Machine Learning
About this Course
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
Python for Everybody
About this Specialization
This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.
Programming for Everybody (Getting Started with Python)
About this Course
This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.
Deep Learning
About this Specialization
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
Neural Networks and Deep Learning
About this Course
If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.
In this course, you will learn the foundations of deep learning. When you finish this class, you will:
- Understand the major technology trends driving Deep Learning
- Be able to build, train and apply fully connected deep neural networks
- Know how to implement efficient (vectorized) neural networks
- Understand the key parameters in a neural network's architecture
This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.
This is the first course of the Deep Learning Specialization.
Learning How to Learn: Powerful mental tools to help you master tough subjects
About this Course
This course gives you easy access to the invaluable learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. We’ll learn about the how the brain uses two very different learning modes and how it encapsulates (“chunks”) information. We’ll also cover illusions of learning, memory techniques, dealing with procrastination, and best practices shown by research to be most effective in helping you master tough subjects.
Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give you ideas for: turbocharging successful learning, including counter-intuitive test-taking tips and insights that will help you make the best use of your time on homework and problem sets. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide.
This course can be taken independent of, concurrent with, or prior to, its companion course, Mindshift. (Learning How to Learn is more learning focused, and Mindshift is more career focused.)
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
About this Course
This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.
After 3 weeks, you will:
- Understand industry best-practices for building deep learning applications.
- Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
- Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
- Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
- Be able to implement a neural network in TensorFlow.
This is the second course of the Deep Learning Specialization.
Python Data Structures
About this Course
This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3.
IBM Data Science
WHAT YOU WILL LEARN
- Create and access a database instance on cloud
- Write basic SQL statements: CREATE, DROP, SELECT, INSERT, UPDATE, DELETE
- Filter, sort, group results, use built-in functions, access multiple tables
- Access databases from Jupyter using Python and work with real world datasets
About this Professional Certificate
Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning.
This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets.
It is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning. No prior computer science or programming knowledge is necessary. We start small, re-enforce applied learning, and build up to more complex topics.
Upon successfully completing these courses you will have done several hands-on assignments and built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in Data Science. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Data Science.
Structuring Machine Learning Projects
About this Course
You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how.
Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.
After 2 weeks, you will:
- Understand how to diagnose errors in a machine learning system, and
- Be able to prioritize the most promising directions for reducing error
- Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
- Know how to apply end-to-end learning, transfer learning, and multi-task learning
I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time.
This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.
Data Science
Launch Your Career in Data Science. A ten-course introduction to data science, developed and taught by leading professors.
WHAT YOU WILL LEARN
- Use R to clean, analyze, and visualize data.
- Navigate the entire data science pipeline from data acquisition to publication.
- Use GitHub to manage data science projects.
- Perform regression analysis, least squares and inference using regression models.
About this Specialization
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Data Science: Foundations using R
WHAT YOU WILL LEARN
- Use R to clean, analyze, and visualize data.
- Learn how to ask the right questions, obtain data, and perform reproducible research.
- Use GitHub to manage data science projects.
About this Specialization
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research. Learners who complete this specialization will be prepared to take the Data Science: Statistics and Machine Learning specialization, in which they build a data product using real-world data.
The five courses in this specialization are the very same courses that make up the first half of the Data Science Specialization. This specialization is presented for learners who want to start and complete the foundational part of the curriculum first, before moving onto the more advanced topics in Data Science: Statistics and Machine Learning.
Introduction to Data Science
Launch your career in Data Science. Data Science skills to prepare for a career or further advanced learning in Data Science.
WHAT YOU WILL LEARN
- Create and access a database instance on cloud
- Write basic SQL statements: CREATE, DROP, SELECT, INSERT, UPDATE, DELETE
- Filter, sort, group results, use built-in functions, access multiple tables
- Access databases from Jupyter using Python and work with real world datasets
About this Specialization
In this Specialization learners will develop foundational Data Science skills to prepare them for a career or further learning that involves more advanced topics in Data Science. The specialization entails understanding what is Data Science and the various kinds of activities that a Data Scientist performs. It will familiarize learners with various open source tools, like Jupyter notebooks, used by Data Scientists. It will teach you about methodology involved in tackling data science problems. The specialization also provides knowledge of relational database concepts and the use of SQL to query databases. Learners will complete hands-on labs and projects to apply their newly acquired skills and knowledge.
Upon receiving the certificate for completion of the specialization, you will also receive an IBM Badge as a Specialist in Data Science Foundations.
Convolutional Neural Networks
About this Course
This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.
You will:
- Understand how to build a convolutional neural network, including recent variations such as residual networks.
- Know how to apply convolutional networks to visual detection and recognition tasks.
- Know to use neural style transfer to generate art.
- Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.
This is the fourth course of the Deep Learning Specialization.
Cloud Architecture with Google Cloud
About this Professional Certificate
The Google Cloud Professional Cloud Architect certification was ranked one of the top paying IT certifications of 2019 by Global Knowledge.
This program provides the skills you need to advance your career as a professional cloud architect and recommends training to support your preparation for the industry-recognized Google Cloud Professional Cloud Architect certification.
You'll have the opportunity to deploy solution elements, including infrastructure components such as networks, systems and applications services, and you'll gain real world experience through a number of hands-on Qwiklabs projects.
Upon successful completion of this program, you will earn a certificate of completion to share with your professional network and potential employers.
If you would like to become Google Cloud certified and demonstrate your proficiency in the understanding of cloud architecture and Google Cloud Platform, design, develop, and manage solutions to drive business objectives, you will need to register for, and pass the official Google Cloud certification exam. You can find more details on how to register and additional resources to support your preparation at cloud.google.com/certification.
Architecting with Google Compute Engine
Launch your career in Cloud Architecture. Design, develop, and manage cloud solutions to drive business objectives.
About this Specialization
This specialization introduces learners to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform, with a focus on Compute Engine. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.
This class is intended for Cloud Solutions Architects, DevOps Engineers or individuals using Compute Engine.
Note: This Specialization used to be called Architecting with Google Cloud Platform. As the Specialization is now focused on Compute Engine as the computing platform, we have renamed it to Architecting with Google Compute Engine. If you are looking to learn more about App Engine, Cloud Functions and Google Kubernetes Engine, which are services previously covered in the Elastic Cloud Infrastructure: Containers and Services course of this specialization, please refer to the Developing Applications with Google Cloud Platform Specialization and the Architecting with Kubernetes Engine Specialization.
Cloud Engineering with Google Cloud
WHAT YOU WILL LEARN
- Learn the skills needed to be successful in a cloud engineering role
- Prepare for the Associate Cloud Engineer certification
- Learn about the infrastructure and platform services provided by Google Cloud Platform
- Understand the purpose and intent of the Associate Cloud Engineer certification and its relationship to other Google Cloud certifications.
About this Professional Certificate
This program provides the skills you need to advance your career as a cloud engineer and recommends training to support your preparation for the industry-recognized Google Cloud Associate Cloud Engineer.
You'll also have the opportunity to practice key job skills, such as setting up a cloud environment and configuring and deploying a solution in the cloud.
Upon successful completion of this program, you will earn a certificate of completion to share with your professional network and potential employers.
If you would like to become Google Cloud certified and demonstrate your proficiency in using the Google Cloud Console and the command-line interface to perform common platform-based tasks to maintain one or more deployed solutions that leverage Google-managed or self-managed services on Google Cloud, you will need to register for, and pass the official Google Cloud certification exam. You can find more details on how to register and additional resources to support your preparation at cloud.google.com/certification.
Google IT Support
The launchpad to a career in IT. This program is designed to take beginner learners to job readiness in under six months.
About this Professional Certificate
This 5-course certificate, developed by Google, includes innovative curriculum designed to prepare you for an entry-level role in IT support. A job in IT can mean in-person or remote help desk work in a small business or at a global company like Google. The program is part of Grow with Google, a Google initiative to help create economic opportunities for all Americans. Learn more.
Upon completion of the certificate, you can share your information with top employers like Cognizant, GE Digital, Hulu, Infosys, Intel, KForce, MCPc, PNC Bank, RICOH USA, Sprint, TEKSystems, Veterans United Home Loans, Walmart and their subsidiaries, and of course, Google. You can also earn a CompTIA and Google dual credential when you complete the Google certificate and pass the CompTIA A+ certification exams.
Through a mix of video lectures, quizzes, and hands-on labs and widgets, the program will introduce you to troubleshooting, customer service, networking, operating systems, system administration and security. You’ll hear from Googlers with unique backgrounds whose own foundation in IT support served as a jumping off point for their careers. By dedicating ~5 hours a week, you can complete in under six months.
Completing the program can earn you up to 12 college credits, the equivalent of 4 associate degree-level courses. Learn more in the FAQs below.
If you’re interested in building on your IT foundations, check out the Google IT Automation with Python Professional Certificate.
What is Data Science?
About this Course
The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.
This course is part of multiple programs
This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:
- Introduction to Data Science Specialization
- IBM Data Science Professional Certificate
Developing Applications with Google Cloud Platform
Design, Develop, and Deploy Apps on GCP. Build secure, scalable, and intelligent cloud-native applications.
WHAT YOU WILL LEARN
- Identify the purpose and value of Google Cloud Platform products and services
- Describe best practices for cloud-native application development
- Implement federated identity management using Firebase authentication
- Deploy applications using Container Builder, Container Registry, and Deployment Manager
About this Specialization
In this specialization, application developers learn how to design, develop, and deploy applications that seamlessly integrate managed services from the Google Cloud Platform (GCP). Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications. Learners can choose to complete labs in their favorite language: Node.js, Java, or Python.
This class is intended for application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform.
This course teaches participants the following skills:
• Use best practices for application development.
• Choose the appropriate data storage option for application data.
• Implement federated identity management.
• Develop loosely coupled application components or microservices.
• Integrate application components and data sources.
• Debug, trace, and monitor applications.
• Perform repeatable deployments with containers and deployment services.
• Choose the appropriate application runtime environment; use Google Kubernetes Engine as a runtime environment and later switch to a no-ops solution with Google App Engine flexible environment.
Architecting with Google Kubernetes Engine
About this Specialization
The Architecting with Google Kubernetes Engine specialization will teach you how to implement solutions using Google Kubernetes Engine, or GKE, including building, scheduling, load balancing, and monitoring workloads, as well as providing for discovery of services, managing role-based access control and security, and providing persistent storage to these applications.
Networking in Google Cloud Platform
Launch your career in Cloud Networking. Design, develop, and manage cloud networking solutions to drive business objectives.
WHAT YOU WILL LEARN
- Configure Google VPC networks, subnets, and routers
- Control administrative access to VPC objects
- Interconnect networks among GCP projects
- Choose among GCP load balancer and proxy options and configure them
About this Specialization
This specialization gives participants broad study of core infrastructure and networking options on Google Cloud Platform. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy GCP networking technologies, such as Google Virtual Private Cloud (VPC) networks, subnets, firewalls; interconnection among networks; load balancing; Cloud DNS; Cloud CDN. The specialization will also cover common network design patterns and automated deployment using Deployment Manager.
This class is intended for the following participants:
● Network Engineers and Admins who are either using Google Cloud Platform or are planning to do so
● Individuals who want to be exposed to software-defined networking solutions in the cloud.
To get the most out of this course, participants should have:
*Prior understanding of the OSI 7-layer model
*Prior understanding of IPv4 addressing
*Prior experience with managing IPv4 routes
Security in Google Cloud Platform
Launch your career in Cloud Security. This self-paced Specialization gives a broad study of security controls and techniques on GCP.
WHAT YOU WILL LEARN
- Understand the Google approach to security.
- Manage administrative identities using Cloud Identity and Implement IP traffic controls using VPC firewalls and Cloud Armor.
- Implement least privilege administrative access using Google Cloud Resource Manager, Cloud IAM.
- Remediate important types of vulnerabilities, especially public access to data and VMs.
About this Specialization
Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure GCP solution, including Cloud Identity, the GCP Resource Manager, Cloud IAM, Google Virtual Private Cloud firewalls, Google Cloud Load balancing, Cloud CDN, Cloud Storage access control technologies, Stackdriver, Security Keys, Customer-Supplied Encryption Keys, the Google Data Loss Prevention API, and Cloud Armor. Participants learn mitigations for attacks at many points in a GCP-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use.
Using Python to Access Web Data
About this Course
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
Google Cloud Platform Fundamentals: Core Infrastructure
About this Course
This course introduces you to important concepts and terminology for working with Google Cloud Platform (GCP). You learn about, and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, and BigQuery. You learn about important resource and policy management tools, such as the Google Cloud Resource Manager hierarchy and Google Cloud Identity and Access Management. Hands-on labs give you foundational skills for working with GCP.
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