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https://github.com/david-palma/ibm-ai-engineering

This IBM Professional Certificate covers machine and deep learning with Python, using SciPy, Scikit-Learn, Keras, PyTorch, and TensorFlow to solve real-world problems through labs and projects.
https://github.com/david-palma/ibm-ai-engineering

apache-spark artificial-intelligence coursera data-science deep-learning ibm jupyter-notebook machine-learning python

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This IBM Professional Certificate covers machine and deep learning with Python, using SciPy, Scikit-Learn, Keras, PyTorch, and TensorFlow to solve real-world problems through labs and projects.

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README 

        
# IBM AI Engineering Professional Certificate



This repository is meant to provide tips to solve the final assignments of the IBM AI Engineering Professional Certificate courses, thus getting a practical understanding of Machine Learning and Deep Learning, including supervised and unsupervised learning using popular Python libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow.



## List of the assignments



There are several courses in this Professional Certificate, each of which contains a different number of assignments. However, only three courses are detailed here:



- [Machine Learning with Python](<./1 - Machine Learning with Python>)



  This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.



- [Scalable Machine Learning on Big Data using Apache Spark](<./2 - Scalable Machine Learning on Big Data>)

  This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer.



- [Introduction to Deep Learning & Neural Networks with Keras](<./3 - Introduction to Deep Learning with Keras>)



  This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.



## License



This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.