With smart algorithms being used almost everywhere these days – from smartphone apps and emails to marketing campaigns and more, machine learning (ML) has got a great boost. Usually, machine learning is linked to AI (artificial intelligence) that helps computers execute specific tasks (without being programmed explicitly) like diagnosis, recognition, planning, prediction, robot control etc. This field focuses on developing algorithms that can learn to grow and modify when they get exposed to new data. To some extent, you can call machine learning similar to data mining since both search through loads of data to find patterns. Yet, they are different since data mining applications extract data for human comprehension, while data is used in machine learning to improve upon the understanding of the program itself. In other words, machine learning programs search data to identify patterns, based on which the program actions are adjusted.
With the field generating a lot of buzz these days, many find machine learning a lucrative domain for their careers. If you too plan to learn machine learning for taking up an in-demand career with AI and smart machines, here are ten ways that can propel you on the road to success:
1. Know what machine learning is
From understanding and experiencing what machine learning is, to knowing the basic maths behind machine learning algorithms, learning how and when to apply them, comprehending the alternative technology, and having hands-on experience, you need a solid grasp on what the field entails as the first step to learn machine learning.
2. Learn and hone the key skill sets
For a successful career in machine learning, you should have in-depth knowledge of probability and statistics; programming languages such as C++/Python/Java/R; distributed computing; Unix tools; applied math and algorithms; and advanced signal processing algorithms.
3. Staying updated and honing other skills
Staying updated about any present or upcoming changes in the field is another key task. You should be aware of news related to theory and algorithms (through following, watching and reading blogs, conference videos, relevant research papers etc), new tools development (via conferences, changelog etc). Whether you Learn machine learning online or not, you should surely read free online resources like machine learning books together with useful papers like Google File System, Google Map-Reduce, The Unreasonable Effectiveness of Data, and Google Big Table.
4. Fan your curiosity
Being curious is a key requirement for being successful in the domain of machine learning, irrespective of whether you learn machine learning online or in the offline mode. Over the last few years, this field has rapidly evolved with new frameworks, techniques, technology and languages, which in turn have brought to the forefront new things to learn. Thus, unless you are eager to learn and get into the habit of taking new online courses relevant to your work, or read articles and about the new developments, being successful would be a tough job.
5. Focus on an approach that’s the ideal fit for the task at hand
A successful machine learning career isn’t just about selecting the right algorithm or tool. You would also need to decide what approach is the ideal fit for the specific situation you are dealing with and design it accordingly. For example, unlike the complicated algorithms for an autonomous car where utilizing your resources to execute incremental algorithm improvement is worth the trouble, those for an online marketing campaign need optimization of all the logistics involved, starting from adjusting the data and GUI to how the users are engaged with or their concerns and feedbacks are listened to.
6. Feed the right data
Using the right data goes a long way in ensuring you are getting valuable insights from your ML algorithms. If you fail to feed the right data, you won’t get the results that you are looking for, even after making algorithm tweaks. You should remember that data is supremely important, and the right data set will give you a far more lift than tweaking your algorithms endlessly. So, mind your data carefully when you learn machine learning and hold onto this habit when you start your work life in the ML domain.
7. Be ready to adapt and change your algorithms
Whether you are encouraging your system users to take an action, or deploying a system, hoping your algorithms would run perfectly forever is a foolish thought. You should remember that machine learning programs flourish in settings where continual trial and error happens. Even after you have designed a pretty good algorithm, it would need to be altered over time when it’s interacting with humans. This means you need to constantly monitor your ML program’s overall effectiveness after its implementation to spot the variables and changes that make it work for the better or worse, and adjust it accordingly to make it work in line with your desired goals. Though this may sound obvious to many, only some are doing this in reality. Even amongst those who are doing it, very few are doing it really well.
8. Be open to use a diverse toolset
A successful career in ML demands that you use a variety of tools. Thus instead of staying glued to just a single tool, you should be ready to use several, and create your systems that are tool-agnostic. You will find a lot of ML tools –both free and paid, from which you can take your pick. From open-source frameworks libraries like Caffe, Shogun, H20, Torch and TensorFlow that you can use for free, to subscription-based options like BigML, Amazon Machine Learning and Microsoft Azure Machine Learning Studio, your choices are many. You may even use ML libraries in several ASF (Apache Software Foundation) projects including SINGA, Mahout, and Spark. The free Cognitive Toolkit of Microsoft is also worth using.
9. Learn to convert business problems into ML problems
Since ML is practically a logical field where mathematics blends with technology and business analysis, you should be able to turn a business problem into a mathematical machine learning problem. This means apart from having a solid grasp on technology, you also need to have intellectual curiosity and a logical bend of mind. So, hone this skill when you learn machine learning because being articulate in converting a business problem into an ML problem is what would help bring value at the end.
10. Develop team-player skills
Machine learning is a collaborative field, where you are most likely to work as a team member. The team is likely to have statisticians, data scientists, software programmers and quantitative analysts, along with others who have direct dealings with the business. Thus, to be a successful machine learning practitioner, you must be capable of and ready to interact with the team members and play your role as an effective team player.
Now that you have a blueprint of success, make the most of this machine learning career opportunity when you learn machine learning online or otherwise.