Master of Technology in Artificial Intelligence – University of Hyderabad
- M.Tech. Computer Science Specialization In Artificial Intelligence – UPES.
- M.Tech. Computer Science Specialization In Artificial Intelligence – UPES.
- Artificial Intelligence Nanodegree – Udacity.
- Machine Learning and Artificial Intelligence (AI) – myTectra.
- Artificial intelligence and Machine Learning Training in Bangalore – Zenrays.
Demand for AI and ML specialists in India is expected to see a 60 % rise by 2018 due to the increasing adoption of automation, says KellyOCG India.
Although AI and machine adoption is on the rise in India, there is negligible talent with experience in technologies like deep learning and neural networks.
With 2-4 years experience in ML, a professional commands a salary of Rs. 15-20 Lac per annum, while for 4-8 years it is Rs 20-50 Lac per annum and for 8-15 years it is Rs 50 lacs to Rs 1 Crore per annum.
The Big Data sector is expected to see increased hiring with lucrative offers from startups to Fortune 500 enterprises. Jobs that will be in demand are Machine learning engineers with an average salary ( per annum) of Rs 12,82,000.
The Future of Machine Learning
The world is unquestionably changing in rapid and dramatic ways, and the demand for Machine Learning engineers is going to keep increasing exponentially. The world’s challenges are complex, and they will require complex systems to solve them. Machine Learning engineers are building these systems. If this is YOUR future, then there’s no time like the present to start mastering the skills and developing the mindset you’re going to need to succeed.