├── LICENSE
├── README.md
└── readme.tr.md
/LICENSE:
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585 | permissions. However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 |
589 | 15. Disclaimer of Warranty.
590 |
591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 |
600 | 16. Limitation of Liability.
601 |
602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 |
612 | 17. Interpretation of Sections 15 and 16.
613 |
614 | If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 |
621 | END OF TERMS AND CONDITIONS
622 |
623 | How to Apply These Terms to Your New Programs
624 |
625 | If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
627 | free software which everyone can redistribute and change under these terms.
628 |
629 | To do so, attach the following notices to the program. It is safest
630 | to attach them to the start of each source file to most effectively
631 | state the exclusion of warranty; and each file should have at least
632 | the "copyright" line and a pointer to where the full notice is found.
633 |
634 |
635 | Copyright (C)
636 |
637 | This program is free software: you can redistribute it and/or modify
638 | it under the terms of the GNU General Public License as published by
639 | the Free Software Foundation, either version 3 of the License, or
640 | (at your option) any later version.
641 |
642 | This program is distributed in the hope that it will be useful,
643 | but WITHOUT ANY WARRANTY; without even the implied warranty of
644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
645 | GNU General Public License for more details.
646 |
647 | You should have received a copy of the GNU General Public License
648 | along with this program. If not, see .
649 |
650 | Also add information on how to contact you by electronic and paper mail.
651 |
652 | If the program does terminal interaction, make it output a short
653 | notice like this when it starts in an interactive mode:
654 |
655 | Copyright (C)
656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
657 | This is free software, and you are welcome to redistribute it
658 | under certain conditions; type `show c' for details.
659 |
660 | The hypothetical commands `show w' and `show c' should show the appropriate
661 | parts of the General Public License. Of course, your program's commands
662 | might be different; for a GUI interface, you would use an "about box".
663 |
664 | You should also get your employer (if you work as a programmer) or school,
665 | if any, to sign a "copyright disclaimer" for the program, if necessary.
666 | For more information on this, and how to apply and follow the GNU GPL, see
667 | .
668 |
669 | The GNU General Public License does not permit incorporating your program
670 | into proprietary programs. If your program is a subroutine library, you
671 | may consider it more useful to permit linking proprietary applications with
672 | the library. If this is what you want to do, use the GNU Lesser General
673 | Public License instead of this License. But first, please read
674 | .
675 |
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1 | 
2 | 
3 | 
4 | [](https://twitter.com/intent/follow?screen_name=demirbasayyuce)
5 |
6 |
7 |
8 | [](https://github.com/ayyucedemirbas/Machine-Learning-Pathway/blob/master/readme.tr.md)
9 |
10 | # Zero-to-Hero Pathway for Machine Learning and Deep Learning
11 |
12 | #### Phase 1: Getting Started with Programming & Machine Learning
13 |
14 | 1. **Python Basics:**
15 | - Start by learning Python, the most widely used programming language in machine learning. You can use resources like:
16 | - Codecademy's Python Course: https://www.codecademy.com/learn/learn-python-3
17 | - Python.org's Official Tutorial: https://docs.python.org/3/tutorial/
18 | - Harvard CS50’s Introduction to Programming with Python: https://cs50.harvard.edu/python/2022/
19 |
20 | 2. **Object-Oriented Programming (OOP):**
21 | - Learn the fundamentals of OOP as it is commonly used in machine learning libraries and projects. Understand concepts like classes, objects, inheritance, and polymorphism.
22 | - Python OOP Tutorial: https://realpython.com/python3-object-oriented-programming/
23 | - freeCodeCamp Object Oriented Programming with Python: https://www.youtube.com/watch?v=Ej_02ICOIgs
24 |
25 | #### Optional: Learning Git and Bash Basics
26 |
27 | **Version Control with Git:**
28 |
29 | - Understand the basics of version control with Git, including creating repositories, making commits, branching, and merging.
30 | - GitHub and Git Tutorial for Beginners: https://www.datacamp.com/tutorial/github-and-git-tutorial-for-beginners
31 | - Git Document: https://git-scm.com/book/en/v2
32 | - W3 Schools Git Tutorial: https://www.w3schools.com/git/
33 |
34 | **Bash Basics:**
35 |
36 | - Learn the fundamentals of Bash scripting and command-line operations to automate tasks and manage your projects effectively.
37 | - Bash Scripting Tutorial for Beginners: https://linuxconfig.org/bash-scripting-tutorial-for-beginners
38 |
39 | 3. **Mathematics for Machine Learning:**
40 | - Brush up on essential mathematical concepts used in machine learning, such as linear algebra, calculus, and probability. You can use:
41 | - Khan Academy's Linear Algebra Course: https://www.khanacademy.org/math/linear-algebra
42 | - Khan Academy's Multivariable Calculus Course: https://www.khanacademy.org/math/multivariable-calculus
43 | - Coursera's Mathematics for Machine Learning Specialization: https://www.coursera.org/specializations/mathematics-machine-learning
44 | - A collection of resources to learn mathematics for machine learning: https://github.com/dair-ai/Mathematics-for-ML
45 |
46 | 4. **Discrete Mathematics:**
47 | - Study discrete mathematics, which is important for understanding algorithms, data structures, and probability theory.
48 | - MIT OpenCourseWare - Mathematics for Computer Science: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2005/
49 |
50 | 5. **Analysis of Algorithms:**
51 | - Understand the fundamentals of algorithm analysis, time complexity, and space complexity, which are essential for optimizing machine learning models and algorithms.
52 | - MIT OpenCourseWare - Introduction to Algorithms: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/
53 | - Coursera - Algorithms Specialization from Stanford University: https://www.coursera.org/specializations/algorithms
54 |
55 | 6. **Introduction to Machine Learning:**
56 | - Enroll in a beginner-level machine learning course that covers the following subtopics:
57 |
58 | **Instance-Based Methods:**
59 | - Learn about k-Nearest Neighbors (k-NN) algorithm and its applications.
60 | - Introduction to k-Nearest Neighbors: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
61 | - Scikit-learn k-NN Documentation: https://scikit-learn.org/stable/modules/neighbors.html
62 |
63 | **Model-Based Methods:**
64 | - Explore model-based methods, including decision trees and random forests.
65 | - Decision Trees and Random Forests: https://en.wikipedia.org/wiki/Decision_tree_learning
66 | - Scikit-learn Decision Trees Documentation: https://scikit-learn.org/stable/modules/tree.html
67 | - Scikit-learn Random Forests Documentation: https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees
68 |
69 | **Supervised Learning:**
70 | - Study supervised learning algorithms such as linear regression and logistic regression.
71 | - Linear Regression: https://en.wikipedia.org/wiki/Linear_regression
72 | - Logistic Regression: https://en.wikipedia.org/wiki/Logistic_regression
73 | - Scikit-learn Linear Regression Documentation: https://scikit-learn.org/stable/modules/linear_model.html
74 | - Scikit-learn Logistic Regression Documentation: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
75 |
76 | **Unsupervised Learning:**
77 | - Understand unsupervised learning techniques like clustering and dimensionality reduction.
78 | - Clustering: https://en.wikipedia.org/wiki/Cluster_analysis
79 | - Dimensionality Reduction: https://en.wikipedia.org/wiki/Dimensionality_reduction
80 | - Scikit-learn Clustering Documentation: https://scikit-learn.org/stable/modules/clustering.html
81 | - Scikit-learn Dimensionality Reduction Documentation: https://scikit-learn.org/stable/modules/decomposition.html
82 |
83 | **Model Evaluation and Hyperparameter Tuning:**
84 | - Learn about model evaluation metrics, cross-validation, and techniques for hyperparameter tuning.
85 | - Scikit-learn Model Evaluation: https://scikit-learn.org/stable/modules/model_evaluation.html
86 | - Scikit-learn Hyperparameter Tuning: https://scikit-learn.org/stable/modules/grid_search.html
87 |
88 | - Coursera's Machine Learning by Andrew Ng: https://www.coursera.org/learn/machine-learning
89 | - edX's Introduction to Machine Learning with Python: https://www.edx.org/course/machine-learning-with-python-a-practical-introduct
90 | - Microsoft ML For Beginners: https://github.com/microsoft/ML-For-Beginners
91 | - A curated list of Machine Learning frameworks, libraries and software: https://github.com/josephmisiti/awesome-machine-learning
92 |
93 | 7. **Data Preprocessing:**
94 | - Learn about data preprocessing techniques such as data cleaning, feature scaling, handling missing values, and data normalization to prepare data for machine learning models.
95 | - Towards Data Science - Data Cleaning and Preprocessing: https://medium.com/analytics-vidhya/data-cleaning-and-preprocessing-a4b751f4066f
96 |
97 | 8. **Data Augmentation:**
98 | - Understand data augmentation, a technique used to artificially expand the size of a training dataset by applying various transformations to existing data samples.
99 | - Data Augmentation for Image Data in Python: https://towardsdatascience.com/data-augmentation-for-deep-learning-4fe21d1a4eb9
100 |
101 | 9. **Hands-on Projects:**
102 | - Practice your skills with small machine learning projects using libraries like scikit-learn. Incorporate data preprocessing and data augmentation techniques in your projects.
103 | - GitHub Repositories and Kaggle Kernels offer a plethora of beginner-friendly ML projects to get you started.
104 |
105 | #### Phase 2: Exploring Deep Learning
106 |
107 | 10. **Neural Networks and Deep Learning:**
108 | - Delve into the foundations of deep learning, including neural networks, activation functions, backpropagation, and optimization techniques.
109 | - Coursera's Deep Learning Specialization by Andrew Ng: https://www.coursera.org/specializations/deep-learning
110 | - Neural Networks and Deep Learning Book by Michael Nielsen: http://neuralnetworksanddeeplearning.com/
111 | - A curated list of Deep Learning tutorials, projects and communities: https://github.com/ChristosChristofidis/awesome-deep-learning
112 |
113 | 11. **TensorFlow and Keras:**
114 | - Learn how to work with deep learning frameworks like TensorFlow and Keras, which are widely used for building and training neural networks.
115 | - TensorFlow's Official Website: https://www.tensorflow.org/
116 | - Keras Documentation: https://keras.io/
117 | - TensorFlow Tutorials: https://www.tensorflow.org/tutorials
118 | - Keras Tutorials: https://keras.io/guides/
119 |
120 | 12. **Image Processing Basics:**
121 | - Image Representation: Understand how digital images are represented as matrices of pixels and how to load and display images using libraries like OpenCV and Pillow.
122 | - Pixel Operations: Learn basic pixel-level operations such as color manipulation, brightness adjustment, and thresholding.
123 | - Image Filtering: Study various image filtering techniques, including blurring, sharpening, edge detection, and noise reduction, using convolutional kernels.
124 | - Image Transforms: Explore image transformation techniques such as rotation, scaling, translation, and affine transformations to modify the spatial orientation of images.
125 | - Histogram Equalization: Understand histogram equalization to improve image contrast and enhance details in images.
126 | - Image Segmentation: Learn about image segmentation techniques to divide an image into meaningful regions or objects.
127 | - Morphological Operations: Study morphological operations like erosion and dilation for image processing tasks.
128 | - Image Compression: Understand image compression techniques to reduce the file size of images without significant loss of quality.
129 | - Feature Extraction: Learn about feature extraction methods for extracting meaningful information from images, such as color histograms, HOG (Histogram of Oriented Gradients), and SIFT (Scale-Invariant Feature Transform).
130 |
131 | Resources:
132 | - Digital Image Processing Book by Rafael C. Gonzalez and Richard E. Woods
133 | - OpenCV Documentation: https://docs.opencv.org/4.x/d9/df8/tutorial_root.html
134 |
135 | 13. **Computer Vision Fundamentals:**
136 | - Learn the basics of computer vision, including feature detection, image matching, and object recognition techniques.
137 | - Feature Detection and Matching: Understand feature detection algorithms like SIFT, SURF, and ORB and how to use them for image matching.
138 | - Object Detection: Study object detection techniques such as Haar cascades and deep learning-based approaches like YOLO and SSD.
139 |
140 | 14. **Convolutional Neural Networks (CNNs) for Computer Vision:**
141 | - Understand CNN architectures, transfer learning for image recognition, and object detection using CNNs.
142 | - Convolutional Neural Networks, Explained: https://towardsdatascience.com/convolutional-neural-networks-explained-9cc5188c4939
143 | - Fast.ai's Practical Deep Learning for Coders course: https://course.fast.ai/
144 | - Stanford's CS231n: Convolutional Neural Networks for Visual Recognition: http://cs231n.stanford.edu/
145 |
146 | **Vision Transformers (ViT):**
147 |
148 | - Vision Transformer is a powerful architecture for image recognition tasks that has gained significant attention in recent years.
149 | - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929
150 |
151 | 15. **Natural Language Processing:**
152 | - Learn the basics of text preprocessing, tokenization, and language modeling.
153 | - Natural Language Toolkit (NLTK) Documentation: https://www.nltk.org/
154 | - Learn about RNNs and their applications in text generation and sentiment analysis.
155 | - Coursera's Natural Language Processing Specialization: https://www.coursera.org/specializations/natural-language-processing
156 | - The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
157 |
158 | 16. **Transformers and Pre-trained Models:**
159 | - Study transformer architectures like BERT and GPT, and learn to use pre-trained models for various NLP tasks.
160 | - Hugging Face's Transformers Library: https://huggingface.co/transformers/
161 | - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding: https://arxiv.org/abs/1810.04805
162 | - GPT-3: Language Models are Few-Shot Learners: https://arxiv.org/abs/2005.14165
163 |
164 | 17. **Time Series Analysis:**
165 | - Learn techniques for time series data preprocessing, modeling, and forecasting.
166 | - Coursera's Time Series Course: https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction?
167 | - Time Series Analysis: Forecasting and Control Book by George Box, Gwilym Jenkins, and Gregory Reinsel
168 |
169 | 18. **Time Series Forecasting with Deep Learning:**
170 | - Understand how to apply recurrent neural networks (RNNs) and LSTM models for time series forecasting.
171 | - TensorFlow Time Series Tutorial: https://www.tensorflow.org/tutorials/structured_data/time_series
172 | - Darts Python library documentation: https://unit8co.github.io/darts/README.html
173 |
174 | 19. **Audio Processing and Speech Recognition:**
175 | - Study audio signal processing, speech recognition, and speech-to-text applications.
176 | - Mozilla's Deep Learning for Audio and Speech: https://github.com/mozilla/DeepSpeech
177 |
178 | #### Phase 3: Model Deployment and MLOps
179 |
180 | 20. **Model Deployment:**
181 | - Learn how to deploy machine learning models in production environments, including cloud platforms and edge devices.
182 | - Flask for API Development: https://flask.palletsprojects.com/
183 | - Deploying ML Models with TensorFlow Serving: https://www.tensorflow.org/tfx
184 | - Deploying ML Models with ONNX Runtime: https://onnxruntime.ai/
185 |
186 | 21. **MLOps:**
187 | - Understand the principles of MLOps and the best practices for managing the end-to-end machine learning lifecycle.
188 | - Continuous Integration and Continuous Deployment (CI/CD) for ML: https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build
189 | - A curated list of references for MLOps: https://github.com/visenger/awesome-mlops
190 |
191 | 22. **Monitoring and Scaling ML Models:**
192 | - Explore techniques for monitoring model performance and scaling ML systems.
193 | - TensorFlow Extended (TFX) Model Monitoring: https://www.tensorflow.org/tfx
194 | - Scaling Machine Learning at Uber with Michelangelo: https://eng.uber.com/scaling-michelangelo/
195 |
196 | 23. **Model Versioning and Experiment Tracking:**
197 | - Learn about version control for ML models and experiment tracking tools to manage model iterations effectively.
198 | - DVC for Machine Learning Versioning: https://dvc.org/
199 | - MLflow for Experiment Tracking: https://mlflow.org/
200 |
201 | 24. **Deploying ML Models in the Cloud:**
202 | - Understand cloud-based deployment options for machine learning models using platforms like AWS, GCP, and Azure.
203 | - AWS SageMaker: https://aws.amazon.com/sagemaker/
204 | - Google Cloud AI Platform: https://cloud.google.com/ai-platform
205 | - Microsoft Azure Machine Learning: https://azure.microsoft.com/en-us/services/machine-learning/
206 |
207 | #### Phase 4: Advanced Deep Learning
208 |
209 | 25. **Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) & Stable Diffusion:**
210 | - Study GANs and VAEs, two powerful techniques in the domain of generative modeling.
211 | - Stable Diffusion: https://course.fast.ai/Lessons/lesson9.html
212 | - Generative Adversarial Networks (GANs) by Ian Goodfellow et al.: https://arxiv.org/abs/1406.2661
213 | - Auto-Encoding Variational Bayes (VAE) by Kingma and Welling: https://arxiv.org/abs/1312.6114
214 |
215 | 26. **Transfer Learning and Model Fine-Tuning:**
216 | - Learn how to leverage pre-trained models and fine-tune them for specific tasks.
217 | - Fast.ai's course on Transfer Learning: https://course.fast.ai/Lessons/lesson1.html
218 |
219 | 27. **Advanced Topics and Research Papers:**
220 | - Start reading research papers and exploring advanced topics in deep learning.
221 | - arXiv.org is a great resource for accessing research papers in the field: https://arxiv.org/
222 | - Google Scholar: https://scholar.google.com/
223 |
224 | 28. **Contributing to Open Source Projects:**
225 | - Contribute to open-source deep learning projects on GitHub. This will help you gain practical experience and collaborate with others in the community.
226 | - A Beginners Guide to Open Source: https://dev.to/arindam_1729/a-beginners-guide-to-open-source-4nc5
227 |
228 | #### Phase 5: Real-world Projects and Specializations
229 |
230 | 29. **Machine Learning Projects and Competitions:**
231 | - Participate in Kaggle competitions and create real-world ML projects to build your portfolio.
232 | - Kaggle: https://www.kaggle.com/
233 |
234 | 30. **Deep Learning Specializations:**
235 | - Enroll in specialized deep learning courses and certifications to gain expertise in specific areas like computer vision, NLP, etc.
236 | - DeepLearning.AI's TensorFlow Developer Professional Certificate: https://www.coursera.org/professional-certificates/tensorflow-in-practice
237 | - Coursera's AI for Medicine Specialization: https://www.coursera.org/specializations/ai-for-medicine
238 | - DeepLearning.AI's Deep Learning Specialization: https://www.coursera.org/specializations/deep-learning
239 |
240 | 31. **Research Internship or Master's Degree (Optional):**
241 | - Consider pursuing a research internship or a master's degree in machine learning or artificial intelligence if you want to dive deeper into the academic and research aspects of the field.
242 |
243 | 32. **Joining ML/DL Communities and Conferences:**
244 | - Engage with ML/DL communities online through forums like Reddit (r/MachineLearning, r/deeplearning), and attend conferences and workshops like NeurIPS, ICML, CVPR, and ACL to stay updated with the latest advancements and network with professionals in the field.
245 |
246 | 33. **Building a Portfolio and Personal Projects:**
247 | - Showcase your skills by creating a portfolio of your projects on GitHub and personal website.
248 | - Collaborate on open-source projects or create your own projects to solve real-world problems and demonstrate your expertise.
249 |
250 | 34. **Continuous Learning and Staying Updated:**
251 | - Machine learning and deep learning are rapidly evolving fields. Stay updated with the latest research papers, blog posts, and tutorials to continuously enhance your skills and knowledge.
252 |
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1 | 
2 | 
3 | 
4 | [](https://twitter.com/intent/follow?screen_name=ayyucedemirbas)
5 | [](https://github.com/ayyucedemirbas/Machine-Learning-Pathway/blob/main/README.md)
6 |
7 |
8 | # Makine Öğrenimi ve Derin Öğrenme için Sıfırdan İleri Seviyeye Giden Yol
9 |
10 | #### Aşama 1: Programlama ve Makine Öğrenimine Başlarken
11 |
12 | 1. **Python Temelleri:**
13 |
14 | - Makine öğreniminde en yaygın kullanılan programlama dili olan - Python'ı öğrenerek başlayın. Aşağıdaki kaynakları kullanabilirsiniz:
15 |
16 | - Codecademy'nin Python Kursu: https://www.codecademy.com/learn/ learn-python-3
17 |
18 | - Python.org'un Resmi Eğitimi: https://docs.python.org/3/tutorial/
19 |
20 | 2. **Nesne Yönelimli Programlama (OOP):**
21 |
22 | - Makine öğrenimi kütüphanelerinde ve projelerinde yaygın olarak kullanılan OOP'nin temellerini öğrenin. Sınıflar, nesneler, kalıtım ve polimorfizm gibi kavramları anlayın.
23 | - Python OOP Eğitimi: https://realpython.com/python3-object-oriented-programming/
24 |
25 |
26 | #### İsteğe bağlı: Git ve Bash Temellerini Öğrenme
27 |
28 | **Git ile Sürüm Kontrolü:**
29 |
30 |
31 | - Depo oluşturma, dallara ayırma ve birleştirme dahil olmak üzere Git ile sürüm kontrolünün temellerini anlayın.
32 | - Yeni Başlayanlar için GitHub ve Git Eğitimi: https://www.datacamp.com/tutorial/github-and-git-tutorial-for-beginners
33 | - Git Belgesi: https://git-scm.com/book/en/v2
34 | - W3 Okulları Git Eğitimi: https://www.w3schools.com/git/
35 |
36 | **Bash Temelleri:**
37 |
38 | - Görevleri otomatikleştirmek ve projelerinizi etkili bir şekilde yönetmek için Bash komut dosyası oluşturma ve komut satırı işlemlerinin temellerini öğrenin.
39 | - Yeni Başlayanlar için Bash Scripting Eğitimi: https://linuxconfig.org/bash-scripting-tutorial-for-beginners
40 |
41 | 3. **Makine Öğrenimi İçin Matematik:**
42 |
43 | - Doğrusal cebir, kalkülüs ve olasılık gibi makine öğreniminde kullanılan temel matematiksel kavramları öğrenin. Kullanabilirsiniz:
44 | - Khan Academy'nin Lineer Cebir Kursu: https://www.khanacademy.org/math/linear-algebra
45 | - Khan Academy'nin Çok Değişkenli Kalkülüs Kursu: https://www.khanacademy.org/math/multivariable-calculus
46 | - Coursera'nın Makine Öğrenimi Uzmanlığı için Matematik: https://www.coursera.org/specializations/mathematics-machine-learning
47 |
48 |
49 | 4. **Ayrık(Discrete) Matematik:**
50 |
51 | - Algoritmaları, veri yapılarını ve olasılık teorisini anlamak için önemli olan ayrık matematiği inceleyin.
52 | - MIT OpenCourseWare - Bilgisayar Bilimleri için Matematik: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2005/
53 |
54 | 5. **Algoritmaların Analizi:**
55 |
56 | - Makine öğrenimi modellerini ve algoritmalarını optimize etmek için gerekli olan algoritma analizi, zaman karmaşıklığı ve uzay karmaşıklığının temellerini anlayın.
57 | - MIT OpenCourseWare - Algoritmalara Giriş: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/
58 |
59 | 6. **Makine Öğrenimine Giriş:**
60 |
61 | - Aşağıdaki alt konuları kapsayan başlangıç seviyesindeki bir makine öğrenimi kursuna kaydolun:
62 |
63 |
64 | **Örnek Tabanlı Yöntemler:**
65 |
66 | - k-Nearest Neighbors ve (k-NN) algoritması ve uygulamaları hakkında bilgi edinin.
67 | - k-Nearest Neighbors Giriş: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
68 | - Scikit-learn k-NN Belgeleri: https://scikit-learn.org/stable/modules/neighbors.html
69 |
70 |
71 | **Model Tabanlı Yöntemler:**
72 |
73 | - Decision trees ve random forests dahil olmak üzere model tabanlı yöntemleri keşfedin.
74 | - Decision Trees Ve Random Forests: https://en.wikipedia.org/wiki/Decision_tree_learning
75 | - Scikit-learn Decision Trees Dokümantasyonu: https://scikit-learn.org/stable/modules/tree.html
76 | - Scikit-learn Random Forests Dokümantasyonu: https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees
77 |
78 |
79 | **Denetimli(Supervised) Öğrenme:**
80 |
81 | - Doğrusal regresyon(linear) ve lojistik(logistic) regresyon gibi denetimli öğrenme algoritmalarını inceleyin.
82 | - Doğrusal (Linear) Regression: https://en.wikipedia.org/wiki/Linear_regression
83 | - Lojistik (Logistic) Regression: https://en.wikipedia.org/wiki/Logistic_regression
84 | - Scikit-learn Doğrusal Regresyon Belgeleri: https://scikit-learn.org/stable/modules/linear_model.html
85 | - Scikit-learn Lojistik Regresyon Belgeleri: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
86 |
87 |
88 | **Denetimsiz(Unsupervised) Öğrenme:**
89 |
90 | - Kümeleme(Clustering) ve boyutsallık(Dimensionality) azaltma gibi denetimsiz(unsupervised) öğrenme tekniklerini anlayın.
91 | - Kümeleme(Clustering): https://en.wikipedia.org/wiki/Cluster_analysis
92 | - Boyut Azaltma(Dimensionality Reduction): https://en.wikipedia.org/wiki/Dimensionality_reduction
93 | - Scikit-learn Kümeleme Belgeleri: https://scikit-learn.org/stable/modules/clustering.html
94 | - Scikit-learn Boyutsallık Azaltma Belgeleri: https://scikit-learn.org/stable/modules/decomposition.html
95 |
96 |
97 | **Model Değerlendirme(Model Evaluation) ve Hiperparametre Ayarlama(Hyperparameter Tuning):**
98 |
99 | - Model değerlendirme metrikleri(model evaluation metrics), çapraz doğrulama(cross-validation) ve hiperparametre ayarlama teknikleri(hyperparameter tuning) hakkında bilgi edinin.
100 |
101 | - Scikit-learn Model Değerlendirmesi(Model Evaluation): https://scikit-learn.org/stable/modules/model_evaluation.html
102 |
103 | - Scikit-learn Hiperparametre Ayarlama(Hyperparameter Tuning): https://scikit-learn.org/stable/modules/grid_search.html
104 |
105 | - Andrew Ng'den Coursera'nın Makine Öğrenimi: https://www.coursera.org/learn/machine-learning
106 |
107 | - edX'in Python ile Makine Öğrenimine Giriş: https://www.edx.org/course/machine-learning-with-python-a-practical-introduct
108 |
109 |
110 |
111 |
112 |
113 |
114 |
115 |
116 | 7. **Veri Ön İşleme:**
117 |
118 | Verileri makine öğrenimi modellerine hazırlamak için veri temizleme, özellik ölçekleme, eksik değerleri işleme ve veri normalleştirme gibi veri ön işleme teknikleri hakkında bilgi edinin.
119 | Veri Bilimine Doğru - Veri Temizleme ve Ön İşleme: https://medium.com/analytics-vidhya/data-cleaning-and-preprocessing-a4b751f4066f
120 |
121 | 8. **Veri Artırma:**
122 |
123 | Mevcut veri örneklerine çeşitli dönüşümler uygulayarak bir eğitim veri kümesinin boyutunu yapay olarak genişletmek için kullanılan bir teknik olan veri artırmayı anlayın.
124 | Python'da Görüntü Verileri için Veri Büyütme: https://towardsdatascience.com/data-augmentation-for-deep-learning-4fe21d1a4eb9
125 |
126 | 9. **Uygulamalı Projeler:**
127 |
128 | Scikit-learn gibi kütüphaneleri kullanarak küçük makine öğrenimi projeleri ile becerilerinizi geliştirin. Projelerinize veri ön işleme ve veri artırma tekniklerini dahil edin.
129 | GitHub Depoları ve Kaggle Kernel'leri, başlamanız için çok sayıda yeni başlayan dostu makine öğrenimi projesi sunar.
130 |
131 | #### 2. Aşama: Derin Öğrenmeyi Keşfetme
132 | 10. **Sinir Ağları ve Derin Öğrenme:**
133 |
134 | - Sinir ağları, aktivasyon fonksiyonları, geri yayılım ve optimizasyon teknikleri dahil olmak üzere derin öğrenmenin temellerini araştırın.
135 | - Andrew Ng'nin Coursera Derin Öğrenme Uzmanlığı: https://www.coursera.org/specializations/deep-learning
136 | - Michael Nielsen'in Sinir Ağları ve Derin Öğrenme Kitabı: http://neuralnetworksanddeeplearning.com/
137 |
138 |
139 | 11. **TensorFlow ve Keras:**
140 |
141 | - Sinir ağları oluşturmak ve eğitmek için yaygın olarak kullanılan TensorFlow ve Keras gibi derin öğrenme çerçeveleriyle nasıl çalışacağınızı öğrenin.
142 | - TensorFlow'un Resmi Web Sitesi: https://www.tensorflow.org/
143 | - Keras Dokümantasyonu: https://keras.io/
144 | - TensorFlow Eğitimleri: https://www.tensorflow.org/tutorials
145 | - Keras Eğitimleri: https://keras.io/guides/
146 |
147 |
148 | 12. **Görüntü İşleme Temelleri:**
149 |
150 | - Görüntü Gösterimi(Image Representation): Dijital görüntülerin piksel matrisleri olarak nasıl temsil edildiğini ve OpenCV ve Pillow gibi kütüphaneleri kullanarak görüntülerin nasıl yükleneceğini ve görüntüleneceğini anlayın.
151 | - Piksel İşlemleri(Pixel Operations): Renk manipülasyonu, parlaklık ayarı ve eşikleme gibi temel piksel düzeyinde işlemleri öğrenin.
152 | - Görüntü Filtreleme(Image Filtering): Konvolüsyonel çekirdekleri kullanarak bulanıklaştırma, keskinleştirme, kenar algılama ve gürültü azaltma dahil olmak üzere çeşitli görüntü filtreleme tekniklerini inceleyin.
153 | - Görüntü Dönüşümleri(Image Transforms): Görüntülerin uzamsal yönelimini değiştirmek için döndürme, ölçekleme, öteleme ve afin dönüşümleri gibi görüntü dönüştürme tekniklerini keşfedin.
154 | - Histogram Eşitleme(Histogram Equalization): Görüntü kontrastını iyileştirmek ve görüntülerdeki ayrıntıları geliştirmek için histogram eşitlemeyi anlayın.
155 | - Görüntü Segmentasyonu(Image Segmentation): Bir görüntüyü anlamlı bölgelere veya nesnelere ayırmak için görüntü segmentasyon teknikleri hakkında bilgi edinin.
156 | - Morfolojik İşlemler(Morphological Operations): Görüntü işleme görevleri için erozyon ve dilatasyon gibi morfolojik işlemleri inceleyin.
157 | - Görüntü Sıkıştırma(Image Compression): Önemli bir kalite kaybı olmadan görüntülerin dosya boyutunu azaltmak için görüntü sıkıştırma tekniklerini anlayın.
158 | - Özellik Çıkarma(Feature Extraction): Renk histogramları, HOG (Histogram of Oriented Gradients) ve SIFT (Scale-Invariant Feature Transform) gibi görüntülerden anlamlı bilgi çıkarmaya yönelik özellik çıkarma yöntemleri hakkında bilgi edinin.
159 |
160 |
161 | **Kaynaklar:**
162 |
163 | - Rafael C. Gonzalez ve Richard E. Woods tarafından yazılan Dijital Görüntü İşleme Kitabı
164 | - OpenCV Dokümantasyonu: https://docs.opencv.org/4.x/d9/df8/tutorial_root.html
165 |
166 |
167 | 13. **Bilgisayarla Görme Temelleri(Computer Vision Fundamentals):**
168 |
169 | - Özellik algılama, görüntü eşleştirme ve nesne tanıma teknikleri dahil olmak üzere bilgisayarla görmenin temellerini öğrenin.
170 | - Özellik Algılama ve Eşleştirme: SIFT, SURF ve ORB gibi özellik algılama algoritmalarını ve bunların görüntü eşleştirme için nasıl kullanılacağını anlayın.
171 | - Nesne Algılama: Haar kaskadları ve YOLO ve SSD gibi derin öğrenme tabanlı yaklaşımlar gibi nesne algılama tekniklerini inceleyin.
172 |
173 |
174 | 1. **Bilgisayarla Görme için Evrişimsel Sinir Ağları (Convolutional Neural Networks (CNNs) for Computer Vision):**
175 |
176 | - CNN mimarilerini, görüntü tanıma için transfer öğrenmeyi ve CNN'leri kullanarak nesne algılamayı anlayın.
177 | - Fast.ai'nin Kodlayıcılar için Pratik Derin Öğrenme kursu: https://course.fast.ai/
178 | - Stanford CS231n: Görsel Tanıma için Evrişimsel Sinir Ağları: http://cs231n.stanford.edu/
179 | **Vision Transformers (ViT):**
180 |
181 | - Vision Transformer, son yıllarda büyük ilgi gören görüntü tanıma görevleri için güçlü bir mimaridir.
182 | - Bir Görüntü 16x16 Sözcük Değerindedir: Ölçekte Görüntü Tanıma için Dönüştürücüler: https://arxiv.org/abs/2010.11929
183 |
184 | 15. **Doğal Dil İşleme:**
185 |
186 | - Metin ön işleme, tokenizasyon ve dil modellemenin temellerini öğrenin.
187 | - Doğal Dil Araç Seti (NLTK) Belgeleri: https://www.nltk.org/
188 | - RNN'ler ve bunların metin oluşturma ve duygu analizindeki uygulamaları hakkında bilgi edinin.
189 | - Coursera'nın Doğal Dil İşleme Uzmanlığı: https://www.coursera.org/specializations/natural-language-processing
190 | - Andrej Karpathy'den Tekrarlayan Sinir Ağlarının Mantıksız Etkinliği: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
191 | 16. **Dönüştürücüler ve Önceden Eğitilmiş Modeller(Transformers and Pre-trained Models):**
192 |
193 | - BERT ve GPT gibi dönüştürücü mimarilerini inceleyin ve çeşitli NLP görevleri için önceden eğitilmiş modelleri kullanmayı öğrenin.
194 | - Hugging Face'in Transformatör Kütüphanesi: https://huggingface.co/transformers/
195 | - BERT: Dil Anlama için Derin Çift Yönlü Dönüştürücülerin Ön Eğitimi: https://arxiv.org/abs/1810.04805
196 | - GPT-3: Dil Modelleri Az Atış Yapan Öğrenicilerdir: https://arxiv.org/abs/2005.14165
197 | 17. **Zaman Serisi Analizi:**
198 |
199 | - Zaman serisi veri ön işleme, modelleme ve tahmin tekniklerini öğrenin.
200 | - Coursera'nın Zaman Serileri Kursu: https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction?
201 | - Zaman Serisi Analizi: George Box, Gwilym Jenkins ve Gregory Reinsel tarafından yazılan Tahmin ve Kontrol Kitabı
202 | 18. **Derin Öğrenme ile Zaman Serisi Tahmini(Time Series Forecasting):**
203 |
204 | - Zaman serisi tahmini için tekrarlayan sinir ağlarının (RNN'ler) ve LSTM modellerinin nasıl uygulanacağını anlayın.
205 | - TensorFlow Zaman Serisi Eğitimi: https://www.tensorflow.org/tutorials/structured_data/time_series
206 |
207 |
208 | 19. **Ses İşleme(Audio Processing) ve Konuşma Tanıma(Speech Recognition):**
209 |
210 | - Ses sinyali işleme, konuşma tanıma ve konuşmadan metne uygulamaları üzerinde çalışın.
211 | - Mozilla'nın Ses ve Konuşma için Derin Öğrenmesi: https://github.com/mozilla/DeepSpeech
212 |
213 |
214 | #### Aşama 3: Model Dağıtımı ve MLOps Model Dağıtımı:
215 |
216 | 20. **Model Dağıtımı:**
217 |
218 | - Bulut platformları ve uç cihazlar da dahil olmak üzere üretim ortamlarında makine öğrenimi modellerinin nasıl dağıtılacağını öğrenin.
219 | - API Geliştirme için Flask: https://flask.palletsprojects.com/
220 | - ML Modellerini TensorFlow Serving ile Dağıtma: https://www.tensorflow.org/tfx
221 | - ML Modellerini ONNX Çalışma Zamanı ile Dağıtma: https://onnxruntime.ai/
222 |
223 |
224 | 21. **MLOps:**
225 |
226 | - MLOps ilkelerini ve makine öğrenimi yaşam döngüsünü uçtan uca yönetmek için en iyi uygulamaları anlayın.
227 | - Makine Öğrenimi için Sürekli Entegrasyon ve Sürekli Dağıtım (CI/CD): https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build
228 |
229 |
230 | 22. **Makine Öğrenmesi Modellerinin İzlenmesi ve Ölçeklendirilmesi:**
231 |
232 | - Model performansını izleme ve makine öğrenimi sistemlerini ölçeklendirme tekniklerini keşfedin.
233 | - TensorFlow Extended (TFX) Model İzleme: https://www.tensorflow.org/tfx
234 | - Michelangelo ile Uber'de Makine Öğrenimini Ölçeklendirme: https://eng.uber.com/scaling-michelangelo/
235 | 23. **Model Versiyonlama ve Deney Takibi:**
236 |
237 | - Makine öğrenimi modelleri için sürüm kontrolü ve model yinelemelerini etkili bir şekilde yönetmek için deney izleme araçları hakkında bilgi edinin.
238 | - Makine Öğrenimi Versiyonlama için DVC: https://dvc.org/
239 | - Deney Takibi için MLflow: https://mlflow.org/
240 |
241 |
242 | 24. **Makine Öğrenimi Modellerini Bulutta Dağıtma:**
243 |
244 | - AWS, GCP ve Azure gibi platformları kullanarak makine öğrenimi modelleri için bulut tabanlı dağıtım seçeneklerini anlayın.
245 | - AWS SageMaker: https://aws.amazon.com/sagemaker/
246 | - Google Cloud AI Platform: https://cloud.google.com/ai-platform
247 | - Microsoft Azure Machine Learning: https://azure.microsoft.com/en-us/services/machine-learning/
248 |
249 |
250 | #### Aşama 4: Gelişmiş Derin Öğrenme
251 |
252 | 25. **Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) & Stable Diffusion:**
253 |
254 | - Üretken modelleme alanında iki güçlü teknik olan GAN'ları ve VAE'leri inceleyin.
255 | - Kararlı Difüzyon: https://course.fast.ai/Lessons/lesson9.html
256 | - Generative Adversarial Networks (GANs), Ian Goodfellow ve diğerleri: https://arxiv.org/abs/1406.2661
257 | - Kingma ve Welling'den Otomatik Kodlamalı Varyasyonel Bayes (VAE): https://arxiv.org/abs/1312.6114
258 | 26. **Transfer Öğrenimi ve Model İnce Ayarı:**
259 |
260 | - Önceden eğitilmiş modellerden nasıl yararlanacağınızı ve belirli görevler için bu modellere nasıl ince ayar yapacağınızı öğrenin.
261 | Fast.ai'nin Transfer Öğrenimi kursu: https://course.fast.ai/Lessons/lesson1.html
262 | 27. **İleri Düzey Konular ve Araştırma Makaleleri:**
263 |
264 | - Araştırma makalelerini okumaya ve derin öğrenme alanındaki ileri düzey konuları keşfetmeye başlayın.
265 | arXiv.org, bu alandaki araştırma makalelerine erişmek için harika bir kaynaktır: https://arxiv.org/
266 | Google Akademik: https://scholar.google.com/
267 | 28. **Açık Kaynak Projelerine Katkıda Bulunma:**
268 |
269 | - GitHub'daki açık kaynaklı derin öğrenme projelerine katkıda bulunun. Bu, pratik deneyim kazanmanıza ve topluluktaki diğer kişilerle işbirliği yapmanıza yardımcı olacaktır.
270 |
271 |
272 | #### Aşama 5: Gerçek Dünya Projeleri ve Uzmanlıklar
273 | 29. **Makine Öğrenimi Projeleri ve Yarışmaları:**
274 |
275 | - Kaggle yarışmalarına katılın ve portföyünüzü oluşturmak için gerçek dünya makine öğrenimi projeleri oluşturun.
276 | - Kaggle: https://www.kaggle.com/
277 |
278 |
279 | 30. **Derin Öğrenme Uzmanlıkları:**
280 |
281 | - Bilgisayarla görme, NLP gibi belirli alanlarda uzmanlık kazanmak için özel derin öğrenme kurslarına ve sertifikalarına kaydolun.
282 | - DeepLearning.AI'nın TensorFlow Geliştirici Profesyonel Sertifikası: https://www.coursera.org/professional-certificates/tensorflow-in-practice
283 | - Coursera'nın Tıp Uzmanlığı için Yapay Zeka Programı: https://www.coursera.org/specializations/ai-for-medicine
284 | - DeepLearning.AI'nın Derin Öğrenme Uzmanlığı: https://www.coursera.org/specializations/deep-learning
285 | 31. **Araştırma Stajı veya Yüksek Lisans Derecesi (İsteğe Bağlı):**
286 |
287 | - Alanın akademik ve araştırma yönlerine daha derinlemesine dalmak istiyorsanız, makine öğrenimi veya yapay zeka alanında bir araştırma stajı veya yüksek lisans derecesi almayı düşünün.
288 |
289 | 32. **ML/DL Topluluklarına ve Konferanslarına Katılmak:**
290 |
291 | - Reddit (r/MachineLearning, r/deeplearning) gibi forumlar aracılığıyla ML/DL topluluklarıyla çevrimiçi etkileşime geçin ve en son gelişmelerden haberdar olmak ve alandaki profesyonellerle ağ kurmak için NeurIPS, ICML, CVPR ve ACL gibi konferanslara katılın.
292 |
293 |
294 | 1. **Portföy Oluşturma ve Kişisel Projeler:**
295 |
296 | - GitHub'da ve kişisel web sitenizde projelerinizden oluşan bir portföy oluşturarak becerilerinizi sergileyin.
297 | - Açık kaynaklı projelerde işbirliği yapın veya gerçek dünyadaki sorunları çözmek ve uzmanlığınızı göstermek için kendi projelerinizi oluşturun.
298 |
299 | 34. **Sürekli Öğrenmek ve Güncel Kalmak:**
300 |
301 | - Makine öğrenimi ve derin öğrenme hızla gelişen alanlardır. Becerilerinizi ve bilginizi sürekli geliştirmek için en son araştırma makaleleri, blog gönderileri ve eğitimlerle güncel kalın.
302 |
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