9 Questions That Have Bugged Every Machine Learning Enthusiast
Machine learning is everywhere. Organisations are looking at adopting ML in various forms and functionalities, thanks to its cost-effectiveness and error-free ways. While the demand for ML professionals has increased dramatically over the years, the lack of ML-specific curriculum in the universities has made it challenging to get the right talent. ML enthusiasts are still looking for ways to quench their knowledge and arm themselves for various ML roles. As ML enthusiasts are trying to learn concepts from various resources like ebooks and online courses, they are perplexed with many questions during their learning period. If you are an ML beginner, and have come across any of these 9 listed confusions, we have an answer for you right here: Why Do I Need To Learn ML? More often than not, this is the first question most professionals face. Given the huge popularity of areas like ML and artificial intelligence, it often becomes a rat race or a herd mentality to get acquainted with these skills. Most professionals register for courses without realising the applications and advantages of posing ML knowledge. Therefore, it becomes important to set it right that why you want to learn ML concepts and you might want to know answers to these questions: What is machine learning? It deals with vast data in the most intelligent fashion to derive actionable insights. Why do I need to know it? If you love dealing with data with precision, this is your only way out and you need to be good at it. Where To Begin? A Coursera course by Andrew Ng, a course from Udacity, an ML crash course by Google developers, or other MOOCs — there are a lot of options currently available to get acquainted with ML concepts. While we have covered many articles on various resources that may be useful to learn ML, the trick to choose the right learning resource for you is simple. Identify your area of interest first. For example, if you want to learn TensorFlow or Caffe, Python or R, learn concepts from the scratch or polish your skills, and choose accordingly. There may be many pointers to keep in mind before selecting the right course or book for you. Which Framework To Opt For? There are many ML frameworks for professionals to start working on. Some of them are TensorFlow, Caffe, Microsoft CNTK, PyTorch and others. These are required for building and deploying ML models. With a wide range of options available, it might often confuse beginners on what to begin with their project with. Our past interactions with ML professionals have shown that beginners often tend to incline towards TensorFlow because of its programmatic approach for creation of networks. It also finds a favourable place in the academic community. On the other hand, if you are a Python developer, Python and Scikit-Learn provide enough abstraction for developers to get started with the ML journey. So if you are an ML professional confused on which framework to opt for, you know the answer. Python Or R Or Julia Or Something Else With ML prioritising predictive accuracy over model interpretability or statistical inference, choosing a programming language that aligns with this need is important. While there are a plethora of languages to be learnt for a great career in ML, data science and analytics, Python and R often win the race. For ML enthusiasts with no prior exposure to programming, it might be intimidating to decide on what to pick. While both Python and R languages have developed robust ecosystems of open source tools and libraries to help researchers, when it comes to ML professionals, Python is often favoured given the fact that it is concentrated more with predictive accuracy. R also has enough flexibility to do some good work with ML. Learning Just One Model Or Many Models? Predictive models, descriptive models and reinforcement learning are the popular types of models in ML. The predictive model is used to predict the future outcome based on the historical data and these type of learning algorithms are termed supervised learning. While descriptive models fall under unsupervised learning, reinforcement learning is the one where the machine is trained to take specific decisions based on the specific business requirement to maximise efficiency. When you have started out in ML, it might be confusing to decide on what to learn. But it is always to try as many variations and learn many models and algorithms to get a hands-on experience in this area. How Much Data Is Enough To Build A Right Model? When beginning your work with ML, data plays a crucial role. ML is not magic, you can’t create something from nothing. However, it can give you interesting results from programming and data. Training data in ML is the key and the more data that you use, it would yield better results. Data Vs Intuition Is ML all about data or intuitions play a crucial role? While you may have learnt all the models and algorithms, there doesn’t seem to be a single way to apply these ML concepts. As you keep advancing from beginners to advanced level, we tend to create an intuitive understanding of algorithms by approaching them from different perspectives. There Are Multiple Approaches To Pick While Solving A Problem Having learnt many tools, modules and concepts in ML, it may often get confusing to choose one specific approach to solve a problem. While there are several algorithms in ML that may come handy, it is important to pick algorithms best suited for a particular problem. For instance, choosing between Logistic Regression and K-Nearest Neighbor algorithm can be confusing to a beginner. It is, therefore, necessary to understand the core concepts related to algorithms to evaluate the precision and accuracy of a model. The Challenge Of Picking Debugging Tools Once a model in ML is designed, debugging might often be required to keep things on track. Most researchers believe that the ML model is extremely hard to debug compared to other traditional programs. Unfortunately, there are not many developer tools for ML and they might have to switch to an entirely different toolchain for developing ML models, which might come out as challenging for beginners.
Machine learning is everywhere. Organisations are looking at adopting ML in various forms and functionalities, thanks to its cost-effectiveness and error-free ways. While the demand for ML professionals has increased dramatically over the years, the lack of ML-specific curriculum in the universities has made it challenging to get the right talent. ML enthusiasts are still […]
If you are a machine learning beginner, and have come across any of these 10 listed confusions, we have an answer for you right here:
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["data science", "scikit-learn", "machine learning", "artificial intelligence", "AI", "PyTorch", "ML", "Python", "analytics", "TensorFlow"]
["AI", "artificial intelligence", "machine learning", "ML", "data science", "analytics", "TensorFlow", "PyTorch", "scikit-learn", "Python"]
Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.