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coursera-ibm-ai-engineering

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IBM - AI Engineering Professional Certificate

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# coursera-ibm-ai-engineering

Coursera: [AI Engineering Professional Certificate](https://www.coursera.org/professional-certificates/ai-engineer) by IBM.

Syllabus: [IBM AI Engineering](IBM-AI-Engineering.ipynb)

## Certificates

[IBM AI Engineering Professional Certificate](Coursera%202CP6LZTVXSGC.pdf)

* [Machine Learning with Python](Coursera%20SG8FTPQYZN6A.pdf)
* [Scalable Machine Learning on Big Data using Apache Spark](Coursera%20AQY45J4F23LH.pdf)
* [Introduction to Deep Learning & Neural Networks with Keras](Coursera%20KF38F7KA8ELK.pdf)
* [Deep Neural Networks with PyTorch](Coursera%20Z3YDVGBMT25B.pdf)
* [Building Deep Learning Models with TensorFlow](Coursera%20ZUKB5RN7G5L5.pdf)
* [AI Capstone Project with Deep Learning](Coursera%2036FTF6J66NXJ.pdf)

## Notes and Summary

This public repo contains only the syllabus of the Professional Certificate. The solved notebooks, quizzes answers and datasets are not included here.

## About this Professional Certificate
The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.

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.

## Course 1: Machine Learning with Python
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.

In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!

## Course 2: Scalable Machine Learning on Big Data using Apache Spark
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.

## Course 3: Introduction to Deep Learning & Neural Networks with Keras
Looking to start a career in Deep Learning? Look no further.

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.

## Course 4: Deep Neural Networks with PyTorch
The course will teach you how to develop deep learning models using Pytorch.

The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.

## Course 5: Building Deep Learning Models with TensorFlow
The majority of data in the world is unlabeled and unstructured.

Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.

## Course 6: AI Capstone Project with Deep Learning
In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge.

They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.

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