Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.

About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.

Learners will practice and master the fundamentals of algorithms through several types of assessments. Every week, there is a multiple-choice quiz to test your understanding of the most important concepts. There are also weekly programming assignments, where you implement one of the algorithms covered in the lecture in a programming language of your choosing. Each course concludes with a multiple-choice final exam.

This Professional Certificate will equip you with all the key skills and technical know-how to kickstart your career as a Full-Stack Cloud Native Application Developer. Guided by experts at IBM, you will learn to build your own cloud-based applications and practice working with the technologies behind them. This program consists of 10 courses with ample instructional content as well as hands-on exercises and projects designed to hone your skills and help you build your portfolio.

No prior programming experience or Cloud background is required to start this program. You'll skill up with the tools and technologies that successful software developers use to build, deploy, test, run, and manage Full Stack Cloud Native applications, giving you the practical skills to begin a new career in a highly in-demand area.

The courses in this program will help you develop skill sets in a variety of technologies including: Cloud foundations, HTML, CSS, JavaScript, GitHub, Node.js, React, Cloud Native practices, DevOps, CI/CD, Containers, Docker, Kubernetes, OpenShift, Istio, Python programming, Databases, SQL, NoSQL, Django ORM, Bootstrap, Application Security, Microservices, Serverless computing, and more.

After completing all the courses in the program, including the capstone project, you will have developed several applications using front-end and back-end technologies and deployed them on a cloud platform using Cloud Native methodologies.

WHAT YOU WILL LEARN

- Develop with front-end development languages and tools such as HTML, CSS, JavaScript, React and Bootstrap.

- Deploy and scale applications using Cloud Native methodologies and tools like Containers, Kubernetes, Microservices and Serverless Functions.

- Program applications using back-end languages and frameworks like Express, Node.js, Python, Django, etc.

- Build your GitHub portfolio by applying your Full Stack Cloud Development skills with multiple labs and hands-on projects, including a capstone.

Throughout the 10 courses in the Professional Certificate you will develop a portfolio of hands-on projects involving various popular technologies and programming languages in Full Stack Cloud Application Development.

These include publishing HTML pages on Cloud Object Storage; creating an interest rate calculator using HTML, CSS, & JavaScript, an AI program deployed on Cloud Foundry using DevOps principles & CI/CD toolchains with a NoSQL database, a Node.js and React application, a containerized guestbook app packaged with Docker deployed with Kubernetes & managed with OpenShift, a Python app bundled as a package, a RDBMS powered application using Django ORM and Bootstrap, and an app built using Microservices & Serverless; and completing a Capstone Project that leverages several technologies into a single scalable Cloud Native Full Stack application.

You will publish these projects through your GitHub repository to share your skills with your peers and prospective employers.

Graphic design is all around us, in a myriad of forms, both on screen and in print, yet it is always made up of images and words to create a communication goal. This four-course sequence exposes students to the fundamental skills required to make sophisticated graphic design: process, historical context, and communication through image-making and typography. The sequence is completed by a capstone project that applies the skills of each course and peer feedback in a finished branding project suitable for a professional portfolio.

The goal of this specialization is to equip learners with a set of transferable formal and conceptual tools for “making and communicating” in the field of graphic design. This core skill set will equip learners for formal studies in graphic design, and a starting point for further work in interface design, motion graphics, and editorial design.

WHAT YOU WILL LEARN

- Gain the fundamental skills needed to be a graphic designer

- Complete a capstone project to add to your professional portfolio

- Communicate through image-making and typography

- Learn everything you need to know to work in interface design, motion graphics, and editorial design

This Specialization will introduce you to the art of our time. Through original films and audio, you will go behind the scenes to look closely at artworks and into studios to hear directly from artists, designers, curators and others. This Specialization is for anyone who would like to learn more about modern and contemporary art. No prior knowledge is required. Enroll to receive invitations to virtual events, gain exclusive access to MoMA resources, and share ideas with an international learner community.

Start with Modern Art & Ideas to learn how artists have taken inspiration from their environment and responded to social issues over the past 150 years. Next, take a deep dive into Seeing Through Photographs, exploring photography from its origins in the mid-1800s through the present. This course addresses the gap between seeing and truly understanding photographs by introducing ideas, approaches and technologies that inform their making.

Explore artworks made since 1980 in What Is Contemporary Art? Ranging from 3-D–printed glass and fiber sculptures to performances in a factory, the works in this course introduce you to the diverse materials, motivations and methods of artists working today. Complete the Specialization with Fashion as Design, and investigate the choices you make about fashion in relation to expression, sustainability, labor practices, identity and more. Learn from makers working with clothing every day—and, in some cases, reinventing it for the future.

Through original films and audio, you will go behind the scenes to look closely at artworks and into studios to hear directly from artists, designers, curators, and others. Enroll to receive invitations to virtual events, gain exclusive access to MoMA resources, and share ideas with an international learner community.

WHAT YOU WILL LEARN

- Develop a deeper understanding of artists’ and designers’ processes, including modes of experimentation and responses to technological innovation.

- Gain confidence in looking at and talking about modern and contemporary art and design, and find inspiration from art all around you.

- Develop critical thinking and looking skills to understand how artists and designers respond to the social and cultural issues of their time.

- Better comprehend the choices you make about fashion with respect to expression, identity, and issues such as labor practices and sustainability.

TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.

In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate.

Ready to deploy your models to the world? Learn how to go live with your models with the TensorFlow: Data and Deployment Specialization.

Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.

In the DeepLearning.AI TensorFlow Developer Professional Certificate program, you'll get hands-on experience through 16 Python programming assignments. By the end of this program, you will be ready to:

- Build and train neural networks using TensorFlow

- Improve your network’s performance using convolutions as you train it to identify real-world images

- Teach machines to understand, analyze, and respond to human speech with natural language processing systems

- Process text, represent sentences as vectors, and train a model to create original poetry!

Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language.

This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.

By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future.

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

This Specialization will equip you with machine learning basics and state-of-the-art deep learning techniques needed to build cutting-edge NLP systems:

- Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, translate words, and use locality-sensitive hashing to approximate nearest neighbors.

- Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.

- Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions. 

- Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering, and to build chatbots. Learn T5, BERT, transformer, reformer, and more with Transformers!

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.

WHAT YOU WILL LEARN

- Use R to clean, analyze, and visualize data.

- Use GitHub to manage data science projects.

- Navigate the entire data science pipeline from data acquisition to publication.

- Perform regression analysis, least squares and inference using regression models.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles.

The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance.

In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production.

By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems. By the end, you'll be ready to:•

- Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements •

- Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application • Build data pipelines by gathering, cleaning, and validating datasets •

- Implement feature engineering, transformation, and selection with TensorFlow Extended •

- Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas •

- Apply techniques to manage modeling resources and best serve offline/online inference requests •

- Use analytics to address model fairness, explainability issues, and mitigate bottlenecks •

- Deliver deployment pipelines for model serving that require different infrastructures •

- Apply best practices and progressive delivery techniques to maintain a continuously operating production system

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistically, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and network to gain insight into their data.

Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

WHAT YOU WILL LEARN

- Conduct an inferential statistical analysis

- Enhance a data analysis with applied machine learning

- Discern whether a data visualization is good or bad

- Analyze the connectivity of a social network

In this Specialization, you’ll develop basic literacy in the language of business, which you can use to transition to a new career, start or improve your own small business, or apply to business school to continue your education. In five courses, you’ll learn the fundamentals of marketing, accounting, operations, and finance. In the final Capstone Project, you’ll apply the skills learned by developing a go-to-market strategy to address a real business challenge.

WHAT YOU WILL LEARN

- Understand branding and go-to-market strategies

- Manage people through motivation and reward systems

- Read income statements, balance sheets, and cash flow statements

- Analyze and improve business processes in services or manufacturing