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We live in a world fundamentally transformed by our own creations. Once imagined only in science fiction, artificial intelligence now powers much of the technology we interact with every day--from smart home devices to cognitive assistants to media recommenders. While subtle by design, the impact of AI is far-reaching. The field of education is no less affected by these technologies. AI shows up in instructional chatbots, personalized learning systems and administrative tools.

In this post, you will learn about the hold out method used during the process of training machine learning model. When evaluating machine learning (ML) models, the question that arises is whether the model is the best model available from the algorithm hypothesis space in terms of generalization error on the unseen / future data set. Whether the model is trained and tested using the most appropriate method. Out of available models, which model to select? These questions are taken care using what is called as hold out method.

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

However, the standard CPython implementation's global interpreter lock (GIL) prevents running the bytecode in multiple threads simultaneously.

Electrification was, without a doubt, the greatest engineering marvel of the 20th century. The electric motor was invented way back in 1821, and the electrical circuit was mathematically analyzed in 1827. But factory electrification, household electrification, and railway electrification all started slowly several decades later. The field of AI was formally founded in 1956. But it's only now--more than six decades later--that AI is expected to revolutionize the way humanity will live and work in the coming decades.

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When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

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