The goal of someone learning ML should be to use it to improve everyday tasks—whether work-related or personal. To do this, it's important to first understand algorithms.
How did Spark become so efficient in data processing compared to MapReduce? Learn about Spark's powerful stack of libraries and big data processing functionalities.
And no, we're not talking about Pavlov's dogs here. Learn about the reinforcement learning aspect of machine learning and the key algorithms that are involved!
Clustering algorithms let machines group data points or items into groups with similar characteristics. See how to use the k-means algorithm with Oracle to do clustering.
Imitation learning can help us solve sample inefficiency and computational feasibility problems, and also might potentially make the AI training process safer.
In the fourth issue of DZone's Coffee With a Data Scientist, we had a chat with business analytics evangelist, Tuhin Chattopadhyay, to glean some of his expert insights and opinions on the Big Data space.
Here's what 22 executives who are familiar with AI said when we asked them, "What are the most common issues you see preventing companies from realizing the benefits of AI?"
Having machine learning skills is not enough. You also need good working knowledge of data structures. Learn more and get some problems to practice with.
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