Free IBM Certifications on data science , machine learning and python

Free IBM Certifications on data science , machine learning and python

admin | 2022-03-10
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IBM offering some free coursed with certifications for students and professionals. You can also earn badges on completion of these courses.

Here are some listed courses:

Blockchain for Developers:

The first course covers basic blockchain concepts such as shared ledgers, smart contracts, provenance, and consensus. You’ll also see what makes a good blockchain network use case and finally build a simple blockchain network.

The second course shows you how to create more complex blockchain applications. You’ll also learn what developers need to know to contribute to the overall blockchain solution for a business network.

The last course shows you how to build a blockchain network for a specific use case: tracking food and other perishable goods through a supply chain. You’ll use an IoT asset tracker and Node-RED to track and collect temperature, motion, and other data. That data is fed to a blockchain network to be used as an immutable record of transaction history throughout the journey of the cargo.

Applied Data Science with Python:

In these data science courses, you’ll learn how to use the Python language to clean, analyze and visualize data. Through our guided lectures and labs, you’ll get hands-on experience tackling interesting data problems.

This is an action-packed learning path for data science enthusiasts who want to work with real world problems using Python. Make sure to take this learning path to solidify your data skills in Python, before diving into machine learning, big data and deep learning in Python

Deep Learning:

In this learning path, you will be able to learn the basic concepts of Deep Leaning and TensorFlow. Then, you will get hands-on experience in solving problems using Deep Learning. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

 

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