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Mathematics for Data Science – Towards Data Science

#artificialintelligence

Learning the theoretical background for data science or machine learning can be a daunting experience, as it involves multiple fields of mathematics, and a long list of online resources. In this piece, my goal is to suggest resources to build the mathematical background necessary to get up and running in data science practical/research work. These suggestions are derived from my own experience in the data science field, and following up with the latest resources suggested by the community. However, if you are a beginner in machine learning and looking to get a job in industry, I don't recommend studying all the math before starting to do actual practical work, this bottom up approach is counter-productive and you'll get discouraged, as you started with the theory (dull?) before the practice (fun!). My advice is to do it the other way around (top down approach), learn how to code, learn how to use the PyData stack (Pandas, sklearn, Keras, etc..), get your hands dirty building real world projects, use libraries documentations and YouTube/Medium tutorials.


The Best Resources I Used to Teach Myself Machine Learning

#artificialintelligence

The field of machine learning is becoming more and more mainstream every year. With this growth come many libraries and tools to abstract away some of the most difficult concepts to implement for people starting out. Most people will say you need a higher level degree in ML to work in the industry. If you love working with data and practical math, then I would say this is not true. I did not graduate college with a Machine Learning or data degree yet I am working with ML right now at a startup.



Free Online Resources To Get Hands-On Deep Learning

#artificialintelligence

With deep learning gaining its momentum in fields like self-driving cars, object detection, voice assistants and text generation, to name a few, the demand for deep learning experts in organisations has also significantly increased. As a matter of fact, big tech companies like Facebook, Google, Apple as well as Microsoft have started investing heavily on deep learning projects which, in turn, increase the number of deep learning open jobs in the market. Having said that, deep learning is one of the complex subsets of machine learning and envelops several layers of components which cannot be grasped in a day. Hence, despite the high demand, there is indeed a gap in deep learning talent for organisations. Not only does it come with prerequisites of linear algebra and calculus knowledge but also enough interest to pursue a complicated subject like deep learning.


The Best Resources I Used to Teach Myself Machine Learning -- Part 1

#artificialintelligence

The field of machine learning is becoming more and more mainstream every year. With this growth, come many libraries and tools to abstract away some of the most difficult concepts to implement for people starting out. Most people will say you need a higher level degree in ML to work in the industry. If you love working with data and practical math, then I would say this is not true. I did not graduate college with a Machine Learning or data degree yet I am working with ML right now at a startup.