End-to-End Machine Learning: From Idea to Implementation
What you'll learn
- How To Efficiently Build Sustainable And Scalable Machine Learning Projects Using The Best Practices
- Data Versioning
- Distributed Data Processing
- Feature Extraction
- Distributed Model Training
- Model Evaluation
- Experiment Tracking
- Error analysis
- Model Inference
- Creating An Application Using The Model We Train
- Metadata management
- Reproducibility
- MLOps
- MLOps principals
- Machine Learning Operations
- Machine Learning
- Deep Learning
- Artificial Intelligence
- AI
Requirements
- Basic Understanding Of Machine Learning
- Python Programming Language
- You Will Learn The Rest In The Course
Description
Embark on a hands-on journey to mastering Machine Learning project development with Python and MLOps. This course is meticulously crafted to equip you with the essential skills required to build, manage, and deploy real-world Machine Learning projects.
With a focus on practical application, you'll dive into the core of MLOps (Machine Learning Operations) to understand how to streamline the lifecycle of Machine Learning projects from ideation to deployment. Discover the power of Python as the driving force behind the efficient management and operationalization of Machine Learning models.
Engage with a comprehensive curriculum that covers data versioning, distributed data processing, feature extraction, model training, evaluation, and much more. The course also introduces you to essential MLOps tools and practices that ensure the sustainability and scalability of Machine Learning projects.
Work on a capstone project that encapsulates all the crucial elements learned throughout the course, providing you with a tangible showcase of your newfound skills. Receive constructive feedback and guidance from an experienced instructor dedicated to helping you succeed.
Join a vibrant community of like-minded learners and professionals through our interactive platform, and kickstart a rewarding journey into the dynamic world of Machine Learning projects powered by Python and MLOps. By the end of this course, you'll have a solid foundation, practical skills, and a powerful project in your portfolio that demonstrates your capability to lead Machine Learning projects to success.
Enroll today and take a significant step towards becoming proficient in developing and deploying Machine Learning projects using Python and MLOps. Your adventure into the practical world of Machine Learning awaits!
Who this course is for:
- Students who are interested in pursuing a career in machine learning project development and want to gain expertise in sustainable and scalable development practices
- Machine learning engineers who are interested in developing machine learning solutions that are scalable and sustainable in the long run
- Data scientists who are looking to expand their skill set to include machine learning project development that is scalable and sustainable
- Researchers who are interested in developing machine learning models more efficiently
- Software developers who want to gain expertise in developing sustainable and scalable machine learning projects
- Start-up founders who want to develop machine learning projects that can be scaled up to meet future demands while also being sustainable
- Technical project managers who want to learn how to effectively manage and oversee sustainable and scalable machine learning projects
- Professionals in the technology industry who want to stay up-to-date with the latest trends and advancements in machine learning project development
- Companies and organizations that want to implement sustainable and scalable machine learning projects to improve their operations, efficiency, and profitability
Instructor
- 4.7 Instructor Rating
- 335 Reviews
- 7,919 Students
- 1 Course
I have always been interested in machines and software. One of the earliest memories I can remember is me building imaginary machines with big building blocks in the kindergarten I was attending.
My interest grew with me. This is the reason why I choose to study Electrical and Electronics Engineering in my Bachalor's degree. Later on, I focused mainly on Computer Vision applications during my Master's degree. During my studies I published 4 papers and graduated with a very good GPA.
Right after my studies, I started to work as a Machine Learning Engineer. Since then, I have worked on many different Computer Vision, NLP, and audio processing applications which have been used by many people.
I have experience both in academia as a Machine Learning Researcher, and in industry as a Machine Learning Engineer. Therefore I know how to combine theory and practice in a well suited way. One of the biggest issues I had in my studies was that most of the instructors just didn't care enough about the lectures, and they didn't care about whether or not the students were getting what they were talking about. I suffered from that a lot, and my intention is to make my courses as clear and detailed as possible, and my goal is to make everyone truly understand the content of my lectures.