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

An overview of the ideas, methods, and institutions that permit human society to manage risks and foster enterprise. Emphasis on financially-savvy leadership skills. Description of practices today and analysis of prospects for the future. Introduction to risk management and behavioral finance principles to understand the real-world functioning of securities, insurance, and banking industries. The ultimate goal of this course is using such industries effectively and towards a better society.

This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, Delta Lake and more. These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets.

WHAT YOU WILL LEARN

- U​se SQL commands to filter, sort, & summarize data; manipulate strings, dates, & numerical data from different sources for analysis

- U​se the collaborative Databricks workspace and create an end-to-end pipeline that reads data, transforms it, and saves the result

- A​ssess and create datasets to solve your business questions and problems using SQL

- ​Develop a project proposal & select your data, perform statistical analysis & develop metrics, and present your findings & make recommendations

Whether you’ve been tinkering with social media platforms for your business already or are completely new to the field of digital marketing, you’ve come to the right place. This six-course program, developed by digital marketing experts at Aptly together with Meta marketers, includes an industry-relevant curriculum designed to prepare you for an entry-level role in social media marketing.

After an introduction to digital marketing and major social media platforms, you’ll learn to establish an online presence, create posts, build a following, and manage your social media accounts. You’ll develop skills in creating and managing advertising campaigns in social media and learn to evaluate the results of your marketing efforts.

Upon successful completion of the program, you’ll earn both the Coursera and the Meta Digital Marketing Associate Certification, proving your skills in social media marketing and in the use of Meta Ads Manager.

Once you earn your Meta Certification, you’ll get exclusive access to the new Meta Career Programs Job Board—a job search platform that connects Meta Certified professionals with 200+ top employers who have committed to sourcing talent through its certification programs. We’ll provide you with the link once you’ve completed all the courses and passed the exam.

The Professional Certificate is now ACE® recommended. You can learn more here.

This specialization is also offered in Arabic, Portuguese, and Spanish.

WHAT YOU WILL LEARN

- Create and analyze an effective advertising campaign for your target audience 

- Create, edit, and troubleshoot ads in Meta Ads Manager

- Create a creative brief that includes the assets for your paid ad 

- Establish and manage a social media presence

Throughout the program, you’ll get to practice your new skills through hands-on projects. Our projects offer an opportunity to apply social media marketing skills practically. From establishing your business goals and brand to creating and evaluating an ad campaign in Meta Ads Manager, you’ll work directly within social media platforms to create engaging and relevant content, discover the ins and outs of each social media platform, practice analyzing metrics, and more. Your results will include a portfolio you can share with a future employer or use at your own business.

A growing number of exciting, well-paying jobs in today’s security industry do not require a college degree. This 8-course Professional Certificate will give you the technical skills to become job-ready for a Cybersecurity Analyst role. Instructional content and labs will introduce you to concepts including network security, endpoint protection, incident response, threat intelligence, penetration testing, and vulnerability assessment.

Cybersecurity is one of the most in-demand career fields.

- According to CyberSeek, from June 2019  through May 2020, there were 171,000 openings for Information Security Analysts, but only 125,000 workers are currently employed in those positions – an annual talent shortfall of 46,000 workers. On average, cybersecurity roles take 21% longer to fill than other IT jobs.

- The U.S. Bureau of Labor Statistics expects 31% hiring growth for Cybersecurity Analysts between 2019 and 2029, growing much faster than average in other careers.

This program is suitable for learners entering the workforce and professionals switching careers. You should be comfortable working with computers, be willing to develop new technical skills, and enjoy collaborative problem solving and communicating solutions.

By the end of this program, you will have completed a real-world security breach hands-on project and applied concepts through industry tool virtual labs to provide you with the confidence to start a career in cybersecurity.

WHAT YOU WILL LEARN

- Develop knowledge of cybersecurity analyst tools including data protection; endpoint protection; SIEM; and systems and network fundamentals.

- Gain skills for incident responses and forensics with real-world cybersecurity case studies.

- Learn about key compliance and threat intelligence topics important in today’s cybersecurity landscape.

- Get hands-on experience to develop skills via industry-specific and open-source Security tools.

Throughout the program, you will use virtual labs and internet sites that will provide you with practical skills with applicability to real jobs that employers value, including:

- Tools: Wireshark, IBM QRadar, IBM MaaS360, IBM Guardium, IBM Resilient, i2 Enterprise Insight Analysis

- Labs: SecurityLearningAcademy.com

- Libraries: Python

- Projects: Investigate a real-world security breach identifying the attack, vulnerabilities, costs and prevention recommendations.