Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. 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’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.
Course 1: Machine Learning with Python - Offered by IBM. Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to ... Enroll for free.
Course 2: Introduction to Deep Learning & Neural Networks with Keras - Offered by IBM. Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning ... Enroll for free.
Course 3: Introduction to Computer Vision and Image Processing - Offered by IBM. Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such ... Enroll for free.
Course 4: Deep Neural Networks with PyTorch - Offered by IBM. The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors ... Enroll for free.
Course 5: Building Deep Learning Models with TensorFlow - Offered by IBM. The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant ... Enroll for free.
Course 6: AI Capstone Project with Deep Learning - Offered by IBM. In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use ... Enroll for free.
Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.
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.
By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.
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.
- 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
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.
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.
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.
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.
Alex Aklson, Joseph Santarcangelo, Romeo Kienzler and SAEED AGHABOZORGI