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Online Course: Basic Concepts of Algorithms and Data Structures Duration : - 60 minutes: condensed format to introduce fundamental concepts with targeted exercises. - 90 minutes: extended format to explore concepts in depth, solve complex problems, and include an interactive question-and-answer session. --- General description This course is designed for students and professionals who are new to computer science, as well as those who want to solidify their foundation in algorithms and data structures. It provides a clear and practical introduction to the essential tools for solving problems effectively, by learning how to design and analyze algorithms. Whether you are preparing for an exam, a technical interview, or want to improve your programming skills, this course will guide you in understanding theoretical concepts and their practical application. --- Educational objectives At the end of the course, participants will be able to: 1. Understand the fundamental concepts of algorithms: sorting, searching, time complexity. 2. Master key data structures: arrays, lists, stacks, queues, trees, and graphs. 3. Solve problems by choosing appropriate data structures and algorithms. 4. Analyze and optimize the performance of algorithms. --- Course syllabus 1. Introduction (5-10 min) - Presentation of the objectives and concepts covered. - Importance of algorithms and data structures in computer science. 2. Fundamental concepts (20-30 min) - Definitions and roles of algorithms and data structures. - Temporal and spatial complexity: basic notions (Big O). - Basic data structures: arrays, lists, and dictionaries. 3. Practical application and examples (30-40 min) - Simple sorting implementation (insertion sort, bubble sort). - Search example (linear search, binary search). - Manipulation of stacks and queues through practical exercises. - Bonus for the 90 min format: Exploration of trees and graphs (simple example of a route). 4. Q&A and conclusion (5-10 min) - Review of the concepts covered. - Tips for continuing to practice and progress. - Suggestions for personal projects to apply the knowledge acquired. --- Teaching methodology - Interactive learning: a combination of theoretical explanations and practical applications. - Concrete examples: each concept is illustrated by practical cases and guided exercises. - Adaptation to needs: the courses are adjusted to the level and objectives of each participant, with a focus on the most relevant aspects. --- Target audience This course is aimed at: - Computer science students wishing to strengthen their mastery of the basics before exams or projects. - Programming beginners who want to understand the essential mechanisms behind problem solving. - Professionals preparing for technical interviews or seeking to improve their understanding of algorithms. --- Benefits of this online course - Personalized support from an experienced trainer. - Educational materials and practical exercises accessible after the session. - Flexible hours to fit your schedule. - Progressive approach to facilitate learning, even for beginners. Develop your skills in algorithms and data structures today to efficiently solve the complex problems of tomorrow! ---

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As a tutor, my goal is to create a supportive and engaging learning environment where students feel confident to explore and grow. I tailor my teaching style to suit each student's unique needs. My focus is on building a solid foundation in algorithm and computational concepts and fostering critical thinking, helping students not only understand the material but also develop independent problem-solving skills. For parents, I value open communication and progress updates, ensuring they are informed and involved in their child’s learning journey. My background in mathematics and data science allows me to approach problems from both analytical and practical perspectives, giving students valuable insights into real-world applications of what they learn.

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### Course Description: Prompt Engineering using Python and LLMs Unlock the power of Large Language Models (LLMs) with our beginner-friendly course, "Prompt Engineering using Python and LLMs." This course is designed to introduce you to the fundamentals of prompt engineering, equipping you with the skills needed to craft effective prompts and leverage the capabilities of LLMs for various applications. #### Course Objectives: - **Introduction to Prompt Engineering:** Understand the basics of prompt engineering and its significance in utilizing LLMs. - **Python for Prompt Engineering:** Learn essential Python programming skills tailored for prompt engineering tasks. - **Harnessing LLMs:** Discover how to use LLMs to generate, manipulate, and analyze text based on your crafted prompts. - **Practical Applications:** Apply your knowledge through hands-on projects and real-world scenarios to build practical solutions. #### Course Outline: 1. **Introduction to Prompt Engineering:** - What is prompt engineering? - Importance and applications of prompt engineering - Overview of Large Language Models (LLMs) and their capabilities 2. **Python Essentials for Prompt Engineering:** - Basic Python programming concepts - Key Python libraries for text processing - Setting up your development environment 3. **Crafting Effective Prompts:** - Understanding prompt structure and components - Techniques for creating clear and concise prompts - Examples of effective prompts for various tasks 4. **Interacting with LLMs:** - Introduction to popular LLMs (eg, GPT-3, GPT-4) - Using APIs to interact with LLMs - Generating text responses based on your prompts 5. **Advanced Prompt Techniques:** - Handling complex queries and multi-step instructions - Refining prompts for improved accuracy and relevance - Using prompts for different applications (eg, content generation, data analysis) 6. **Practical Projects:** - Building a text generation application - Creating a chatbot using prompt engineering techniques - Developing a data extraction tool using LLMs 7. **Ethical Considerations and Best Practices:** - Ensuring ethical use of LLMs and prompt engineering - Best practices for responsible AI development - Avoiding common pitfalls and challenges 8. **Future Directions and Further Learning:** - Exploring advanced topics in prompt engineering - Keeping up with the latest advancements in LLMs - Resources for continued learning and development #### Who Should Enroll: - Beginners with no prior experience in prompt engineering or LLMs - Individuals interested in learning Python programming - Aspiring AI enthusiasts looking to explore the potential of LLMs #### Prerequisites: - Basic computer literacy and familiarity with high school-level mathematics - No prior programming or AI experience required #### Race Outcomes: By the end of this course, you will be able to: - Understand the fundamentals of prompt engineering - Write and execute Python code for prompt engineering tasks - Create effective prompts to interact with LLMs - Develop practical applications using LLMs - Apply ethical considerations and best practices in your work Join us in "Prompt Engineering using Python and LLMs" to embark on your journey into the world of AI and prompt engineering. Gain the skills and confidence needed to create impactful solutions and unlock the full potential of Large Language Models.

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