Top Keras courses you should complete in 2020

Learn the most popular deep learning library.

Keras is one of the most popular machine learning libraries currently. It’s versatile, easy to use and bringing great capabilities. In this text I’ll review the most popular courses on Coursera related to Keras.

Best Keras courses and tutorials

Introduction to Deep Learning & Neural Networks with Keras is a course offered by IBM.

After completing this course, learners will be able to:

• describe what a neural network is, what a deep learning model is, and the difference between them.

• demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.

• demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.

• build deep learning models and networks using the Keras library.

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IBM AI Engineering is another course offered by IBM and it covers more than just Keras.

Thanks to this course you will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.

You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.

By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.

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Introduction to Deep Learning is a course offered by HSE university. The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers.

Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.

The prerequisites for this course are:

  • Basic knowledge of Python.
  • Basic linear algebra and probability.

Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand:

  • Linear regression: mean squared error, analytical solution.
  • Logistic regression: model, cross-entropy loss, class probability estimation.
  • Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions.
  • The problem of overfitting.
  • Regularization for linear models.
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Using Machine Learning in Trading and Finance is a different course offered by New York Institute of Finance as it directs finance professionals, investment management professionals, and traders. But this Specialization can be also for machine learning professionals who seek to apply their craft to trading strategies.

At the end of the course you will be able to do the following:

  • Design basic quantitative trading strategies
  • Use Keras and Tensorflow to build machine learning models
  • Build a pair trading strategy prediction model and back test it
  • Build a momentum-based trading model and back test it

To be successful in this course, you should have a basic competency in Python programming and familiarity with the Scikit Learn, Statsmodels and Pandas library. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).


First of all, here’s a video version of this text where I talk about top Keras courses on Coursera:

Best Keras courses on Coursera review

All in all those 4 courses are the most popular currently when it comes to learning Keras and putting it to use.

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Data Science Job

Finally if you want to learn more about becoming a Data Scientist, read my book Data Science Job: How to become a Data Scientist which will guide you through the process of becoming a data scientist from the very beginning to the very end, no matter what’s your background.

*disclaimer: the above links are affiliate.

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