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Tensorflow is one of the machine learning (ML) engineer’s favorite open-source libraries for representing the code functions involved in ML, and visualizing mathematical operations used in neural networks and other ML setups.
Here are six courses available on the Coursera learning portal that guide students toward a fuller understanding of the Tensorflow environment.
- Introduction to Tensorflow for AI Machine Learning and Deep Learning (Offered by deeplearning.ai)
- Tensorflow in Practice Learning (Offered by deeplearning.ai)
- Convolutional Neural Networks and Tensorflow (Offered by deeplearning.ai)
- Image Understanding with Tensorflow on GCP (Offered by Google Cloud Platform)
- Serverless Machine Learning with Tensorflow on Google Cloud Platform (Offered by Google Cloud Platform)
- Natural Language Processing with Tensorflow (Offered by deeplearning.ai)
Introduction to Tensorflow for AI Machine Learning and Deep Learning (Offered by deeplearning.ai)
This course helps students understand how to build scalable algorithms, and how deep learning works. Neural networks are one focus of this diversified course that utilizes some of the knowledge of specialist Andrew Ng to show students Tensorflow principles at work.
This is an intermediate-level course that's 100% online and takes approximately eight hours to complete, with a suggested time frame of four weeks.
Students will learn to train a neural network for computer vision, learn Tensorflow best practices, learn to understand convolutional neural networks, and build a basic neural network with Tensorflow.
An all-around guide to this type of visualization and handling of machine learning components.
Tensorflow in Practice Learning (Offered by deeplearning.ai)
Four modules help students explore artificial intelligence (AI) applications and how they're made. Building and training neural networks is part of this curriculum, and students will learn to use convolutions in image processing, in order to facilitate cutting-edge identification and classification capabilities.
Students can get a firsthand look at how machines learn to process text and how neural networks handle input data.
Hands-on elements o the course will show how these types of technologies work in the real world. This online course takes about a month to complete and is an intermediate-level course.
Convolutional Neural Networks and Tensorflow (Offered by deeplearning.ai)
This course focuses specifically on the convolutional neural network, which is a specific kind of concept in the machine learning world.The CNN, as it’s called, handles image processing through the use of various layers within the neural network.
Techniques like striding and padding are used to filter and survey images, and the information gets funnelled through the system to eventually train the computer to identify objects or other aspects of an image.
Students will learn about how a computer "sees" information, and what specific operations lead to effective image processing and identification tasks.
Students will learn about various problems like plot loss, overfitting and drop out in the search for the best practices in building and maintaining CNN capabilities for facial recognition, product development and more.
Transfer learning will also be part of this syllabus, and students will learn more about feature extraction and feature selection as a component of successful dimensionality.
This intermediate-level course is all online and takes about seven hours to complete with a suggested course time frame of four weeks.
Image Understanding with Tensorflow on GCP (Offered by Google Cloud Platform)
This advanced machine learning course is specifically designed with Google Cloud in mind. This top environment has been a go-to for many developers crafting the newest and best ML programs.
This course will show students different strategies for putting together image classifiers and will help them to understand convolutional neural network builds. Feature extraction and selection are also part of the focus of this course, and students will get training in how to prevent overfitting and related problems.
Hands-on components require knowledge of basic SQL, Python and Tensorflow.
This course is 100% online at an advanced level and takes 11 hours to complete with a suggested time investment of 5-7 hours per week.
Serverless Machine Learning with Tensorflow on Google Cloud Platform (Offered by Google Cloud Platform)
This course also utilizes the idea of working with Tensorflow on the Google Cloud Platform, but adds the idea of serverless computing to envision machine learning in a different type of environment.
In serverless computing, functions are designed for as-needed delivery. This course will talk about use cases for this type of setup, and will allow students to participate in building a Tensorflow ML model. There's an emphasis on scalability and deployment with understanding of preprocessing features and how to spin up ML models in an efficient virtualized capacity.
This intermediate-level course is all online and takes 12 hours to complete, with a suggested time frame of one week.
Natural Language Processing with Tensorflow (Offered by deeplearning.ai)
One of the most popular applications of Tensorflow and other machine learning tools is the practice of natural language processing (NLP).
This course will get students familiar with some of the components of NLP related to the tagging of units of speech and other techniques that help neural networks to build structural predictive models. The NLP has benefitted much from ML, and students can benefit from seeing first-hand how these techniques work.
With hands-on study, students will address real-world problems such as how to apply recurrent neural networks and LSTMs in Tensorflow and how to process text using tokenization and vectors.
This course is a 100% online intermediate-level course that takes nine hours to complete with a suggested timeframe of four weeks.
Conclusion
Use any of these innovative learning opportunities to get better connected to the nuts and bolts of ML through understanding not just the terminology, but the builds of systems commonly worked up using Tensorflow.
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