|Coursera TensorFlow Specialization Fully Available|
|Written by Sue Gee|
|Monday, 05 August 2019|
The fourth and final course of Coursera's TensorFlow Specialization is now available, with modules on sequences, time series and prediction. Coursera also has two other new Specializations in related areas.
Sequences, Time Series and Prediction completes the TensorFlow in Practice Specialization. This is a four-month program at intermediate level intended for software developers who want to build AI-powered algorithms using TensorFlow, the AI framework originated by Google and now open sourced.
We first reported on this ML (machine learning) Specialization, which is a collaboration between Andrew Ng's company, deeplearning.ai, and Google's TensorFlow team, when the first, introductory, course launched in March, see TensorFlow For Beginners From Coursera. Now that all its courses available the TensorFlow in Practice Specialization has the following learning outcomes:
It consists of the following courses:
The pre-requisites for this latest and final, module are to have taken the first 3 courses of the TensorFlow Specialization, to be comfortable coding in Python and with high school-level math. it has the following learning outcomes:
If you want to go further with neural networks, Coursera's Deep Learning Specialization, also from deeplearning.ai, comprises five courses, between 2 and 4 weeks each (77 hours in total) at intermediate to advanced level. If you need more grounding in Machine Learning as preparation, Andrew Ng's popular Machine Learning course is still available as a free, standalone course.
Coursera has recently added two other Specializations in the closely related areas of artificial intelligence and reinforcement learning.
Applied AI: Artificial Intelligence with IBM Watson Specialization is offered by IBM and consists of six courses, each of 4-5 weeks, at beginner level. Its blurb states:
Rather than create complex AI algorithms and interfaces from scratch, learners will use IBM Watson AI services and APIs to create smart applications with minimal coding. By the end of this Specialization, learners will complete several projects that showcase proficiency in applied AI.
Reinforcement Learning Specialization comes from the University of Alberta and consists of four courses, each of 4-5 weeks, at intermediate level. Its learning outcomes are:
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|Last Updated ( Monday, 05 August 2019 )|