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How to start a career in Machine Learning

How to start a career in Machine Learning

Machine learning is a branch of AI that focuses on developing computer programs that can learn through repeated exposure to data. The programs improve as they become more experienced at reading patterns. For example, speech recognition or facial recognition are functions of machine learning. When you feed facial data to your phone, for instance, it will store this information and can use it to login in the future by drawing on this stored information. Similarly, an advertisement on a website or an email that seems aligned to your interests is all down to machine learning algorithms using your browsing information to recommend products it deems will be of interest to you.
These are just a few examples. As most industries continue to imagine ways they could increase efficiency, application of machine learning will continue to grow in new and exciting ways.
Why go for a career in Machine Learning
Machine learning is fast becoming a relevant field in today’s business world. A survey conducted by O’Reilley, found that 51% of companies are in the early stages of adopting machine learning while 36% are seasoned users. This growth brings with it opportunities for anyone with software and data skills. In fact, the same study found that (job titles related to machine learning are common in organizations that have adopted machine learning) = 39% of companies surveyed reported having jobs titled Machine learning engineer while 20% had deep learning engineer titles.
With demand comes attractive pay packages. A 2018 job satisfaction survey conducted by KDnuggets found that while data science jobs are still very satisfying and well paying, machine learning is the next big thing in terms of opportunity, job satisfaction, and best salaries. Most machine learning jobs pay north of $100,000. With such attractive pay, it is easy to see why anyone seeking to make a significant career jump would want to go into machine learning.

Figure 1: Job satisfaction survey by KDnuggets
Besides the salary gains mentioned above, a lot of professionals find machine learning attractive because of what it can do for them.
Achieve what was impossible before. That is, improve methods and find new uses for data
Before machine learning, taking advantage of opportunities, increasing the security of client accounts, anticipating consumer wants, among others, were all problems that were both time and money intensive to solve. Implementing machine learning algorithms helps you to solve such problems quickly.
Mark of achievement
If you like mastering new technologies, machine learning is a great place to start. Not only will you prove to yourself that you can master difficult subjects, but with your new skills, you will have the ability to create tools and implement algorithms by yourself.
Expand your current career and solve problems
Burtch Works reports that generalist ‘unicorns’ will soon be a thing of the past. The job market is in demand for specialists in AI and machine learning who can use tools like TensorFlow and work in areas such as image processing and language processing.
If you work with data or are a systems administrator, taking a course in machine learning will allow you to be more productive at your job. The ability to solve system problems and to give actionable insights are both skills that will make you more invaluable to your company.
Skills you need to succeed in a Machine Learning Job
To start a career in machine learning, you need the following skills:
Mathematics and statistics
This includes proficiency in the following domains:
With knowledge in probabilistic techniques such as Bayes Nets and Markov processes, you can deal with the predictive aspects of machine learning algorithms.
Statistical techniques such as hypothesis testing and variance analysis make the building blocks of machine learning algorithms.
Data Modeling
You will work with unstructured data, and for this, you need to know how to estimate the structure of data sets, find patterns in data and find ways around irregular data sets.
Software Design
For machine learning algorithms and applications to work well, they should integrate with existing software components. As such, you should understand APIs (static APIs, dynamic libraries, web APIs, etcetera) in addition to being able to design interfaces that are flexible and can adapt to future changes. As well, skills such as requirements analysis, documentation, developing use cases and testing, will enable you to create reliable software.
Applying ML Libraries & Algorithms
There are many algorithms and libraries such as MLib, Tensorflow, and CNTK that have already been developed by other developers. Part of your job will be to implement these libraries and algorithms. However, even though you don’t have to create your own algorithms, you still need to know how to apply these existing models effectively. This means you should understand learning procedures, models and their application to technology. You also need to know and anticipate potential pitfalls.
Programming Skills
Programming skills and knowledge of software development are fundamental skills in creating dynamic algorithms. Subsets of these skills include:
Knowledge of computer architecture and data structures
Understanding concepts such as b-trees, stacks, sorting algorithms, among others
Machine learning can be implemented in many programming languages. However, the components of each language determine its suitability for different ML tasks and projects. The 2 most common ML languages include:
R is tailored for data mining and statistical computing. In fact, there exists hundreds of statistical models and algorithms created in R to perform different tasks. Though its syntax differs from other languages, R is not challenging to learn.
Python is a general purpose and is the most favored language by machine learning engineers. Python contains libraries such as pandas, SciPy and NumPy, which are useful for data processing. Its machine learning libraries include Theano, tensorflow and scikit-learn, all of which have varying capabilities necessary for training machines. Theano’s speed, for instance, makes it great for running complex computational tasks. Tensorflow, with its multi-layered nodes, makes it easy to use large datasets to train and set up neural networks. Scikit Learn is a good choice for novice learners, or for those who don’t have in-depth knowledge of programming as it can be applied to most purposes without needing to do any extensive coding. Scikit-Learn is useful for data mining tasks.
Steps to start a career in Machine Learning
There is no one specified way to start a career in machine learning. Where you begin depends on your level of experience at the moment. For instance, an undergrad student, a Ph.D., and someone with work experience may all different learning paths. The learning path taken will also depend on knowledge in areas like mathematics, programming, and statistics. As such, whereas experience counts, it should be in the fields that support the fundamental skills required to study machine learning.
Below are some possible paths to take to build a career in machine learning:
You have work experience and a background in Software Engineering
If this is where you are, you should proceed as follows
Study the basics of machine learning. You can do this by enrolling in one of the machine learning courses offered at SimpliLearn.
Work as part of a team of machine learning engineers. Since your background is software engineering, contribute to the team with this skill.
As you continue learning on the job, get involved in as many tasks as possible involving creating algorithms.
As you gain experience and confidence, you can move on to doing machine learning jobs full time.
You are have just graduated with computer science
If this is you today, one thing you could do is to join a team as a software engineer and from there, take the career path outlined above for an experienced software engineer. This, however, could be a long path and as there are entry-level jobs that now require machine learning, you could take a course right away. You will need programming skills, knowledge of statistics and mathematics to enroll in a machine learning certificate course.
You are a data scientist/analyst or researcher
The advantage for you as a data analyst is that you are already comfortable working with data. Machine learning will give you the tools you need to unlock more possibilities with your data. For your career path, proceed as follows:
If you do not know any programming languages, start by learning python or R. You should also polish up on your algebra and statistics.
Learn the basics of machine learning. You can either take the courses mentioned above or enroll for the Deep learning with TensorFlow course.
Get a job that requires knowledge of machine learning. This will help you to keep learning on the job and to perfect your skills.
Final words: Don’t stop learning
Andrew Ng, adjunct professor at Stanford emphasizes the importance of keeping on learning until you get to your desired level of proficiency. You shouldn’t expect to get really good overnight. Even after your course, keep learning and keep practicing. Look for data sets to work with and opportunities to create algorithms.
More so, interact and compete with other machine learners on platforms such a Kaggle to learn what they know and to sharpen your skills. Read books, attend webinars and connect to more experienced machine learning professionals through their blogs or social media pages. Total immersion is the key to getting really good after you get your certification.

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“machine learning courses offered at SimpliLearn” =

“Deep learning with TensorFlow” =

“Andrew Ng” =

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