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.
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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:
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