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Machine Learning might be a great career move for you, especially if Artificial Intelligence and how it works fascinates you. It is through ML that Apple’s Siri and Microsoft’s Cortana came into being. Additionally, Machine Learning is responsible for providing you with recommendations of products on e-shopping sites like Amazon or show recommendations on websites like Netflix. If the working behind these features excites you, then Machine Learning is where you should move your career graph to. Moreover, all corporate giants in some way looking to keep themselves updated in this field. They are building technologies that would bring them closer to Artificial Intelligence. Therefore, it would definitely be a good way to up skill yourself and move up in your career.
It is one of the basic requirements to kick-start a career in Machine Learning. The basic knowledge of linear algebra would suffice at the beginning of your career. Machine Learning is very closely related to statistics as well. You need to know the fundamentals of statistics and probability theory, descriptive statistics, Baye’s rule and random variables, probability distributions, sampling, hypothesis testing, regression, and decision analysis.
You need to know how to work with matrices and lmpw some basic operations on matrices such as matrix addition, subtraction, scalar and vector multiplication, inverse, transposing, and vector spaces.
Another aspect that is a necessity for a Machine Learning career would be programming. Learning languages like Python and R could be a great way to start off. These are the most used languages. Moreover, once you have formed a base in programming it would be easier and beneficial for you to learn more programming languages. A little bit of coding skills is enough, but it is better to have knowledge of data structures, algorithms, and OOPs concept. It is up to you to decide which programming language you want to master, but it is advisable to have a little understanding of other languages and what their advantages and disadvantages are over your preferred one.
Data Engineer Skills
You need to be able to work with large amounts of data and have knowledge about data pre-processing, SQL and NoSQL, ETL, Data Analysis, and Data Visualization.
Machine Learning Algorithms
You need to be familiar with popular ML algorithms such as linear regression, logistic regression, decision trees, random forest, clustering, reinforcement learning, and neural networks.
Machine Learning Frameworks
You need to be familiar with popular ML frameworks such as scikit-learn, TensorFlow, Azure, Caffe, Theano, Spark, and Torch.
Skill sets aside, programming for ML requires a change in conceptual thinking. Developing innovative Machine Learning applications, requires a deeper understanding of how an ML algorithm makes the decisions it does. The programmer who lacks this knowledge reduces ML to the level of a mitigating the ability to effectively iterate or grow the platform.