Coursera - Generative AI for Software Development Skill Certificate

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Free Download Coursera - Generative AI for Software Development Skill Certificate
Released 9/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 75 Lessons ( 6h 18m ) | Size: 4 GB
Leverage AI in your software development workflow. Enhance your skills as a software developer, grow your career, and stay competitive in this fast-paced industry.

What you'll learn
Learn how LLMs work. By knowing how machine learning systems work, you'll be able to use them more effectively to support your work as a developer.
Experiment quickly. Using LLMs can speed up your ability to prototype and test new features, allowing you to quickly iterate and ship your code.
Optimize your code quality. Get to production-ready code faster by working with an LLM to find and fix bugs.
Team up with AI on engineering tasks. Break through roadblocks and with your team by leveraging an LLM's knowledge of development roles and tasks.
Skills you'll gain
Software Engineering
Large Language Models
Software Development
Generative AI
Machine Learning
Generative AI is transforming software development by enhancing and augmenting traditional coding practices.
In this Skill Certificate from DeepLearning.AI, you'll learn from Laurence Moroney, former AI lead at Google, as he guides you through a comprehensive pathway to understand and apply generative AI technologies in real-world software development.
As AI-powered coding tools are set to transform the industry, with Gartner predicting 70% adoption by platform engineering teams by 2027, this program will support you in learning these technologies at all stages of your career. It will show you how to use LLMs to leverage your own experience and knowledge to become a faster, more productive worker. You will be introduced to a new paradigm for software development, where LLMs become invaluable members of your development team by acting as pair coding partners, software testers, security experts, and knowledgeable colleagues.
Go beyond theory and apply your new skills to real-world projects. This hands-on approach ensures you graduate with the practical expertise to leverage generative AI and augment your development workflow.
When you complete the program, you'll earn a Skill Certificate from DeepLearning.AI, demonstrating your aptitude with the latest GenAI skills and tools to help you advance in your career.
Applied Learning Project
Pair-coding with an LLM to efficiently modify data structures for use in production and at big data scales
Work with an LLM as a skilled software tester to identify bugs, create edge case tests, and update code to correct errors
Implement a functioning local database from scratch, and partner with an LLM to think through software design issues and how to optimize for efficient, secure data access.
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4 GB | 00:11:40 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

01 conversation-between-laurence-moroney-and-andrew-ng (75.5 MB)
01 module-introduction (22.68 MB)
02 configuration-driven-development-overview (82.03 MB)
03 choosing-a-configuration-file-format (38.85 MB)
04 json-and-pickle (81.58 MB)
05 using-the-dall-e-api (65.52 MB)
06 implementing-cdd (48.64 MB)
07 serializing-results (97.39 MB)
01 module-introduction (40.55 MB)
02 setting-up-a-simple-database (41.91 MB)
03 design-and-implement-a-database-schema (51.3 MB)
04 implementing-crud-operations (68.3 MB)
05 module-exercise (28.93 MB)
06 advanced-querying (29.68 MB)
07 databaset-optimization (55.82 MB)
08 debugging (89.46 MB)
09 graded-lab-social-network-database (4.09 MB)
01 module-introduction (40.02 MB)
02 gang-of-four-patterns-overview (34.91 MB)
03 singletons (80.39 MB)
04 patterns-advice-from-an-llm (79.88 MB)
05 singleton-for-database-connection-manager (53.44 MB)
06 factory-patterns (63.72 MB)
07 template-method-pattern (54.15 MB)
08 strategy-pattern (32.51 MB)
09 course-conclusion (78.61 MB)
01 conversation-between-laurence-moroney-and-andrew-ng (234.62 MB)
01 what-is-generative-ai (40.01 MB)
02 ai-and-machine-learning (24.41 MB)
03 machine-learning-example (16.82 MB)
04 supervised-learning (20.13 MB)
05 introduction-to-transformers (22.17 MB)
06 key-transformer-concepts (78.31 MB)
01 transformers-and-code (27.3 MB)
02 generating-code-with-chatbots (76.3 MB)
03 iterative-prompting (42.59 MB)
04 giving-the-llm-feedback (56.08 MB)
05 assigning-the-llm-a-role (69.65 MB)
06 leveling-up-with-multiple-roles (17.56 MB)
07 expert-roles-for-specialized-knowledge (55.06 MB)
08 llm-best-practices (44.47 MB)
01 data-structures-in-production (15.28 MB)
02 arrays (32.86 MB)
03 linked-lists (80.96 MB)
04 self-paced-exercise-doubly-linked-lists (33.78 MB)
05 trees (87.67 MB)
06 graphs (63.97 MB)
07 graded-lab-graph-algorithms (24.7 MB)
09 hash-tables-and-maps (108.73 MB)
10 course-conclusion (29.9 MB)
01 conversation-between-laurence-moroney-and-andrew-ng (83.77 MB)
01 module-introduction (35.49 MB)
02 testing-and-debugging-strategies (41.64 MB)
03 exploratory-testing (51.89 MB)
04 functional-testing (66.39 MB)
05 automated-testing (76.17 MB)
06 software-performance-testing (57.82 MB)
07 security-testing (41.91 MB)
08 flask-example-walkthough-optional (55.82 MB)
09 analyzing-code-for-security-vulnerabilities (62.86 MB)
01 module-introduction (30.13 MB)
02 principles-of-good-documentation (68 MB)
03 inline-comments (60.14 MB)
04 documentation-comments (51.51 MB)
05 automated-documentation-tools (48.34 MB)
06 autodocs-with-sphinx-walkthrough-optional (68.43 MB)
06 autodocs-with-sphinx-walkthrough-optional Autodocs with Sphinx walkthrough optional (356.69 KB)
07 documentation-in-other-coding-languages (61.01 MB)
08 documentation-for-life-in-production (32.77 MB)
01 module-introduction (65.89 MB)
02 virtual-environments (41.96 MB)
03 researching-dependencies (52.95 MB)
04 dependency-conflicts (44.82 MB)
05 dependencies-and-security (69.04 MB)
06 dependency-management-and-other-programming-languages (36.25 MB)
07 course-conclusion (40.79 MB)

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