click to
look inside
look inside
Look inside
Source code
Book Forum
Source code on GitHub
Learn Machine Learning by Making Projects
🎙️ Alexey Grigorev interviewed
🎙️ AI in Action with Alexey Grigorev
🎙️ How to Build an #Impactful #Career in #DataScience
🎙️ Alexey Grigorev - How to become a Kaggle master & Data Science
🎙️ DataCast with Alexey Grigorev
🎙️ AI Game Changers with Alexey Grigorev
🎙️ Chai Time Data Science with Alexey Grigorev
🎙️ Product-oriented Data Science with Alexey Grigorev
🎙️ Getting into machine learning and building a community
🎙️ Extracting Value from Data with Alexey Grigorev
🎙️ Expert hour with Alexey Grigorev ~ Machine Learning Bookcamp
🎙️ Machine Learning: Getting the Skills Needed to Work as a Data Scientist or Machine Learning Engineer with Alexey Grigorev
🎙️ Talk with Alexey Grigorev on ML in Production, DataTalks and Machine Learning Bookcamp
Deploying Machine Learning Models, Part 1: saving models
Manning Early Access Program (MEAP)
Read chapters as they are written, get the finished eBook as soon as it’s ready, and receive the pBook long before it's in bookstores.
FREE
You can see any available part of this book for free.Click the table of contents to start reading.
placing your order...
Don't refresh or navigate away from the page.
- customers also bought these items
- Data Science Bookcamp
- Probabilistic Deep Learning
- Python Workout
- Math and Architectures of Deep Learning
- Machine Learning with TensorFlow, Second Edition
An amazing introduction to learning machine learning by doing projects.
Look inside
The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you’ve learned in previous chapters. By the end of the bookcamp, you’ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.
about the technology
Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that’s exactly what you’ll be doing in Machine Learning Bookcamp.about the book
In Machine Learning Bookcamp you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You’ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you’re done working through these fun and informative projects, you’ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems.what's inside
- Code fundamental ML algorithms from scratch
- Collect and clean data for training models
- Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow
- Apply ML to complex datasets with images and text
- Deploy ML models to a production-ready environment
about the reader
For readers with existing programming skills. No previous machine learning experience required.about the author
Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning.
customers also reading
This book
1°
2°
3°
FREE domestic shipping on orders of three or more print books
This book not only explains how to use the different machine-learning algorithms with real-life examples but also provides very clear explanations on how to measure the quality of the predictors.
The greatest thing about the book - it provides hands-on experience. Step-by-step instructions are easy to follow and plenty of pictures instil a sense of confidence.
A step by step, comprehensive introduction to the world of machine learning using Python
I appreciate the author's approach to how ML can be useful or not. It does not try to sell you on the idea of why you should have ML constantly in use or use outright false claims. The author is very knowledgeable in this space and writes in an easy to understand format.
Machine Learning Bookcamp is a well organized and written guide to developing or refreshing one's understanding of machine learning.
I really liked the book style. I've always liked practical books and this one has plenty of exercises and suggestions for future projects.
A perfect introduction for ML with great practical scenarios.
related titles
related titles
.jpg)
.jpg)
