A full-stack approach in cloud computing and AI encompasses mastering both front-end and back-end development, integrating cloud services, and utilizing AI technologies. This holistic skill set empowers developers to create and deploy comprehensive, scalable applications.
$ 599 [$ 799]
Buy NowDive into the dynamic field of cloud computing and artificial intelligence with our Full Stack for Cloud Computing & AI Professional Course. Designed for undergraduates, this course offers a comprehensive curriculum covering essential skills in both front-end and back-end development, cloud services, and AI technologies. You'll learn to build and deploy scalable applications, manage cloud infrastructure, and integrate intelligent features using machine learning. By the end of the course, you'll be well-equipped to excel in the tech industry and tackle real-world challenges with modern cloud and AI solutions.
Cloud computing delivers IT resources via the internet. It includes architecture (frontend/backend), components (servers, storage, databases), and applications (SaaS, IaaS). Key terms: virtualization, scalability. Types: public, private, hybrid.
Basics of IT and networking, understanding of virtualization, familiarity with operating systems, knowledge of databases, and basic programming skills. Basic grasp of internet and security concepts is also helpful.
Understand cloud computing concepts, architecture, and services. Gain hands-on skills with platforms like AWS/Azure, learn cloud management, security, and prepare for certification exams.
School students interested in technology, computers, and how the internet works will benefit from this course. It's perfect for those curious about cloud computing and considering a tech career in the future.
Cloud computing means using the internet to store and access apps, games, and files instead of keeping everything on a single computer.
-Frontend: The part of cloud computing that users see and interact with, such as apps and websites.
-Backend: The behind-the-scenes part where data and files are stored and managed.
-Servers: Powerful computers that store and manage lots of data.
-Storage: Online space where files are saved.
-Databases: Organized places for keeping and organizing lots of information.
-Networking: How computers share information over the internet.
-Applications
-Keywords
-Types of Cloud Computing
Learn Linux basics: its differences from Unix, architecture (kernel, shell, file system), and how to use commands. Practice essential commands like ls, cd, and cp to manage files and directories.
Basic computer skills, understanding of operating systems, and familiarity with command-line interfaces. No prior Linux knowledge needed, but comfort with general computing is helpful.
Learn Linux basics, including its architecture, differences from Unix, and how to use commands. Gain practical skills with essential commands to manage files, directories, and system resources effectively.
This Linux course is great for students curious about computers and tech. Perfect for those interested in IT or programming. No prior experience needed; just a passion to learn and explore Linux.
-Linux: An open-source operating system used for personal computers, servers, and more.
-Features: Free to use, customizable, and secure.
-Unix: An older, commercial operating system developed in the 1970s.
-Linux: A newer, open-source operating system modeled after Unix.
-Differences
-Kernel: Core part of Linux managing hardware and system resources.
-Shell: User interface for interacting with the system.
-File System: Organization and access of files and directories.
-Utilities: Tools and programs for specific tasks.
-Command: A text instruction used to tell the system to perform a specific task.
-Basic Commands: ls, cd, pwd, cp, mv, rm
Learn Python from basics to advanced topics with hands-on sessions covering variables, data types, loops, functions, OOP, file handling, and more, preparing you for real-world applications.
No prior programming experience is required. Basic computer skills and a familiarity with concepts like variables and logic can be helpful for a smoother learning experience.
Master Python basics, solve problems with loops and functions, understand data structures, apply OOP principles, handle files, build real-world applications, and use key Python libraries.
Introduction to Python, its history, and its significance in the programming world.
Explore Python's features such as simplicity, readability, and versatility.
Instructions on how to install Python and set up the development environment.
Overview of Python applications in web development, data science, automation, and more.
Introduction to variables, variable naming conventions, and types of variables in Python.
Detailed discussion on Python's built-in data types such as integers, floats, and strings.
Overview of Python’s reserved keywords and their significance.
How to use comments in Python code for better readability and documentation.
Explanation of various operators in Python, including arithmetic, comparison, and logical operators.
Understanding and implementing if-else statements and their variations.
Introduction to for and while loops and their use cases.
Manipulating and using string data, including common methods and operations.
How to define functions, pass arguments, and return values.
Introduction to tuples, their properties, and their use cases.
Overview of sets, their properties, and common operations.
Explanation of iterators, how they work, and their applications.
Introduction to OOP concepts such as classes, objects, inheritance, and polymorphism.
Understanding variable scope, including local, global, and nonlocal scopes.
How to use standard and custom modules to organize and reuse code.
Introduction to constructors and their role in object creation and initialization.
Techniques for handling files, including opening, reading, writing, and closing files.
How to handle errors and exceptions using try, except, finally, and raise.
Using list comprehensions for concise and efficient list manipulation.
Introduction to dictionaries, their operations, and use cases.
Understanding lambda functions and their use in creating small, anonymous functions.
Introduction to regular expressions for pattern matching and text processing.
Handling and manipulating date and time data using the datetime module.
Basics of web scraping using libraries like BeautifulSoup and requests.
Using libraries like Matplotlib and Seaborn to create visualizations.
Applying learned concepts to a final project and reviewing key topics covered in the course.
Explore AI fundamentals, machine learning basics, deep learning concepts, and natural language processing. Learn data preparation, model building, and real-world applications in a concise training.
Basic computer skills and familiarity with programming concepts are recommended. No prior experience in AI or ML is required, but a logical mindset will be helpful for understanding concepts.
Understand AI and ML fundamentals, explore deep learning and neural networks, grasp NLP basics, learn data handling, and apply knowledge through practical projects and real-world applications.
This course is ideal for beginners interested in AI, ML, deep learning, and NLP, including students, professionals, and enthusiasts looking to build a strong foundation in these technologies.
Introduction to artificial intelligence, machine learning, their history, and their impact on various fields.
Overview of machine learning types (supervised, unsupervised, reinforcement learning), and introduction to basic algorithms.
Techniques for collecting and preparing data, and an overview of ML models like linear regression and decision trees.
Basics of deep learning, neural networks, and key components such as layers and activation functions.
Introduction to NLP, its significance, and basic tasks such as text classification and sentiment analysis.
Applying learned concepts to a simple project, integrating AI, ML, deep learning, and NLP techniques.
Introduction to various AI tools and platforms, including their roles and applications in AI development. Overview of tools like ChatGPT, Notion AI, Chatbot and Grammarly
Introduction to ChatGPT, an AI language model developed by OpenAI. Learn about its capabilities, how it generates text, and practical applications for conversational AI and content creation.
Using Notion for AI-Powered Productivity
Introduction to chatbot platforms and their AI functionalities. Learn the basics of designing, building, and deploying chatbots using tools like Dialogflow or Microsoft Bot Framework.
Overview of Grammarly, an AI-powered writing assistant. Discover how it helps improve grammar, spelling, and style in writing, and how to integrate it into your workflow for better communication.
