جديد في تعلم الآلة، ولكن لديهم خلفية برمجية متوسطة
يهدف هذا المحتوى إلى توجيه المطورين الجدد إلى تعلم الآلة خلال المراحل الأولى من رحلة تعلم الآلة الخاصة بهم. سترى أن العديد من الموارد تستخدم TensorFlow، ومع ذلك، فإن المعرفة قابلة للتحويل إلى أطر تعلم الآلة الأخرى.
الخطوة 1: فهم ما هو كل شيء عن ML
تم تصميم TensorFlow 2.0 لتسهيل بناء الشبكات العصبية للتعلم الآلي، ولهذا السبب يستخدم TensorFlow 2.0 واجهة برمجة تطبيقات تسمى Keras. يُعد كتاب التعلم العميق باستخدام بايثون من تأليف فرانسوا تشوليه، مبتكر Keras، مكانًا رائعًا للبدء. اقرأ الفصول من 1 إلى 4 لفهم أساسيات تعلم الآلة من وجهة نظر المبرمج. يتعمق النصف الثاني من الكتاب في مجالات مثل رؤية الكمبيوتر ومعالجة اللغات الطبيعية والتعلم العميق التوليدي والمزيد. لا تقلق إذا كانت هذه المواضيع متقدمة جدًا في الوقت الحالي لأنها ستصبح أكثر منطقية في الوقت المناسب.
يوفر هذا الكتاب التمهيدي نهجًا يعتمد على الكود أولاً لمعرفة كيفية تنفيذ سيناريوهات التعلم الآلي الأكثر شيوعًا، مثل رؤية الكمبيوتر، ومعالجة اللغة الطبيعية (NLP)، ونمذجة التسلسل للويب، والهاتف المحمول، والسحابة، وأوقات التشغيل المضمنة.
احصل على تخصص مطور TensorFlow ، والذي يأخذك إلى ما هو أبعد من الأساسيات إلى رؤية الكمبيوتر التمهيدية والبرمجة اللغوية العصبية ونمذجة التسلسل.
يؤدي إكمال هذه الخطوة إلى استمرار المقدمة، ويعلمك كيفية استخدام TensorFlow لإنشاء نماذج أساسية لمجموعة متنوعة من السيناريوهات، بما في ذلك تصنيف الصور، وفهم المشاعر في النص، والخوارزميات التوليدية، والمزيد.
في هذا التخصص المكون من أربع دورات والذي يدرسه أحد مطوري TensorFlow، ستستكشف الأدوات ومطوري البرامج الذين يستخدمونها لإنشاء خوارزميات قابلة للتطوير مدعومة بالذكاء الاصطناعي في TensorFlow.
باستخدام أمثلة ملموسة، والحد الأدنى من النظرية، وإطاري عمل Python الجاهزين للإنتاج - Scikit-Learn و TensorFlow - يساعدك هذا الكتاب على اكتساب فهم بديهي للمفاهيم والأدوات اللازمة لبناء أنظمة ذكية.
[null,null,[],[],[],null,["# Basics of machine learning\n\n[TensorFlow](/tutorials) › [Resources](/resources/models-datasets) › [Learn ML](/resources/learn-ml) › [Guide](/resources/learn-ml/basics-of-machine-learning) › \n\nBasics of machine learning with TensorFlow\n==========================================\n\nThis curriculum is for people who are:\n\n- New to ML, but who have an intermediate programming background \nThis content is intended to guide developers new to ML through the beginning stages of their ML journey. You will see that many of the resources use TensorFlow, however, the knowledge is transferable to other machine learning frameworks. \n\nStep 1: Understand what ML is all about\n---------------------------------------\n\nTensorFlow 2.0 is designed to make building neural networks for machine learning easy, which is why TensorFlow 2.0 uses an API called Keras. The book [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python-second-edition) by Francois Chollet, creator of Keras, is a great place to get started. Read chapters 1-4 to understand the fundamentals of ML from a programmer's perspective. The second half of the book delves into areas like Computer Vision, Natural Language Processing, Generative Deep Learning, and more. Don't worry if these topics are too advanced right now as they will make more sense in due time. \n[AI and Machine Learning for Coders](https://www.oreilly.com/library/view/ai-and-machine/9781492078180/) \nby Laurence Moroney \nThis introductory book provides a code-first approach to learn how to implement the most common ML scenarios, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. \n[View book](https://www.oreilly.com/library/view/ai-and-machine/9781492078180/) \nCode \nTheory \nBuild \n[Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python-second-edition) \nby Francois Chollet \nThis book is a practical, hands-on introduction to Deep Learning with Keras. \n[View book](https://www.manning.com/books/deep-learning-with-python-second-edition) \nCode \nTheory \nBuild \n\n##### ⬆ Or ⬇\n\nTake an online course such as Coursera's [Introduction to TensorFlow](https://www.coursera.org/learn/introduction-tensorflow) or Udacity's [Intro to TensorFlow for Deep Learning](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187), both of which cover the same fundamentals as Francois's book. You may also find [these videos](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) from 3blue1brown helpful, which give you quick explanations about how neural networks work on a mathematical level.\n\nCompleting this step will give you the foundations of how ML works, preparing you to go deeper. \n\nDeepLearning.AI\n[Intro to TensorFlow for AI, ML, and Deep Learning](https://www.coursera.org/learn/introduction-tensorflow) \nDeveloped in collaboration with the TensorFlow team, this course is part of the TensorFlow Developer Specialization and will teach you best practices for using TensorFlow. \n[View course](https://www.coursera.org/learn/introduction-tensorflow) \nCode \nBuild \n[Intro to TensorFlow for Deep Learning](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187) \nIn this online course developed by the TensorFlow team and Udacity, you'll learn how to build deep learning applications with TensorFlow. \nFree [View course](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187) \nCode \nMath \nTheory \nBuild \n\nStep 2: Beyond the basics\n-------------------------\n\nTake the [TensorFlow Developer Specialization](https://www.coursera.org/specializations/tensorflow-in-practice), which takes you beyond the basics into introductory Computer Vision, NLP, and Sequence modelling.\n\nCompleting this step continues your introduction, and teaches you how to use TensorFlow to build basic models for a variety of scenarios, including image classification, understanding sentiment in text, generative algorithms, and more. \n\nDeepLearning.AI\n[TensorFlow Developer Specialization](https://www.coursera.org/specializations/tensorflow-in-practice) \nIn this four-course Specialization taught by a TensorFlow developer, you'll explore the tools and software developers use to build scalable AI-powered algorithms in TensorFlow. \n[View course](https://www.coursera.org/specializations/tensorflow-in-practice) \nCode \nBuild \n\nStep 3: Practice\n----------------\n\nTry some of our [TensorFlow Core tutorials](/tutorials), which will allow you to practice the concepts you learned in steps 1 and 2. When you're done, try some of the more advanced exercises.\n\nCompleting this step will improve your understanding of the main concepts and scenarios you will encounter when building ML models.\n\nStep 4: Go deeper with TensorFlow\n---------------------------------\n\nNow it's time to go back to [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python-second-edition) by Francois and finish chapters 5-9. You should also read the book [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/), by Aurelien Geron. This book introduces ML and deep learning using TensorFlow 2.0.\n\nCompleting this step will round out your introductory knowledge of ML, including expanding the platform to meet your needs. \n[Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) \nby Aurélien Géron \nUsing concrete examples, minimal theory, and two production-ready Python frameworks---Scikit-Learn and TensorFlow---this book helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. \n[View book](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) \nCode \nTheory \nBuild \n[Next\nTheoretical and advanced machine learning with TensorFlow](/resources/learn-ml/theoretical-and-advanced-machine-learning) \n\nLearn, develop and build with TensorFlow\n----------------------------------------\n\n[Get started](/learn)"]]
