IBM 人工智能工程专业证书

你将学到什么

课程概况

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.

包含课程

课程1
使用 Python 进行机器学习

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!

By just putting in a few hours a week for the next few weeks, this is what you’ll get.
1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy
2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.
3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.

If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

课程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.

After completing this course, you will be able to:
- gain a practical understanding of Apache Spark, and apply it to solve machine learning problems involving both small and big data
- understand how parallel code is written, capable of running on thousands of CPUs.
- make use of large scale compute clusters to apply machine learning algorithms on Petabytes of data using Apache SparkML Pipelines.
- eliminate out-of-memory errors generated by traditional machine learning frameworks when data doesn’t fit in a computer's main memory
- test thousands of different ML models in parallel to find the best performing one – a technique used by many successful Kagglers
- (Optional) run SQL statements on very large data sets using Apache SparkSQL and the Apache Spark DataFrame API.

Enrol now to learn the machine learning techniques for working with Big Data that have been successfully applied by companies like Alibaba, Apple, Amazon, Baidu, eBay, IBM, NASA, Samsung, SAP, TripAdvisor, Yahoo!, Zalando and many others.

NOTE: You will practice running machine learning tasks hands-on on an Apache Spark cluster provided by IBM at no charge during the course which you can continue to use afterwards.

Prerequisites:
- basic python programming
- basic machine learning (optional introduction videos are provided in this course as well)
- basic SQL skills for optional content

The following courses are recommended before taking this class (unless you already have the skills)
https://www.coursera.org/learn/python-for-applied-data-science or similar
https://www.coursera.org/learn/machine-learning-with-python or similar
https://www.coursera.org/learn/sql-data-science for optional lectures

课程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.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.

课程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.Learning Outcomes:
After completing this course, learners will be able to:
• explain and apply their knowledge of Deep Neural Networks and related machine learning methods
• know how to use Python libraries such as PyTorch for Deep Learning applications
• build Deep Neural Networks using PyTorch

课程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.Learning Outcomes:
After completing this course, learners will be able to:
• explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.
• describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.
• understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
• apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

课程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.Learning Outcomes:
• determine what kind of deep learning method to use in which situation
• know how to build a deep learning model to solve a real problem
• master the process of creating a deep learning pipeline
• apply knowledge of deep learning to improve models using real data
• demonstrate ability to present and communicate outcomes of deep learning projects

课程项目

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. You will also complete a Capstone Project and demonstrate ability to present and communicate outcomes of deep
learning projects