Zero Knowledge Beginner's Guide to Starting Machine Learning Journey

Original Text:

Hello, I am a student who's going to start learning about . My current knowledge on the topic is almost zero. Can you recomm any good learning resources such as books, online courses, and tutorials that might help me get started?

Answered Text:

Greetings! As an aspiring learner in the domn of , your excitement and eagerness are commable. Given your current state with virtually no foundational knowledge about this field, I am delighted to guide you towards some meticulously chosen resources that can lay a strong groundwork for your learning journey.

Here's what you might find helpful:

Books:

1. Pattern Recognition and by Christopher M. Bishop: This book offers an excellent introduction to the field of from a statistical perspective, making it suitable even for beginners with its comprehensive coverage.

2. Hands-On with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron: This book is particularly appealing due to its practical approach, focusing on real-world applications using popular libraries like Scikit-Learn, Keras, and TensorFlow.

Online Courses:

1. Coursera's Course by Andrew Ng Stanford University: Taught by a renowned expert in the field, this course is ideal for beginners as it covers the fundamentals of with real-world examples.

2. edX's Introduction to Data Science using Python: This course not only introduces you to data science but also equips you with skills essential for understanding and implementing .

Tutorials:

• Kaggle: Participating in competitions or exploring kernels on Kaggle can provide practical insights into applying techniques.

• YouTube Channels: Subscribing to channels like Sentdex, 3Blue1Brown, and StatQuest with Josh Starmer for video tutorials that expln complex concepts in an intuitive way.

These resources are designed not only to impart theoretical knowledge but also to help you develop hands-on skills. They cater to a variety of learning styles, ensuring that everyone can find something that suits their needs. Whether you're looking to self-study or seek guidance through structured courses, these materials will be your reliable companions on this exciting path into the world of .

Embark on this journey with confidence and curiosity! If you have any specific questions as you progress, feel free to ask. Good luck!

Answered Text in English:

Hello there! As an eager student about to embark on a fascinating exploration of , your enthusiasm is truly commable. With virtually no prior knowledge on this subject, I'm thrilled to guide you toward carefully selected resources that will form the solid foundation for your educational voyage.

Here are some recommations to consider:

Books:

1. Pattern Recognition and by Christopher M. Bishop: This comprehensive book introduces from a statistical standpoint, making it accessible even to beginners with its broad coverage of the field.

2. Hands-On with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron: Known for its practical approach, this book focuses on real-world applications using popular libraries like Scikit-Learn, Keras, and TensorFlow.

Online Courses:

1. Coursera's Course by Andrew Ng Stanford University: Led by a leading expert in the field, this course is perfect for beginners as it covers fundamental concepts of with practical examples.

2. edX's Introduction to Data Science using Python: This course not only introduces you to data science but also equips you with essential skills needed for understanding and implementing .

Tutorials:

• Kaggle: Engaging in competitions or exploring kernels on Kaggle can provide hands-on experience applying techniques.

• YouTube Channels: Subscribing to channels like Sentdex, 3Blue1Brown, and StatQuest with Josh Starmer offers video tutorials that elucidate complex concepts in an intuitive manner.

These resources are designed not only to impart theoretical knowledge but also to enhance your practical skills. They cater to diverse learning styles, ensuring that every learner finds a suitable companion on their journey into the world of .

Embark on this exciting path with confidence and curiosity! Should you have any specific questions as you progress, feel free to reach out. Best wishes for success in your eavors!
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