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Throughout the IBM Certified Professional Artificial Intelligence Engineering program, you will build a portfolio of projects that demonstrate your mastery of course topics. Hands-on projects will give you a working knowledge of machine learning libraries and deep learning frameworks like SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow.
You will also complete an in-depth capstone project, where you will apply your artificial intelligence and neural network skills to a real-world challenge and demonstrate your ability to communicate project results.
Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies in all industries use data to make decisions.
To stay competitive, organizations need skilled AI engineers using cutting-edge methods like machine learning algorithms and deep learning neural networks to deliver data. Actionable intelligence driven for your business. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.
You will master the fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages such as Python.
You will apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems related to object recognition, computer vision, image and video processing, text analysis, natural language processing ( NLP), recommender systems. and other types of classifiers.
Through hands-on projects, you'll gain essential data science skills to scale machine learning algorithms on big data with Apache Spark. You will build, train, and implement different types of deep architectures, including convolutional neural networks, recurrent networks, and automatic encoders.
In addition to earning a Coursera Professional Certificate, you'll also receive a digital badge from IBM that recognizes your AI engineering proficiency.
Part of the IBM Artificial Intelligence Engineering Professional Certificate, this course dives into the basics of machine learning using a familiar and accessible programming language, Python. In this course, we'll review two main components: First, you'll learn about the purpose of machine learning and where it applies to the real world. Second, you'll get an overview of machine learning topics such as supervised versus unsupervised learning, model evaluation, and machine learning algorithms.
In this course, you'll 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, here's what you'll get.
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.
This course, part of the IBM Professional Certificate in Artificial Intelligence Engineering, will give you the skills to scale data science and machine learning (ML) tasks on Big Data ensembles 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:
NOTE: You will practice running machine learning tasks hands-on on an IBM-provided Apache Spark cluster free of charge during the course, which you can continue to use afterwards. Prerequisites: - Basic Python Programming - Basic Machine Learning (optional introductory videos are also provided in this course) - 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
Are you 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 today, such as 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 create your first deep learning model using the Keras library.
After completing this course, students will be able to:
The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and the automatic differencing package. Then each section will cover different models starting with the fundamentals like linear regression and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of the different functions of activation, normalization and dropout layers. Then, convolutional neural networks and transfer learning will be discussed. Finally, several other deep learning methods will be covered.
Learning Outcomes: After completing this course, learners will be able to:
Most of the world's data is unlabeled and unstructured. Shallow neural networks cannot easily capture the relevant structure in, for example, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course, you will use the TensorFlow library to apply deep learning to different types of data to solve real-world problems.
Learning Outcomes: After completing this course, learners will be able to:
In this capstone, students will apply their knowledge and deep learning experience 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 preprocess data for a real problem, build the model and validate it. Students will then present a project report to demonstrate the validity of their model and their competence in the field of deep learning.
Learning Outcomes:
With a Coursera Professional Certificate you could start a new career or change your current career. Today's world is demanding skills and knowledge in technologies that more and more companies need. Learn at your own pace, when and where you want. In a period of less than a year you could acquire a professional certificate, which added to your current profession, could give you substantial added value to your work experience and attract the attention of "head hunters".
The most prestigious universities and the most relevant companies in the world offer professional certificates in alliance with Coursera
Professional certificates are the best way to receive top quality education in the world, at unique values.
Each study offers certification options highly valued by recruiters, or prepare you for official industry certification.
Professional certificates are in-depth studies in high-demand technology skills that you can earn in about 6-10 months.
Coursera offers courses from over 200 leading universities and companies to deliver online learning around the world. With a Coursera Plus subscription, you get unlimited access to over 90% of all courses, and the most popular professional certificates and specializations on Coursera.
Data science, business and personal development. You can enroll in multiple courses at once, earn unlimited certificates, and learn in-demand job skills to start, grow, and even change careers.
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Ph.D., Sr. Data Scientist
IBM Developer Skills Network
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IBM offers a wide range of consulting and technology services; a broad portfolio of middleware for collaboration, predictive analytics, software development, and systems management; and the most advanced servers and supercomputers in the world. Using its R&D, technology and business consulting expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM spends more than $6 billion a year on R&D, and just completed its 5st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 9 Nobel Laureates, 5 US National Medals of Technology, 6 US National Medals of Science, 10 Turing Awards, and XNUMX Fellows of the US Inventors Hall of Fame
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