Everything You Need To Become A Machine Learner


This list of resources is specifically targeted at Web Developers and Data Scientists.… so do with it what you will…

This list borrows heavily from multiple lists created by : sindresorhus



Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.

The goal of AI is to create computer models that exhibit “intelligent behaviors” like humans, according to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.

Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.”

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Natural language processing

Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.

Neural networks

Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.

In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.



Be familiar with howMachine Learning is applied at other companies



Be able to frame anMachine Learning problem



Be familiar with data ethics



Be able to import data from multiple sources



Be able to setup data annotation efficiently



Be able to manipulate data with Numpy



Be able to manipulate data with Pandas



Be able to manipulate data in spreadsheets



Be able to manipulate data in databases



Be able to use Linux



Be able to perform feature selection and engineering



Be able to experiment in a notebook



Be able to visualize data



Be able to do literature review using research papers



Be able to model problems mathematically



Be able to setup project structure



Be able to version control code



Be able to version control data



Be able to use experiment management tools



Be able to setup model validation



Be familiar with inner working of models



Bays theorem is super interesting and applicable ==> – [📰] Naive Bayes classification



Be able to improve models



Be familiar with fundamental Machine Learning concepts

CNN



Implement models in scikit-learn



Be able to implement models in Tensorflow and Keras



Be able to implement models in PyTorch



Be able to implement models using cloud services



Be able to apply unsupervised learning algorithms



Be able to implement NLP models



Be familiar with multi-modal machine learning



Be familiar with Recommendation Systems



Be able to implement computer vision models



Be able to model graphs and network data



Be able to implement models for timeseries and forecasting



Be familiar with basics of Reinforcement Learning



Be able to perform hyperparameter tuning



Be familiar with literature on model interpretability



Be able to optimize models for inference



Be able to write unit tests



Be familiar withMachine Learning System Design



Be able to serveMachine Learning models



Be able to setup batch inference



Be able to build interactive UI for models



Be able to use Docker for containerization



Be able to use Cloud



Be familiar with serverless architecture



Be able to monitorMachine Learning models



Be able to perform load testing



Be able to perform A/B testing



Be proficient in Python



Have a general understanding of other parts of the stack



Be familiar with fundamental Computer Science concepts



Be able to apply proper software engineering process



Be able to efficiently use a text editor



Be able to communicate and collaborate well



Be familiar with the hiring pipeline



Broaden Perspective

Source: DEV Community

August 6, 2021
Category : News
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Senior Software Developer

Creator of @LzoMedia I am a backend software developer based in London who likes beautiful code and has an adherence to standards & love's open-source.