Machine Learning foundation From Data engineering to Model Deployment

About This Course

This training course offers a deep dive into machine learning, using AWS as the primary platform for implementation. It begins with an Introduction to Machine Learning and AWS, covering core concepts and the use of AWS services like SageMaker for ML workflows. The course progresses into Data Preparation and Feature Engineering, teaching participants how to prepare data for optimal model performance, followed by Exploratory Data Analysis, where students learn to analyze and visualize data effectively.

In Building Machine Learning Models, participants will develop models using both supervised and unsupervised techniques. Model Evaluation and Tuning will equip them with the skills to assess model performance and improve accuracy through hyperparameter tuning. The Deploying Machine Learning Models module covers how to operationalize models in production environments using AWS services. The program further explores specialized areas like Natural Language Processing (NLP) in the Working with Text Data module, and computer vision in the Working with Image and Video Data module.

For advanced learners, Advanced Topics in Machine Learning delves into cutting-edge techniques like deep learning and reinforcement learning. Finally, the course wraps up with ML Best Practices and Case Studies, offering insights into real-world applications and common challenges in the machine learning lifecycle.

Prerequisites

This machine learning training course on AWS has a few prerequisites to ensure you get the most out of it. Here's a breakdown:

Essential

Basic Programming Knowledge: You should be comfortable with at least one programming language, ideally Python, as it's widely used in machine learning and AWS services like SageMaker. This includes understanding variables, data types, loops, conditional statements, and functions.

Fundamentals of Statistics and Mathematics: A grasp of basic statistical concepts like mean, standard deviation, and probability distributions is important. You should also be familiar with linear algebra concepts like vectors and matrices.

Familiarity with Cloud Computing: While not mandatory, having a basic understanding of cloud computing concepts and how services are delivered will be beneficial, especially when working with AWS.

Recommended

Prior Machine Learning Exposure: While the course covers fundamentals, some prior exposure to machine learning concepts would be helpful. This could be through online courses, tutorials, or books.

AWS Account: To fully participate in the hands-on labs and projects, you'll need an active AWS account. This will allow you to access and utilize the various AWS services covered in the training.

Beyond Technical Skills

Problem-Solving Mindset: Machine learning involves tackling complex problems and iteratively refining solutions. A strong analytical and problem-solving approach is crucial.

Curiosity and eagerness to learn: The field of machine learning is constantly evolving. A willingness to learn new concepts and explore different techniques is essential for success.

Course Staff

Samir Amri

Mr.Samir is a Cloud arhictecture Solution & Consultant at Firt-HUB IT . He has designed and delivered numerous training sessions on diverse computer science and pedagogy topics, specializing in interactive enrichment development and content creation for mobile apps and data science across various platforms. As a certified Cloud solutions architect and consultant, with expertise on Azure and AWS, and further proficiency in web app development , Mr.Samir is a proven leader in planning, executing, and delivering impactful IT projects and training programs.

Malek Amri

Malek is an aspiring data scientist with a strong foundation in mathematics and computer science, currently working towards a PHD in Computer Science for Data Science at Université Paris Saclay after completing her Master Degree at the same French University and an undergraduate studies at Sorbonne Université and Université Paris Cité. She is gaining valuable practical experience as an apprentice at Groupement Les Mousquetaires, where she applies advanced Data modelisaiton for sales forecasting, develops personalized recommendation systems, and utilizes Cloud services and Generative IA tools for computer vision tasks. Her diverse background also includes experience as an English language instructor and a French language program at Université Catholique de Lyon, showcasing her strong communication skills and commitment to lifelong learning.

Frequently Asked Questions

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About the Course Content and Structure

- What specific machine learning algorithms will be covered? We'll cover algorithms like linear regression, decision trees, SVMs, and more.
- How much time will be dedicated to hands-on labs and projects? Expect a good balance of theory and hands-on labs, with plenty of practical application.
- What are the specific AWS services we will be using, and will we get any hands-on experience with them? You'll work with SageMaker, Glue, QuickSight, Comprehend, Rekognition, and other AWS services.
- Will the course cover any specific industry applications of machine learning? We'll touch upon applications in various industries through case studies.
- What is the format of the course? The course format and schedule are provided in the detailed course description.
- Are there any assessments or exams involved? Assessments may include quizzes, assignments, and/or a final project.

About Prerequisites and Difficulty

- What level of programming experience is truly necessary? Basic Python is sufficient; we'll guide you through the necessary code.
- How much math and statistics knowledge is required? A foundational understanding of math and statistics is helpful.
- Is this course suitable for beginners, or is it aimed at more experienced individuals? The course is suitable for beginners with some technical background.
- What resources are available to help me if I get stuck? Support will be available through instructors, forums, or online resources.

About Career Relevance and Outcomes

- The course aligns with industry needs and can boost your career in machine learning.
- What kind of career opportunities can I expect after completing this course? You'll be prepared for roles like ML Engineer, Data Scientist, or AI Specialist.
- Are there any real-world examples or case studies that demonstrate how the skills learned in this course are applied in industry? Real-world case studies will showcase the practical applications of the skills learned.

1st-HUB-IT: FS-101 Machine Learning foundation From Data engineering to Model Deployment