Overall, 2017 saw an upward trend in talent acquisition across Machine Learning. This will further increase in 2018.
With technology such as Machine learning, AI and predictive analytics reshaping the business landscape, software product, aggregators, Fintech and E-commerce will drive the demand for technology professionals in India.
Machine Learning is usually associated with Artificial Intelligence (AI) that provides computers with the ability to do certain tasks, such as recognition, diagnosis, planning, robot control, prediction, etc., without being explicitly programmed. It focuses on the development of algorithms that can teach themselves to grow and change when exposed to new data.
Also Read : How to Start a Career in Machine Learning?
Now, are you trying to understand some of the skills necessary to get a Machine Learning job? A great candidate should have a deep understanding of a broad set of algorithms and applied math, problem solving and analytical skills, probability and statistics and programming languages.
Here is a list of key skill sets in detail:
- Programming Languages like Python/C++/R/Java: If you want a job in Machine Learning, you will probably have to learn all these languages at some point. C++ can help in speeding code up. R works great in statistics and plots, and Hadoop is Java-based, so you probably need to implement mappers and reducers in Java.
- Probability and Statistics: Theories help in learning about algorithms. Great samples are Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models. You need to have a firm understanding of Probability and Stats to understand these models. Use statistics as a model evaluation metric: confusion matrices, receiver-operator curves, p-values, etc.
- Data Modeling & Evaluation: A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy/error measure (e.g. log-loss for classification, sum-of-squared-errors for regression, etc.) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.)
- Machine Learning Algorithms: Having a firm understanding of algorithm theory and knowing how the algorithm works, you can also discriminate models such as SVMs. You will need to understand subjects such as gradient decent, convex optimization, quadratic programming, partial differential equations and alike.
- Distributed Computing: Most of the time, machine learning jobs entail working with large data sets these days. You cannot process this data using single machine, you need to distribute it across an entire cluster. Projects such as Apache Hadoop and cloud services like Amazon’s EC2 makes it easier and cost-effective.
- Advanced Signal Processing techniques: Feature extraction is one of the most important parts of machine-learning. Different types of problems need various solutions, you may be able to utilize really cool advance signal processing algorithms such as: wavelets, shearlets, curvelets, contourlets, bandlets.
- Other skills:
- Update yourself: You must stay up to date with any up and coming changes. It also means being aware of the news regarding the development to the tools (changelog, conferences, etc.), theory and algorithms (research papers, blogs, conference videos, etc.).
- Read a lot: Read papers like Google Map-Reduce, Google File System, Google Big Table, The Unreasonable Effectiveness of Data.
The next question you would have is, “What can I do to develop these skills?” Unless you already have a strong quantitative background, the road to becoming a Machine Learning Specialist will be a bit challenging – but not impossible.
However, if it’s something you’re sincerely interested in, and have a passion for Machine Learning and lifelong learning, don’t let your background discourage you from pursuing Machine Learning as a career.
There is industry endorsed courses available for the same that you can pursue and excel in Machine Learning. You can check our website imarticus.org for more information.
Related Post : What is The Easiest Way To Learn Machine Learning?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The goal of machine learning is to get computers to learn in a similar manner to humans.
Machine learning is a type of artificial intelligence that helps computers learn without having to be programmed by a person. These computers are programmed in a way that focuses on data that they receive on a regular basis. This data can then help the machine “learn” what preferences are and adjust itself accordingly.
Nowadays, the development in Artificial Intelligence (AI) have brought us to the stage where organizations are using a various algorithms, analysis, and experience to learn and program themselves without human intervention.
This type of procedure will create changes too many industries. The use of machine learning has grown exponentially in the past few years, and you may not realize how widely it is used.
Following stats are just tip of the iceberg:
- 85% of customer interactions will be managed without humans by 2020.
- 38% of jobs could be replaced by AI/machine learning by the 2030s.
- 20% of top executives rely on machine learning to run their businesses.
- 48% projected growth in the Automotive Industry by 2025.
Source: jigtechnologies.com; elearninginfographics.com; pwc.co.uk; mckinsey.com
Have you ever wondered why your Facebook feed contains several sponsored advertisements for digital cameras the day after you do a Google search for good digital cameras? Or have you seen supercomputers like Watson max all questions on a show and go on to beat established quiz enthusiasts hands down? All of it is machine learning in action for you, where those computers or software systems have been fed large amounts of data and also given algorithms that help them think like a human brain would, to make educated decisions. Artificial intelligence and machine learning have captured the imagination of scientists, corporates, users and even job seekers all over the world. As a job seeker, though, you should be clear about the skills you will need before you jump on to the bandwagon.
Also Read: Future of Machine Learning in India
Many job seekers have a wrong notion that picking up a few programming languages is the first step to a machine learning career. But the skillsets required going some way further back. Since machine learning will involve a non-human entity making correct predictions and inferences, therefore the focus is on cold, hard logic and mathematical principles. You need to have a strong grounding in probability (Markov and Bayes should be names familiar to you!) and also statistics. You need to have the knack for looking at a big pile of data (probably unstructured), and be able to identify gaps in it, and also spot trends and patterns.
Programming Requirements (Skills)
This is the core of the skillsets you would require and would be likely to require you to have a formal certification too. You need to be able to use your programming skills and your knowledge of data structures and computer architecture to create dynamic algorithms. You should be adept at parallel programming, and the basics of stacks and b-trees should come easily to you. A good machine learning engineer needs to be proficient in application programming interfaces (APIs), and also have the ability to use machine learning libraries created by other developers.
Programming Requirements (Languages)
As we know it today, machine learning is technically bound to any one particular programming language, so you do have a choice there. But let us examine the three most popular programming languages that machine learning engineers commonly use. First up is Python whose multiple libraries like NumPy and SciPy (and also specific machine learning libraries like Theano or TensorFlow) make it one of the most popular programming languages for machine learning. Then there is another language called R, which lends itself very well to machine learning. This programming language is a favourite of data scientists and statistical programmers and has been happily adopted by several machine learning engineers. And finally, we have the old warhorse C++, which is not as advanced as Python or R, but because it is good for networking protocols and infrastructure interfacing, it is still used for machine learning programming.
Apart from these technical skills which you need, two more skills are also necessary – the ability to look at a system holistically (including sales, inventory, billing etc.), and the ability to remember that your output would have machines as the audience, not humans! So are you up for it?
Machine language is generally related artificial intelligence, which provides the machine or computers with the ability to complete certain tasks like diagnosis, planning, prediction, recognition or robot control. It consists of different algorithms, which you can use to teach the machines to change and grow when exposed to new data.
The process of implementing machine language is somewhat similar to data mining because the process looks through data and searches for the same pattern. Now that you have an idea of what machine learning is, let’s have a look at the skills that are required to get a machine learning job.
Also Read: Future of Machine Learning in India
#1: Computer Science & Programming Skills
Some of the fundamentals of computer science are essential when you are looking to learn machine learning. Concepts like data structure, algorithms, complexity and computability, along with computer architecture are essential for artificial intelligence. In fact, you should also have knowledge of programming languages like C, C++, Java, Python and R, among others. A little bit knowledge of assembly language doesn’t hurt either.
#2: Probability & Statistics
Conditional probability, and its characteristics and the techniques derived from it plays a key role in the machine learning algorithms. Moreover, you should also know about the different terms of statistics like mean, median and mode along with variance and standard deviation. These are all necessary to not only observe the pattern but also validate the data that is received through different means. Some machine learning algorithms are in essence an extension of the common statistical operation procedures.
#3: Applied Mathematics & Algorithms
You need to know not only how to solve a problem but also how to implement it in short executable steps when it comes to machine learning. Algorithms help you to understand how to break down a problem into executable steps, and that is why this is important. In addition, you also need to know about gradient, convex optimization and its application in daily life, so that you can implement it in machine learning.
#4: Operating Systems
When it comes to machine learning, most of the coding is done in Linux or some version of it. So, you need to be versatile with Unix or a version of Linux, which is in use presently. You also need to know about the Linux tools, which will make your life easier in the long run. Some examples include grep, find, sort and tr.
#5: Software Engineering & Designing of Systems
When you are designing a machine learning tool, you are also designing an advanced software. So, at the end of the day, you need to know how to design system, and how you can implement your ideas in that. You also need to understand how different algorithms interact with your system, and how you can speed up the process without compromising on the resource space.
Now that you know about the skills required for machine learning jobs, it is time to get started on acquiring these skills. In case you have some of these skills, make sure you hone them so that you can implement it and build a great system when the time comes. Good luck!
Related Article: What is The Easiest Way To Learn Machine Learning?
Machine Learning is applied to enable machines to process and make decisions by figuring patterns without explicit programming. This can be achieved via multiple techniques one of them is through training machines on a large dataset called training dataset that is used to create models to help machines in making decisions when exposed to real-time data.
There is no shortcut to learning, and when it comes to Machine Learning the process is definitely not quick but if you are inclined to Artificial Intelligence then there is a smarter way of ensuring quality learning with little investment.
Also Read: Future of Machine Learning in India
Machine Learning is about optimization and to optimize data mining learners should have a decent level of programming knowledge and skills. There are many languages that provide Machine Learning capabilities and there are various online courses available to learn them, but it is imperative to choose a language you already have some background with to make sure you pick up fast. Python is easy to learn and is optimal for data manipulation and repeated tasks while R caret is a little elusive but is good for ad-hoc analysis and exploring datasets.
Before you really embark on your journey to become a Machine Learning specialist you need to understand the concepts of Machine Learning and invest in the theory of it via specific online courses like Machine Learning course from Andrew Ng and Learning from Data course by Prof. Yaser Abu-Mostafa. Learning from videos has proven to be more efficient and quick, although the power of books should never be undermined since in this article our focus is to make learning quicker I recommend videos and slideshows over books and papers.
As you acquire deeper knowledge of Machine Learning you would come across various Machine Learning algorithms, these are broadly classified into three categories based on the amount of “feedback” provided to a system to enforce learning, these categories are:
1.) Supervised Learning.
2.) Un-Supervised Learning.
3.) Reinforcement Learning.
To acquire a better understanding of these algorithms you need to have the fundamental knowledge of Linear algebra, Probability theory, Optimization, Calculus and Multivariable calculus etc.
Machine Learning works on raw unstructured big data so it is important for you to understand data statistics including descriptive and inferential statistics. There are a number of online courses on statistics, Udacity and Edx provide courses that cover descriptive stats, probability, and inferential stats.
You also need to have a deep understanding of various Data Cleaning techniques and different stages of data explorations to deal with a large number of unstructured data bits. Most of the times Machine Learning systems need to process incomplete or damaged/scrambled data, for such scenarios handy knowledge of techniques like Variable Identification, Univariate and Multivariate analysis, Missing values treatment, Outlier treatment becomes very useful.
Once you have undergone the basic courses for Machine Learning foundation building it is time to practice what you have learned, Kaggle Knowledge competition is a good place to start. By experimenting more you can polish your skills well and know your level, and shortcomings on which you can work on. Popular Machine Learning communities to help you further in learning are as follows:
Related Article: What are The Skills You Need to Become a Machine Learning Engineer?
The field of Machine learning is expanding fast nowadays with the application of smart algorithms being applied from apps to emails to as far as marketing campaigns. What this means is that machine learning or Artificial Intelligence is the new in-demand career option you can choose.
But being a new field comparatively, you may have many doubts and confusion as to how you can actually get yourself to adopt Machine learning as a career. Let’s ponder over some things you need to master to get your career in machine learning startup.
Also Read : Future of Machine Learning in India
- Understand the field first
It is an obvious but important fact. Understanding the concept of machine learning and basic math behind it along with the alternative technology while also having hands-on experience with the technology is the key to dive into this field at first.
- Covert problems in Mathematics
Having a logical mind is imperative in machine learning. You need to be able to blend technology, analysis and math together in this field. Your focus on technology must be strong and you must possess curiosity along with openness toward business problems. The ability to pronounce a business problem into a mathematical one will take you long into the field only.
- Background in Data Analysis
A background in data analysis is perfect for transitioning or getting into machine learning as a career. An analytical mindset is crucial for success in the field, which means one has to possess the ability to ponder over causes, consequences and discipline to search for the data and digging into it, understand the working and its consequences.
- Gain knowledge of the industry first
Machine learning, like any other industry, possesses its own unique needs and goals. Therefore, the more you research and learn about your desired industry, the better you’ll do here. You have to study the basic and everyday working of the industry along with all the technicalities involved in it.
Where to Find Work as Machine Learning Expert
Job portals are a good way to find work in your starting days in machine learning. You can apply for a job in portals such as Indeed.com, Monster, Glassdoor, etc. You can sign up on some freelancing site (such as Upwork) too to get your starting assignment as a machine learning expert.
The best companies to work for in the field
Two types of companies can provide you machine learning job as present: huge MNCs and established companies, or start-up businesses. There are two basic markets at present for machine learning experts for you to tap on. First is the Cloud and the other is the logs, which allows companies or analytics to let customers create their own algorithms.
The large companies which dominate the data analysis and Machine learning field include Databricks and IBM Watson Analytics. Google has also made forays into the AI recently while many of its partners are also looking for professionals to get their machine learning initiative started.
Machine learning (ML) technologies represent one of the exciting new aspects of the digital age. These technologies are premised on sophisticated algorithms that empower modern enterprises to tackle a variety of business problems. Computers and digital systems that use ML are designed to gain experience from various processes and apply certain rules and data sets to perform complex calculations.
Modern machine learning systems also leverage the use of cloud technologies in a bid to maximize speed and cost-effectiveness.
Also Read : Future of Machine Learning in India
- Recent advances in the commercialization of cloud computing services allow business organizations to utilize huge offerings in compute and storage services. Large cloud players are offering modern enterprises an opportunity to use cloud computing solutions powered by machine learning technologies. These systems are also “creating new opportunities for innovators to offload labour-intensive research and analysis to the cloud.”
- Machine learning systems are enabling business decision makers to visualize data more efficiently. The use of these technologies enables business analysts and business managers to access and utilize data analysis paradigms. This means that machine learning systems are essentially crunching huge volumes of data and electronic information and presenting patterns, analysis, and insights to modern businesses. This personnel can analyze these patterns to quickly initiate business decisions in response to evolving market conditions.
- Digital technologies have emerged as a major enabler in modern societies. Machine learning and artificial intelligence technologies are no exceptions. Cloud-based machine learning algorithms can process current data from business environments to predict future consumer requirements, market trends, etc. This enables business organizations to process unprecedented amounts of data in the ultimate pursuit of growing and expanding their commercial footprints. Companies and brands that can effectively anticipate future requirements are better positioned for future market performance.
- Certain industries such as logistics and transportation can gain clear benefits by implementing machine learning technologies. Vehicles can be fitted with digital devices and transmission systems that generate data regarding the performance of vehicle systems and sub-systems. Analysis of such data can help vehicle designers and engineers to refine and improve the performance of each vehicle over time. Higher mileage from each vehicle and fewer maintenance hours can help these businesses to earn larger profits.
- Machine learning algorithms can help the banking and insurance industry to spot and prevent instances of fraud. Certain insurance service providers are using the technology to scan the faces of loan applicants and insurance policy applicants. These algorithms have access to huge databases that enable them to detect any scope or intent of fraud ahead of time. Thus, machine learning systems help these service providers to expand the scope of their business while cutting scope for malfeasance and thereby reducing losses. Such use of machine learning technologies is expected to gain momentum in time.
- Artificial intelligence technologies and machine learning algorithms are helping businesses to make decisions that are more efficient. Retailers can use these technologies to analyze sales data from the past and other points of market information to control their inventories and supplies. This approach removes the scope of guesswork in certain aspects of business operations while creating scope for efficient operations and greater profits.
Related Article : Skills Required to Learn Machine Learning
For the first time, an industry-wide survey was conducted to establish a comprehensive view of the state of data science and machine learning. We received over 16,000 responses and learned a ton about who is working with data, what’s happening at the cutting edge of machine learning across industries, and how new data scientists can best break into the field. The below report shares some of our key findings and includes interactive visualizations so you can easily cut the data to find out exactly what you want to know.
Machine Learning (ML) is a subset of Artificial Intelligence, which enables the computers to perform certain tasks such as Recognition, Diagnosis, Planning, Robotics Control, Prediction etc., without specific programming. Machine Learning focuses on developing algorithms with the capability of teaching itself to grow and adapt when exposed to new sets of data. As a result, there is a massive interest in the field of machine learning, in individuals who wish to pursue their career in this field, as well as organisations who wish to reap the benefits by its application.
As a Machine Learning engineer, it is very important that you understand not only the specific skill set, but also that you have a fair understanding of the environment, for which you are designing.
Also Read: Future of Machine Learning in India
Let us understand this with the help of an example, assume that you are working for a retail store. And let us say the company wants to design a reward system, through which coupons are issued based on facts like, purchase history, with the intent that the issued coupons will actually be used. Now traditional data analysis approach would be to study the historical data, and figure out trends, and subsequently propose a strategy. But in the Machine Learning approach an engineer would need to create an automated coupon generation system, however, you will only be successful, if you understand the peripheral functions of the environment like the inventory, Catalogue, Pricing, Purchase Orders, Invoice Generation, CRM Software etc.,
So the skill requirement is not only restricted to the application of machine learning algorithms and the understanding of what to apply when, it is also equally important to understand the Interconnected Relationships of these Functions so that you can then successfully create a software which integrates interface, for an effective output.
Now for the real deal, the actual technical skills you need to kick-start your career as a machine learning engineer. You need to have a good and detailed understanding of the ML Algorithms, Mathematics, Skills in Problem Solving and Analytical thinking, and above all an innate sense of Curiosity. In addition to this, the below mentioned Skill….
Programming Languages like C++ can help in speeding code up, R, Python & Java works wonders for statistics.
Theories like Naïve Bayes, Hidden Markov Model, would require you to have a good understanding of Probability and Statistics so that you can comprehend these models.
A firm understanding of Applied Math and Algorithm theory, along with the knowledge of how the algorithms works, will help you discriminate models.
You will also need to skill yourself on Distributed Computing, as a machine learning role would require you to work on large datasets, which cannot be processed using a single machine, but you will be required to distribute it across an entire cluster
Data Modelling and Evaluation, Data Modelling is the process of estimating the underlying structure of any given dataset, with the intent of finding a pattern that is useful or picks up predictions of previously unseen trends. This process will be futile if the appropriate evaluation is not done to access the effectiveness of the model. So that you can choose an appropriate error measure, and apply an evaluation strategy, it is important that you understand these measures, even while applying standard algorithms.
Software Engineering and System Design, are considered as the typical output of any ML engineer’s deliverables. It is that small component that becomes a part of the larger ecosystem. Like said earlier you need to make the puzzle, keeping in mind the various components, ensure they work with the help of proper communication of the system with the interface, and finally carefully design the system such, that any bottlenecks are avoided and the algorithms successfully scale along with the volume of data.
It is hence without a doubt that the demand for machine Learning Engineers will rise exponentially, as the challenges of the world are complex and only complex systems will be able to solve them. Machine Learning Engineers are building these complex systems, therefore you become the future!
Also Read : Skills Required to Learn Machine Learning
Artificial Intelligence (AI), Machine learning (ML) and Deep Learning (DL), can be imagined as the three bears from Goldilocks staying together in a house, where each member has a specific use but yet conceptually they are interconnected. Artificial Intelligence is the big umbrella under which resides the Machine Learning concepts, and Deep Learning can be referred to as a sub-set of Machine Learning. So while Deep Learning and subsequently Machine learning comprises of Artificial Intelligence, the other way around is not necessarily true.
The field of data science is buzzing with these terminologies, and in all the noise it is very easily misinterpreted, and often Machine Learning and Deep Learning are used interchangeably for one another. One thing is certain that Deep Learning is a technique for implementing Machine Learning.
Also Read: Future of Machine Learning in India
Let us understand this by breaking down the puzzle….
Artificial Intelligence orchestrates the capacity of a machine to comprehend complex human tasks like, decision making, understanding spoken language, detecting fraud, in short, it enables a machine to imitate intelligent human behaviour.
Machine Learning is surely a part of AI, where it enables a computer to act in a specific way without the need for explicit programming. This is imperative as the volume of data is ever increasing and to keep up, the machine should have the capability of implementing effective algorithms which can effectively and efficiently make predictions by recognising patterns. To perform the same, data scientists have a number of existing ML methods or Algorithms which can be easily applied to any data problem, at the same time it can be applied to a number of real-life use cases, for example, recommendation engines, or applying Natural Language Processing (NLP) in chat logs.
Deep Learning is a subset of ML and when data scientist refers to the term deep learning they most often mean Deep Artificial Neural Networks or alternatively Deep Reinforcement Learning.
Deep Artificial Neural Networks are essentially a set of Algorithms which are popular in recent times, for setting new records in accuracy while dealing with complex problems like, Image Recognition, Sound Recognition, Recommendations Systems, etc…, To add on, one can easily define DL in a similar manner to ML, so it can be safely said that DL also enables a computer to act in a specific manner without the need of explicit programming, with the addition that DL ensures it produces results with higher accuracy.
DL is often more complex as compared to ML, the prerequisites to DL would be a high-performance computer and huge volumes of Labelled data to give reliable results.
ML can be used with a small volume data set and has a shorter training time, while DL will be effective with large volume data set and often requires a longer training time. In ML you can use your own features, so for example, if you need a computer to be trained on recognising an image of a cat, you will first need to key in all the relevant features of a cat into it. In DL you will need to feed the computer with large volumes of data with cat images, and the system will choose the features on itself which characterises a cat. Hence the training time is longer, while the chances of accurate information are higher due to the complexity of reaching the conclusion, in some cases, it is more accurate than humans.
Driverless Cars, Movie Recommendations and preventive health care are all possibilities enabled by Deep Learning. DL in the future could be responsible for machine assistance in possibly all aspects of life. Deep Learning has the promise of taking the application of AI from fantasy to reality.
Related Article: Skills Required to Learn Machine Learning