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Our Courses
AI for Medicine
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.
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Course by
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Self Paced
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English
Remote Sensing Image Acquisition, Analysis and Applications
Welcome to Remote Sensing Image Acquisition, Analysis and Applications, in which we explore the nature of imaging the earth's surface from space or from airborne vehicles. This course covers the fundamental nature of remote sensing and the platforms and sensor types used. It also provides an in-depth treatment of the computational algorithms employed in image understanding, ranging from the earliest historically important techniques to more recent approaches based on deep learning.
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Course by
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Self Paced
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23 hours
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English
Deep Learning Fundamentals with Keras
New to deep learning? Start with this course, that will not only introduce you to the field of deep learning but give you the opportunity to build your first deep learning model using thepopular Keras library.
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30
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English
Deep Learning with Python and PyTorch
This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.
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72
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English
Deep Learning with Tensorflow
Much of theworld's data is unstructured. Think images, sound, and textual data. Learn how to apply Deep Learning with TensorFlow to this type of data to solve real-world problems.
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English
Applied Deep Learning Capstone Project
In this capstone project, you'll use either Keras or PyTorch to develop, train, and test a Deep Learning model. Load and preprocess data for a real problem, build the model and then validate it.
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Course by
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64
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English
PyTorch and Deep Learning for Decision Makers
Learn how PyTorch, a deep learning framework, can be used to automate and optimize processes through the development and deployment of state-of-the-art AI applications. The course will also help you understand the importance of data quality, how to choose the right model, and the challenges in deploying and maintaining both deep learning and machine learning applications.
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Course by
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15
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English
PyTorch Basics for Machine Learning
This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.
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Course by
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Self Paced
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English
Health Data Science Foundation
This course is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios. We cover deep learning (DL) methods, healthcare data and applications using DL methods.
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Course by
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Self Paced
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24 hours
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English
Transfer Learning for Food Classification
In this hands-on project, we will train a deep learning model to predict the type of food and then fine tune the model to improve its performance.
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Course by
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Self Paced
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3 hours
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English
Machine Learning: Theory and Hands-on Practice with Python
In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems.
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Course by
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Self Paced
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English
IBM Data Analyst
Prepare for a career in the high-growth field of data analytics. In this program, you’ll learn in-demand skills like Python, Excel, and SQL to get job-ready in as little as 4 months. Data analysis is the process of collecting, storing, modeling, and analyzing data that can inform executive decision-making, and the demand for skilled data analysts has never been greater. This program will teach you the foundational data skills employers are seeking for entry-level data analytics roles.
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Course by
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Self Paced
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English
Deep Learning for Real Estate Price Prediction
In this hands-on guided project, we will predict real estate prices with deep learning. In this project, we will predict home sale prices in King County in the U.S. between May, 2014 and May, 2015 using several features such as number of bedrooms, bathrooms, view, and square footage. This guided project is practical and directly applicable to the real estate industry. You can add this project to your portfolio of projects which is essential for your next job interview.
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Course by
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Self Paced
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2 hours
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English
Deep learning in Electronic Health Records - CDSS 2
Overview of the main principles of Deep Learning along with common architectures. Formulate the problem for time-series classification and apply it to vital signals such as ECG. Applying this methods in Electronic Health Records is challenging due to the missing values and the heterogeneity in EHR, which include both continuous, ordinal and categorical variables. Subsequently, explore imputation techniques and different encoding strategies to address these issues. Apply these approaches to formulate clinical prediction benchmarks derived from information available in MIMIC-III database.
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Course by
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Self Paced
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32 hours
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English
AI for Everyone: Master the Basics
Learn what Artificial Intelligence (AI) is by understanding its applications and key concepts including machine learning, deep learning and neural networks.
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Course by
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33
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English
Advanced Deployment Scenarios with TensorFlow
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this final course, you’ll explore four different scenarios you’ll encounter when deploying models. You’ll be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You’ll move on to TensorFlow Hub, a repository of models that you can use for transfer learning.
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Course by
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Self Paced
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13 hours
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English
Deep Learning and Reinforcement Learning
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning.
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Course by
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32 hours
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English
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies.
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Course by
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Self Paced
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English
Machine Learning with Python
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.
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Course by
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Self Paced
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33 hours
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English
Technologies and platforms for Artificial Intelligence
This course will address the hardware technologies for machine and deep learning (from the units of an Internet-of-Things system to a large-scale data centers) and will explore the families of machine and deep learning platforms (libraries and frameworks) for the design and development of smart applications and systems.
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Course by
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Self Paced
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3 hours
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English
Facial Expression Classification Using Residual Neural Nets
In this hands-on project, we will train a deep learning model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect facial expressions.
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Course by
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Self Paced
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3 hours
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English
Optimize TensorFlow Models For Deployment with TensorRT
This is a hands-on, guided project on optimizing your TensorFlow models for inference with NVIDIA's TensorRT. By the end of this 1.5 hour long project, you will be able to optimize Tensorflow models using the TensorFlow integration of NVIDIA's TensorRT (TF-TRT), use TF-TRT to optimize several deep learning models at FP32, FP16, and INT8 precision, and observe how tuning TF-TRT parameters affects performance and inference throughput.
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Self Paced
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3 hours
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English
Applied Data Science
This action-packed Specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. If you’re interested in pursuing a career in data science, and already have foundational skills or have completed the Introduction to Data Science Specialization, this program is for you! This 4-course Specialization will give you the tools you need to analyze data and make data driven business decisions leveraging computer science and statistical analysis.
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Self Paced
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English
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types.
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Course by
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Self Paced
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English
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?
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Course by
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Self Paced
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8 hours
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English