Prompte engneering using python and Large Language Models (LLMs)

From 56 C$ /h

### 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 (e.g., 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 (e.g., 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

#### Course 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.

Extra information

bring your laptop

Location

Online from Canada

About Me

Programming with several programming languages, such as C, JAVA, and Python.
Data scientist: extracting knowledge from structured, semi-structured, and unstructured data.
Teach programming languages and data science.
Five years of experience in teaching.

Education

Ph.D. in Artificial Intelligence Multi-modal from Sidi Mohamed Ben Abdellah University.
Master's degree in Big Data analytics and smart systems, from Sidi Mohamed Ben Abdellah University.
Bachelor's degree in Computer Science and Mathematics from Ibn Zohr University

Experience / Qualifications

Five years of experience in teaching.
Freelancer in several programming projects.

Age

Children (7-12 years old)

Teenagers (13-17 years old)

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Advanced

Duration

60 minutes

The class is taught in

English

Arabic

French

Skills

Python

Computer programming

Availability of a typical week

(GMT -04:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

Teaching the Programming language(JAVA, Python, C, JavaScript)

33 C$ /h

### Course Description: Teaching the Programming Languages (JAVA, Python, C, JavaScript)

Welcome to the comprehensive course on Teaching the Programming Languages: JAVA, Python, C, and JavaScript. This course is designed for aspiring programmers and educators who aim to master the fundamentals and advanced concepts of four of the most popular programming languages in the industry.

#### Course Objectives:
- **Introduction to Programming Concepts:** Understand the core principles of programming, including variables, data types, control structures, functions, and algorithms.
- **Language-Specific Syntax and Features:** Gain proficiency in the syntax and unique features of JAVA, Python, C, and JavaScript.
- **Hands-On Coding Practice:** Apply your knowledge through numerous coding exercises, projects, and real-world scenarios.
- **Debugging and Problem-Solving:** Develop strong debugging and problem-solving skills to efficiently resolve coding issues.
- **Advanced Topics:** Explore advanced topics such as object-oriented programming, web development, data structures, and algorithms.
- **Teaching Methodologies:** Learn effective teaching strategies to impart programming knowledge to others, whether in a classroom setting or online.

#### Course Outline:
1. **Introduction to Programming:**
- Basics of programming and computational thinking
- Overview of the four languages: JAVA, Python, C, and JavaScript

2. **JAVA Programming:**
- Syntax and basic constructs
- Object-oriented programming concepts
- Exception handling and multithreading
- Building GUI applications

3. **Python Programming:**
- Syntax and basic constructs
- Data structures and libraries
- Functional programming and modules
- Web development with Flask/Django

4. **C Programming:**
- Syntax and basic constructs
- Memory management and pointers
- File handling and system programming
- Data structures and algorithm implementation

5. **JavaScript Programming:**
- Syntax and basic constructs
- DOM manipulation and event handling
- Asynchronous programming and AJAX
- Front-end frameworks (React, Angular, or Vue.js)

6. **Integrated Projects:**
- Cross-language projects to solidify understanding
- Real-world applications and problem-solving

7. **Teaching Strategies:**
- Curriculum development and lesson planning
- Interactive and engaging teaching methods
- Assessment and feedback techniques

#### Who Should Enroll:
- Aspiring programmers who want to learn multiple programming languages
- Educators and trainers looking to enhance their teaching skills
- Professionals seeking to expand their coding expertise for career advancement

#### Prerequisites:
- Basic understanding of computer operations
- No prior programming experience required, but familiarity with basic programming concepts is beneficial

#### Course Outcomes:
By the end of this course, you will be able to:
- Write, debug, and optimize code in JAVA, Python, C, and JavaScript
- Develop comprehensive projects using each language
- Effectively teach programming concepts to others
- Apply advanced programming techniques to solve complex problems

Join us in this journey to become proficient in four powerful programming languages and enhance your teaching abilities to inspire the next generation of coders.

AI and data science (the steps to handle a project)

43 C$ /h

Embark on a comprehensive journey through Artificial Intelligence and Data Science with our course, "AI and Data Science: The Steps to Handle a Project." This course is meticulously designed for individuals who aspire to become proficient in managing and executing AI and data science projects from inception to deployment.

#### Course Objectives:
- **Foundational Knowledge:** Understand the core principles of AI and data science, including key concepts, methodologies, and tools.
- **Project Lifecycle Management:** Learn the systematic approach to handling AI and data science projects through each project lifecycle phase.
- **Hands-On Experience:** Gain practical experience through real-world projects and case studies.
- **Advanced Techniques:** Explore advanced techniques and algorithms in AI and data science.
- **Ethical and Responsible AI:** Understand the ethical implications and best practices for responsible AI development and deployment.

#### Course Outline:
1. **Introduction to AI and Data Science:**
- Overview of AI and data science
- Key concepts and terminologies
- Applications and industry use cases

2. **Project Scoping and Planning:**
- Defining the problem statement
- Identifying objectives and success metrics
- Project planning and timeline management

3. **Data Collection and Preprocessing:**
- Data collection methods and sources
- Data cleaning, transformation, and integration
- Exploratory data analysis and visualization

4. **Model Development:**
- Selection of appropriate algorithms and models
- Training, validation, and testing of models
- Hyperparameter tuning and optimization

5. **Model Evaluation and Validation:**
- Evaluation metrics and performance analysis
- Cross-validation techniques
- Model interpretability and explainability

6. **Deployment and Monitoring:**
- Model deployment strategies and tools
- Monitoring and maintaining model performance
- Continuous integration and continuous deployment (CI/CD)

7. **Project Documentation and Presentation:**
- Creating comprehensive project documentation
- Presenting findings and insights to stakeholders
- Effective communication of technical results

8. **Ethics and Best Practices:**
- Ethical considerations in AI and data science
- Ensuring fairness, accountability, and transparency
- Best practices for sustainable and responsible AI

#### Course Outcomes:
By the end of this course, you will be able to:
- Manage and execute AI and data science projects from start to finish
- Collect, preprocess, and analyze data effectively
- Develop, evaluate, and deploy robust AI models
- Communicate insights and results clearly to stakeholders
- Apply ethical and responsible practices in AI development

Join us to master the end-to-end process of handling AI and data science projects and become a proficient practitioner capable of delivering impactful solutions.

4.85

(13)

Hello, I am a doctoral student in electrical engineering and associate professor in engineering sciences, experienced in the field of electrical engineering, I offer support courses in the subjects of engineering sciences (Electronics, automatics, electrical engineering, automation, programming).

Digital electronics
Analog electronic
electromagnetism (propagation of high frequency waves)
Automatic (continuous, sampled)
electrical engineering (transformers, electrical machines, switching power supply)
C / c ++ programming, Assembler, ARM, STM32
renewable energy (wind, PV)
engineering Sciences
RDM
Python,VHDL
PIC Microprocessor and Microcontroller
Signal processing and data acquisition
Engineering Sciences

These courses allow the student to get up to speed and regain confidence in all scientific subjects, just as they prepare him effectively for the Baccalaureate, the Preparatory Classes or various examinations of the engineering classes.

COURSE OBJECTIVES AND PEDAGOGICAL APPROACH

Resumption and deepening of fundamental concepts through exercises with course reminders.

Put the student in a situation of questioning and research.

Respond to individual issues and questions

Exercise training in order to achieve real mastery of the content.

Learn to build theoretical reasoning from observable facts or hypotheses.

Specific preparation for higher education requirements (in-depth content, increase in work capacity, enrichment of scientific background)

This educational approach is effective since it has often led me to interesting results with my students.

Associate professor provides support courses in electrical engineering

Dr.

4.85

(13)

I am Dr Iyer- a tutor with over 18 years of teaching experience as of 2023 and students from across the globe. I teach one-on-one online (over Skype/ Google Hangout and other media) using a pen tablet and the screen-share feature.

I have helped several students in courses like Python Programming, R Programming, Data Science,
Machine learning etc. I can customise the content to domains like business, economics finance and investments as per student requirements.

I have taught students of various age groups - high school (IB/Cambridge/IGCSE/ ICSE,) University (bachelors, masters, doctoral) and working industry professionals.

More than anything, I trust that if I can replace the fear of a subject with love for it, then I would have truly made a difference to the student.

Matías

4.85

(13)

Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning.

This popularity means that Python is constantly evolving. It offers a wide range of tools and libraries, which are free and very varied.

As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly.

Beyond these general principles, there is no magic rule or method. Some approaches work with some students but not with others. Adaptation to individual needs is therefore the main objective of private lessons. So I will do my best to find what motivates and helps my student.

Mathieu

4.85

(13)

With 7 years of experience as a developer in a Factory, I now develop Wordpress websites for large groups.

In the training program, you will learn to:

- Install, configure, and deploy WordPress sites
- Develop custom Wordpress themes and plugins
- Use powerful tools like ACF Pro, Gravity Forms, WPML, AIOS Security, WP rocket...
- Create custom post types and tailor-made taxonomies
- Develop pages using the right methods in HTML, CSS, Javascript & PHP
- Administer and manage a site database
- Contribute to the good natural referencing (SEO) of your site
- Use WP-Cli to speed up the maintenance of your sites
- Use Docker to containerize your local/prod projects and facilitate their management
- Administer your WP sites the right way
- And much more...

For any questions, you can contact me by PM.

See you soon,
Matthew

Adam

4.85

(13)

Learn to code with method and logic
Whether it's to succeed in the NSI (Digital Sciences and Technology) specialization in high school, design personal projects, or prepare for higher scientific studies, mastering code relies on solid algorithmic thinking. I help students understand the structure of programming languages and the logic of data.

Subject areas and languages taught:

Algorithms & Logic: Designing data structures and solving problems.

Programming Languages: Python, C/C++, C# and Java.

Data Management: Analysis and SQL queries / databases.

Basic Web Development: HTML & CSS for creating structured pages.

The goal is to take the student from simply writing code to true autonomy in development.

Adam

4.85

(13)

doctoral student in engineering sciences provides support courses in analog and digital electronics at any DEUG level and engineering schools. having scientific and technical knowledge, three years of experience in the field of teaching, pedagogy and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they are having difficulty. for more info please contact me

Amin

4.85

(13)

Are you a university student, engineer, or professional who needs to actually use data — not just learn theory about it?
This course is built around real problems and real code. We skip the textbook formulas and go straight to applying statistics and data science the way professionals do: with Python (pandas, NumPy, scikit-learn, matplotlib) and R (RStudio).
What we cover, adapted to your level and goals:
- Descriptive and inferential statistics (the ones that actually matter)
- Data cleaning, exploration, and visualization
- Regression, classification, and intro to machine learning
- Time series and forecasting basics
- R for statistical analysis and academic research

Who this is for:
- University students in statistics, economics, engineering, or biology
- Professionals wanting to move into data analysis or data science
- Researchers who need to process and present data properly

I use Python and R professionally as a working engineer — everything I teach comes from real application, not just academic exercises.

Salah

4.85

(13)

Master Python with Personalized Courses

Discover the art of programming with Python courses tailor-made to meet your specific needs. Whether you are a beginner, intermediate or professional, my lessons are suitable for all levels.

Why Choose My Courses?

Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

Practical Experience: Learn by doing with real-world projects that build your understanding and skills.

Ongoing Support: Get unlimited email support for any questions you have between sessions.

As a Python expert, I have a passion for teaching and sharing my knowledge. My goal is to guide you effectively in your learning journey.

Book Your First Lesson:

Start your journey to Python mastery now by booking your first lesson. Whether you aspire to enter the development field or hone your existing skills, these courses are designed for you.

Amine

4.85

(13)

Discover programming lessons suitable for children! With a fun and educational approach, my lessons allow young minds to dive into the fascinating world of programming. Provide your children with an enriching learning opportunity in a fun and stimulating environment.

Baia

4.85

(13)

I teach Python specifically for finance and data applications - the kind used in economics, business analytics, and quantitative programs. This isn't a general "learn to code" course; it's built around real financial data, benchmarking, and the workflows you'll actually use in coursework or early career work.

Topics include:
Python fundamentals through a finance lens (data structures, functions, control flow).
Working with financial data and datasets.
Performance benchmarking and writing efficient code.
Applying concepts from Hilpisch's Python for Finance.

Adam

4.85

(13)

🐍 Python Course – Learn to code and create your projects!

This course is for anyone who wants to:

✅ Learn Python from the beginning
✅ Strengthen their programming skills

📚 On the program:

Variables

Loops

Functions

Data structures

Practical projects for implementation

💡 How does the course work?

Clear explanations to understand the programming logic

Targeted exercises adapted to your level

Concrete projects to create your own applications

🎯 My goal:

Helping you understand the logic behind the code

Progress at your own pace

Create your own projects in Python and gain independence

Youssef

4.85

(13)

This computer science support course is designed for students and learners wishing to strengthen their foundations or improve their level in computer science and programming.
I support participants in a pedagogical and progressive manner, adapting to their level and objectives (university courses, training, practical work, exams, projects).
The goal is to understand, practice and gain autonomy through clear explanations and concrete examples.

Verónica

4.85

(13)

Primary School Teacher. I offer personalized guidance in developing lesson plans and learning activities adapted to current educational regulations. During our sessions, you will learn to structure and write a complete lesson plan, including specific competencies, assessment criteria, core knowledge, methodology, addressing diversity, evaluation, and learning situations. My goal is to help you understand each section so you can independently develop a coherent, rigorous, and high-quality lesson plan.

You will learn to design practical, creative proposals, developing the necessary skills to apply them in the classroom or in competitive examination processes.

Ammar

4.85

(13)

A- TOPICS YOU CAN EXPLORE AND MASTER:
1- PYTHON FOUNDATIONS
• Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming
• Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code
• Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management

2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS
• Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design
• Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use
• Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies
• Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges

3- DATA PREPARATION AND EXPLORATION
• NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data
• Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation
• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

4- MATHEMATICAL FOUNDATIONS
• Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics
• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
• Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms

5- SUPERVISED MACHINE LEARNING
• Linear and polynomial regression, logistic regression, and regularized models
• k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers
• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results

6- UNSUPERVISED LEARNING
• Clustering using k-means, hierarchical clustering, and density-based methods
• Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery
• Method selection, evaluation of data structure, and interpretation of results without predefined labels

7- MODEL EVALUATION AND IMPROVEMENT
• Training, validation, and test sets; cross-validation; hyperparameter optimization
• Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2
• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

8- DEEP LEARNING
• Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent
• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

9- ARTIFICIAL INTELLIGENCE APPLICATIONS
• Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models
• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
• Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications

10- GENERATIVE AI AND LARGE LANGUAGE MODELS
• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
• Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant
• Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation

11- TOOLS AND LIBRARIES
• Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch
• Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project
• Additional libraries may be introduced depending on the selected specialization and dataset

12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
• Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results
• University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications
• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

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B- PERSONALIZED TUTORING: LEARNING HOW TO REASON
Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly.

Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan.

The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach.

A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps.

You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level.

My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.

Mariam

4.85

(13)

This cohort is designed for young people who want to learn in an affordable, flexible, and enjoyable way without having to dedicate a huge amount of time each week or even just extra support.

This beginner-friendly course introduces students to the world of computers and computer science through simple explanation.

Students will learn how computers work, including hardware, software, memory, storage, data, and how a computer processes information. They will then explore how applications are used to create and organize information, with practical experience using tools such as Microsoft Word, PowerPoint, and Excel.

As the course progresses, students will be introduced to important computer science concepts including binary numbers, algorithms, programming, databases, networks, the Internet, and cybersecurity.

By the end of the course, students will have a good foundation in computer science and improved digital skills.