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Master Machine Learning & Neural Networks for Data Science, Time Series, & NLP
Updated on Sep, 2026
Programming, Data Science, Deep Learning
Duration - 3.5 hours
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In this self-paced course, you will learn how to use Tensorflow 2 to build recurrent neural networks (RNNs). We'll study the Simple RNN (Elman unit), the GRU, and the LSTM. We'll investigate the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We'll apply RNNs to both time series forecasting and natural language processing (NLP). We'll apply LSTMs to stock "price" predictions, but in a different way compared to most other resources. It will mostly be an investigation about what not to do, and how not to make the same mistakes that most blogs and courses make when predicting stocks. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.
This course is intended for:
Suggested prerequisites:
In this course, we will cover:
Check out the detailed breakdown of what’s inside the course
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