返回到 Introduction to Machine Learning

4.9

19 个评分

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6 个审阅

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with TensorFlow, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more)....

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6 个审阅

创建者 Shukshin Ivan

•Nov 24, 2018

It was great to touch new professional area and to understand its fundamentals. The course gives a broad view on machine learning, so I think now I really understand, what the machine learning is and how to use it in my work and even my political investigations.

创建者 Ayse Ulubay

•Nov 12, 2018

I like this introductory course, very good one to start to learn machine learning. I will definitely continue studying and re-watch the videos.

创建者 KAVADIBALLARI VINEESH

•Oct 24, 2018

GOOD COURSE

创建者 Erica Ryan

•Oct 05, 2018

This was a really great course for understanding the basics of machine learning through a lot of simple but relevant, real world examples.

创建者 Michael Boerrigter

•Sep 30, 2018

Excellent course. Concepts such as gradient descent and convolutions as they pertain to neural networks are explained without going into the mathematical details but, in my opinion, are explained more intuitively and better, as compared to most other courses. The course does include some ungraded Jupyter notebooks exemplifying key elements of deep learning networks. Highly recommended to 'cement' understanding of neural networks.

创建者 Sameera Koluguri

•Sep 19, 2018

Very Good course explaining the theoretical concepts related to deep learning . Thank you