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Ahmed Mohamed Mohamed Hashem
في نهاية هذا المشروع ، ستنشئ Neural Network بسيطة تتكون من 3 طبقات: طبقة الإدخال والطبقة المخفية وطبقة الإخراج باستخدام python . سوف تكون قادرًا على تحديد المفاهيم الأساسية للتعلم الآلي وأنواعه وال algorithms المختلفة ومتى تستخدم بالضبط كل منها. ستتمكن أيضًا...
Read more
في نهاية هذا المشروع ، ستنشئ Neural Network بسيطة تتكون من 3 طبقات: طبقة الإدخال والطبقة المخفية وطبقة الإخراج باستخدام python . سوف تكون قادرًا على تحديد المفاهيم الأساسية للتعلم الآلي وأنواعه وال algorithms المختلفة ومتى تستخدم بالضبط كل منها. ستتمكن أيضًا...
Read more
في نهاية هذا المشروع ، ستنشئ Neural Network بسيطة تتكون من 3 طبقات: طبقة الإدخال والطبقة المخفية وطبقة الإخراج باستخدام python . سوف تكون قادرًا على تحديد المفاهيم الأساسية للتعلم الآلي وأنواعه وال algorithms المختلفة ومتى تستخدم بالضبط كل منها. ستتمكن أيضًا من تنفيذ جميع functions المطلوبة التي ستساعدك في بناء الشبكة ، واختبار هذه الfunctions ، وأخيرًا ، ستتمكن من اختبار ال neural network بالكامل وحساب دقتها.
في هذا المشروع ، سنستخدم Python IDLE لأن لغة Python هي واحدة من أكثر لغات البرمجة المتاحة التي يمكن الوصول إليها بسبب تركيبها المبسط الذي يركز على اللغة الطبيعية ، ويستخدم بشكل كبير في تطبيقات التعلم الآلي وعلوم البيانات التي تعد من أكبر الاتجاهات في علوم الكمبيوتر الآن
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possible dealbreakers
Students with some familiarity with programming in Python programming language will find that this course further enhances their knowledge
Teaches the vital concepts of machine learning , its types, and various algorithms
Guides students through the steps of building a neural network from scratch, including input layer, hidden layer, and output layer
Enhances practical skills in writing functions for building and testing neural networks in Python
Uses Python IDLE, making it accessible to beginners and experienced programmers alike
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Set regular meeting times and locations.
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Establish a communication channel for the group.
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Take turns leading discussions and sharing your knowledge.
Review the concepts of linear algebra
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Ensure a solid foundation before starting this course by reviewing the concepts of linear algebra.
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Linear Algebra
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Review your notes or textbooks from a previous linear algebra course.
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Watch online videos or tutorials on linear algebra.
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Solve practice problems to test your understanding.
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Focus on concepts such as vector spaces, matrices, and linear transformations.
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Practice multiplying matrices by hand multiple times.
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Enroll in the "Neural Networks and Deep Learning" specialization.
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Choose a problem difficulty level.
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Solve the problem using Python.
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Choose a competition that aligns with your interests and skill level.
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Build and train your machine learning model.
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Submit your predictions and track your progress on the leaderboard.
Form a study group with classmates
Show steps
Enhance your understanding through collaborative learning by forming a study group with classmates.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Identify classmates who are interested in forming a study group.
- Set regular meeting times and locations.
- Establish a communication channel for the group.
- Take turns leading discussions and sharing your knowledge.
Review the concepts of linear algebra
Show steps
Ensure a solid foundation before starting this course by reviewing the concepts of linear algebra.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your notes or textbooks from a previous linear algebra course.
- Watch online videos or tutorials on linear algebra.
- Solve practice problems to test your understanding.
- Focus on concepts such as vector spaces, matrices, and linear transformations.
Review matrix multiplication
Show steps
Begin this course by making sure you are proficient with matrix multiplication through review and practice.
Browse courses on
Matrix Multiplication
Show steps
Show steps
Show steps
- Find resources like tutorials, online exercises, or practice problems.
- Practice multiplying matrices by hand multiple times.
- Check your solutions against an online matrix calculator.
- Consider using a whiteboard or physical matrix blocks for better visualization.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete the Coursera tutorial on Neural Networks
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Build your neural network knowledge by completing the Coursera tutorial on neural networks.
Browse courses on
Neural Networks
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Show steps
Show steps
- Sign up for a Coursera account.
- Enroll in the "Neural Networks and Deep Learning" specialization.
- Complete the first module, "Introduction to Neural Networks".
- Watch the video lectures, read the course materials, and complete the quizzes.
Identify mentors in the field of machine learning
Show steps
Enhance your learning journey by connecting with experienced mentors in the field of machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Attend industry events and conferences.
- Join online communities and forums related to machine learning.
- Reach out to professors or researchers in the field.
- Ask for recommendations from your peers.
Solve coding exercises on LeetCode
Show steps
Enhance your coding skills by solving coding exercises on LeetCode.
Browse courses on
Coding
Show steps
Show steps
Show steps
- Create a LeetCode account.
- Choose a problem difficulty level.
- Solve the problem using Python.
- Submit your solution.
- Review the solutions of others.
Participate in Kaggle competitions
Show steps
Test and sharpen your machine learning skills by participating in Kaggle competitions.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Create a Kaggle account.
- Choose a competition that aligns with your interests and skill level.
- Download the competition data and familiarize yourself with the problem.
- Build and train your machine learning model.
- Submit your predictions and track your progress on the leaderboard.
Career center
Learners who complete بناء Neural Network مكونه من 3 طبقات بأستخدام لغة Python will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course provides a hands-on introduction to building and training neural networks, which are a key component of many machine learning applications. By completing this course, aspiring Machine Learning Engineers can gain valuable experience and enhance their skills in this in-demand field.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help organizations make informed decisions. This course provides an introduction to machine learning and neural networks, which are valuable tools for data analysis. By completing this course, aspiring Data Analysts can enhance their skills and become more effective in their roles.
Data Scientist
Data Scientist
Data Scientists analyze data to extract insights and help organizations make informed decisions. This course provides a strong foundation in the fundamentals of machine learning, including neural networks, which are essential for data science. By understanding the concepts and techniques taught in this course, aspiring Data Scientists can build a solid foundation for success in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. This course provides a foundation in machine learning and neural networks, which are increasingly used in quantitative analysis. By understanding these concepts, aspiring Quantitative Analysts can enhance their skills and become more competitive in the job market.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides a foundation in the principles of machine learning and neural networks, which are increasingly used in software development. By understanding these concepts, aspiring Software Engineers can expand their skillset and become more competitive in the job market.
Business Analyst
Business Analyst
Business Analysts help organizations improve their performance by identifying and solving business problems. This course provides an introduction to machine learning and neural networks, which are becoming increasingly important for business analysis. By completing this course, aspiring Business Analysts can gain valuable insights and enhance their skills.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course provides an introduction to machine learning and neural networks, which are increasingly used in product development. By understanding these concepts, aspiring Product Managers can gain valuable insights and enhance their skills.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course provides an introduction to machine learning and neural networks, which are becoming increasingly important for project management. By completing this course, aspiring Project Managers can gain valuable insights and enhance their skills.
Consultant
Consultant
Consultants provide advice and guidance to organizations on a variety of topics. This course provides an introduction to machine learning and neural networks, which are increasingly used in consulting. By completing this course, aspiring Consultants can gain valuable insights and enhance their skills.
Teacher
Teacher
Teachers educate students in a variety of subjects. This course provides an introduction to machine learning and neural networks, which are increasingly used in education. By completing this course, aspiring Teachers can gain valuable insights and enhance their skills.
Researcher
Researcher
Researchers conduct original research in a variety of fields. This course provides an introduction to machine learning and neural networks, which are increasingly used in research. By completing this course, aspiring Researchers can gain valuable insights and enhance their skills.
Writer
Writer
Writers create content for a variety of purposes. This course provides an introduction to machine learning and neural networks, which are increasingly used in writing. By completing this course, aspiring Writers can gain valuable insights and enhance their skills.
Journalist
Journalist
Journalists gather and report news and information. This course provides an introduction to machine learning and neural networks, which are increasingly used in journalism. By completing this course, aspiring Journalists can gain valuable insights and enhance their skills.
Salesperson
Salesperson
Salespeople sell products and services to customers. This course provides an introduction to machine learning and neural networks, which are increasingly used in sales. By completing this course, aspiring Salespeople can gain valuable insights and enhance their skills.
Marketer
Marketer
Marketers promote products and services to consumers. This course provides an introduction to machine learning and neural networks, which are increasingly used in marketing. By completing this course, aspiring Marketers can gain valuable insights and enhance their skills.
For more career information including salaries, visit:
OpenCourser.com/course/3bq2bz/bn
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
بناء Neural Network مكونه من 3 طبقات بأستخدام لغة Python.
Focuses on the mathematical and algorithmic foundations of deep learning. Provides a detailed introduction to convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive treatment of pattern recognition and machine learning from a statistical perspective. Covers a wide range of topics, including Bayesian methods, support vector machines, and Markov models.
Provides a thorough introduction to deep learning concepts and their implementation using Python and Keras, a high-level neural networks API.
Presents machine learning concepts from a probabilistic and optimization perspective. Covers Bayesian inference, gradient descent, and advanced optimization techniques.
Provides a comprehensive treatment of statistical learning methods, including supervised and unsupervised learning, model selection, and regularization techniques.
Provides a comprehensive overview of machine learning concepts and techniques using Python libraries such as Scikit-Learn, Keras, and TensorFlow. Covers fundamental concepts, model evaluation, feature engineering, and deep learning.
Provides theoretical foundations and practical implementation of machine learning algorithms in Python. Covers supervised and unsupervised learning, feature selection, and model evaluation.
Focuses on implementing machine learning algorithms using the R programming language. Covers topics such as linear regression, logistic regression, and decision trees.
Emphasizes practical applications of machine learning algorithms by providing code examples and exercises. Explores topics such as recommender systems, natural language processing, and swarm intelligence.
Introduces the basics of machine learning in a clear and accessible way. Provides real-world examples and practical tips for implementing machine learning solutions.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3bq2bz/bn
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ahmed Mohamed Mohamed Hashem
Good to know
Students with some familiarity with programming in Python programming language will find that this course further enhances their knowledge
Teaches the vital concepts of machine learning , its types, and various algorithms
Guides students through the steps of building a neural network from scratch, including input layer, hidden layer, and output layer
Enhances practical skills in writing functions for building and testing neural networks in Python
Uses Python IDLE, making it accessible to beginners and experienced programmers alike
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