Zero-to-Hero Pathway for Machine Learning and Deep Learning

Phase 1: Getting Started with Programming & Machine Learning

1. Python Basics:

   • Start by learning Python, the most widely used programming language in machine learning. You can use resources like:
     • Codecademy's Python Course: https://www.codecademy.com/learn/learn-python-3
     • Python.org's Official Tutorial: https://docs.python.org/3/tutorial/
     • Harvard CS50’s Introduction to Programming with Python: https://cs50.harvard.edu/python/2022/

2. Object-Oriented Programming (OOP):

   • Learn the fundamentals of OOP as it is commonly used in machine learning libraries and projects. Understand concepts like classes, objects, inheritance, and polymorphism.
   • Python OOP Tutorial: https://realpython.com/python3-object-oriented-programming/
   • freeCodeCamp Object Oriented Programming with Python: https://www.youtube.com/watch?v=Ej_02ICOIgs

Optional: Learning Git and Bash Basics

Version Control with Git:

• Understand the basics of version control with Git, including creating repositories, making commits, branching, and merging.
• GitHub and Git Tutorial for Beginners: https://www.datacamp.com/tutorial/github-and-git-tutorial-for-beginners
• Git Document: https://git-scm.com/book/en/v2
• W3 Schools Git Tutorial: https://www.w3schools.com/git/

Bash Basics:

• Learn the fundamentals of Bash scripting and command-line operations to automate tasks and manage your projects effectively.
• Bash Scripting Tutorial for Beginners: https://linuxconfig.org/bash-scripting-tutorial-for-beginners

3. Mathematics for Machine Learning:

   • Brush up on essential mathematical concepts used in machine learning, such as linear algebra, calculus, and probability. You can use:
   • Khan Academy's Linear Algebra Course: https://www.khanacademy.org/math/linear-algebra
   • Khan Academy's Multivariable Calculus Course: https://www.khanacademy.org/math/multivariable-calculus
   • Coursera's Mathematics for Machine Learning Specialization: https://www.coursera.org/specializations/mathematics-machine-learning
   • A collection of resources to learn mathematics for machine learning: https://github.com/dair-ai/Mathematics-for-ML

4. Discrete Mathematics:

   • Study discrete mathematics, which is important for understanding algorithms, data structures, and probability theory.
   • MIT OpenCourseWare - Mathematics for Computer Science: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2005/

5. Analysis of Algorithms:

   • Understand the fundamentals of algorithm analysis, time complexity, and space complexity, which are essential for optimizing machine learning models and algorithms.
   • MIT OpenCourseWare - Introduction to Algorithms: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/
   • Coursera - Algorithms Specialization from Stanford University: https://www.coursera.org/specializations/algorithms

6. Introduction to Machine Learning:

   • Enroll in a beginner-level machine learning course that covers the following subtopics:

   Instance-Based Methods:

   • Learn about k-Nearest Neighbors (k-NN) algorithm and its applications.
   • Introduction to k-Nearest Neighbors: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
   • Scikit-learn k-NN Documentation: https://scikit-learn.org/stable/modules/neighbors.html

   Model-Based Methods:

   • Explore model-based methods, including decision trees and random forests.
   • Decision Trees and Random Forests: https://en.wikipedia.org/wiki/Decision_tree_learning
   • Scikit-learn Decision Trees Documentation: https://scikit-learn.org/stable/modules/tree.html
   • Scikit-learn Random Forests Documentation: https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees

   Supervised Learning:

   • Study supervised learning algorithms such as linear regression and logistic regression.
   • Linear Regression: https://en.wikipedia.org/wiki/Linear_regression
   • Logistic Regression: https://en.wikipedia.org/wiki/Logistic_regression
   • Scikit-learn Linear Regression Documentation: https://scikit-learn.org/stable/modules/linear_model.html
   • Scikit-learn Logistic Regression Documentation: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

   Unsupervised Learning:

   • Understand unsupervised learning techniques like clustering and dimensionality reduction.
   • Clustering: https://en.wikipedia.org/wiki/Cluster_analysis
   • Dimensionality Reduction: https://en.wikipedia.org/wiki/Dimensionality_reduction
   • Scikit-learn Clustering Documentation: https://scikit-learn.org/stable/modules/clustering.html
   • Scikit-learn Dimensionality Reduction Documentation: https://scikit-learn.org/stable/modules/decomposition.html

   Model Evaluation and Hyperparameter Tuning:

   • Learn about model evaluation metrics, cross-validation, and techniques for hyperparameter tuning.

   • Scikit-learn Model Evaluation: https://scikit-learn.org/stable/modules/model_evaluation.html

   • Scikit-learn Hyperparameter Tuning: https://scikit-learn.org/stable/modules/grid_search.html

   • Coursera's Machine Learning by Andrew Ng: https://www.coursera.org/learn/machine-learning

   • edX's Introduction to Machine Learning with Python: https://www.edx.org/course/machine-learning-with-python-a-practical-introduct

   • Microsoft ML For Beginners: https://github.com/microsoft/ML-For-Beginners

   • A curated list of Machine Learning frameworks, libraries and software: https://github.com/josephmisiti/awesome-machine-learning

7. Data Preprocessing:

   • Learn about data preprocessing techniques such as data cleaning, feature scaling, handling missing values, and data normalization to prepare data for machine learning models.
   • Towards Data Science - Data Cleaning and Preprocessing: https://medium.com/analytics-vidhya/data-cleaning-and-preprocessing-a4b751f4066f

8. Data Augmentation:

   • Understand data augmentation, a technique used to artificially expand the size of a training dataset by applying various transformations to existing data samples.
   • Data Augmentation for Image Data in Python: https://towardsdatascience.com/data-augmentation-for-deep-learning-4fe21d1a4eb9

9. Hands-on Projects:

   • Practice your skills with small machine learning projects using libraries like scikit-learn. Incorporate data preprocessing and data augmentation techniques in your projects.
   • GitHub Repositories and Kaggle Kernels offer a plethora of beginner-friendly ML projects to get you started.

Phase 2: Exploring Deep Learning

10. Neural Networks and Deep Learning:

    • Delve into the foundations of deep learning, including neural networks, activation functions, backpropagation, and optimization techniques.
    • Coursera's Deep Learning Specialization by Andrew Ng: https://www.coursera.org/specializations/deep-learning
    • Neural Networks and Deep Learning Book by Michael Nielsen: http://neuralnetworksanddeeplearning.com/
    • A curated list of Deep Learning tutorials, projects and communities: https://github.com/ChristosChristofidis/awesome-deep-learning

11. TensorFlow and Keras:

    • Learn how to work with deep learning frameworks like TensorFlow and Keras, which are widely used for building and training neural networks.
    • TensorFlow's Official Website: https://www.tensorflow.org/
    • Keras Documentation: https://keras.io/
    • TensorFlow Tutorials: https://www.tensorflow.org/tutorials
    • Keras Tutorials: https://keras.io/guides/

12. Image Processing Basics:

    • 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.
    • Pixel Operations: Learn basic pixel-level operations such as color manipulation, brightness adjustment, and thresholding.
    • Image Filtering: Study various image filtering techniques, including blurring, sharpening, edge detection, and noise reduction, using convolutional kernels.
    • Image Transforms: Explore image transformation techniques such as rotation, scaling, translation, and affine transformations to modify the spatial orientation of images.
    • Histogram Equalization: Understand histogram equalization to improve image contrast and enhance details in images.
    • Image Segmentation: Learn about image segmentation techniques to divide an image into meaningful regions or objects.
    • Morphological Operations: Study morphological operations like erosion and dilation for image processing tasks.
    • Image Compression: Understand image compression techniques to reduce the file size of images without significant loss of quality.
    • 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).

    Resources:

    • Digital Image Processing Book by Rafael C. Gonzalez and Richard E. Woods
    • OpenCV Documentation: https://docs.opencv.org/4.x/d9/df8/tutorial_root.html

13. Computer Vision Fundamentals:

    • Learn the basics of computer vision, including feature detection, image matching, and object recognition techniques.
    • Feature Detection and Matching: Understand feature detection algorithms like SIFT, SURF, and ORB and how to use them for image matching.
    • Object Detection: Study object detection techniques such as Haar cascades and deep learning-based approaches like YOLO and SSD.

14. Convolutional Neural Networks (CNNs) for Computer Vision:

    • Understand CNN architectures, transfer learning for image recognition, and object detection using CNNs.
    • Convolutional Neural Networks, Explained: https://towardsdatascience.com/convolutional-neural-networks-explained-9cc5188c4939
    • Fast.ai's Practical Deep Learning for Coders course: https://course.fast.ai/
    • Stanford's CS231n: Convolutional Neural Networks for Visual Recognition: http://cs231n.stanford.edu/

    Vision Transformers (ViT):

    • Vision Transformer is a powerful architecture for image recognition tasks that has gained significant attention in recent years.
    • An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929

15. Natural Language Processing:

    • Learn the basics of text preprocessing, tokenization, and language modeling.
    • Natural Language Toolkit (NLTK) Documentation: https://www.nltk.org/
    • Learn about RNNs and their applications in text generation and sentiment analysis.
    • Coursera's Natural Language Processing Specialization: https://www.coursera.org/specializations/natural-language-processing
    • The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy: http://karpathy.github.io/2015/05/21/rnn-effectiveness/

16. Transformers and Pre-trained Models:

    • Study transformer architectures like BERT and GPT, and learn to use pre-trained models for various NLP tasks.
    • Hugging Face's Transformers Library: https://huggingface.co/transformers/
    • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding: https://arxiv.org/abs/1810.04805
    • GPT-3: Language Models are Few-Shot Learners: https://arxiv.org/abs/2005.14165

17. Time Series Analysis:

    • Learn techniques for time series data preprocessing, modeling, and forecasting.
    • Coursera's Time Series Course: https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction?
    • Time Series Analysis: Forecasting and Control Book by George Box, Gwilym Jenkins, and Gregory Reinsel

18. Time Series Forecasting with Deep Learning:

    • Understand how to apply recurrent neural networks (RNNs) and LSTM models for time series forecasting.
    • TensorFlow Time Series Tutorial: https://www.tensorflow.org/tutorials/structured_data/time_series
    • Darts Python library documentation: https://unit8co.github.io/darts/README.html

19. Audio Processing and Speech Recognition:

    • Study audio signal processing, speech recognition, and speech-to-text applications.
    • Mozilla's Deep Learning for Audio and Speech: https://github.com/mozilla/DeepSpeech

Phase 3: Model Deployment and MLOps

20. Model Deployment:

    • Learn how to deploy machine learning models in production environments, including cloud platforms and edge devices.
    • Flask for API Development: https://flask.palletsprojects.com/
    • Deploying ML Models with TensorFlow Serving: https://www.tensorflow.org/tfx
    • Deploying ML Models with ONNX Runtime: https://onnxruntime.ai/

21. MLOps:

    • Understand the principles of MLOps and the best practices for managing the end-to-end machine learning lifecycle.
    • Continuous Integration and Continuous Deployment (CI/CD) for ML: https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build
    • A curated list of references for MLOps: https://github.com/visenger/awesome-mlops

22. Monitoring and Scaling ML Models:

    • Explore techniques for monitoring model performance and scaling ML systems.
    • TensorFlow Extended (TFX) Model Monitoring: https://www.tensorflow.org/tfx
    • Scaling Machine Learning at Uber with Michelangelo: https://eng.uber.com/scaling-michelangelo/

23. Model Versioning and Experiment Tracking:

    • Learn about version control for ML models and experiment tracking tools to manage model iterations effectively.
    • DVC for Machine Learning Versioning: https://dvc.org/
    • MLflow for Experiment Tracking: https://mlflow.org/

24. Deploying ML Models in the Cloud:

    • Understand cloud-based deployment options for machine learning models using platforms like AWS, GCP, and Azure.
    • AWS SageMaker: https://aws.amazon.com/sagemaker/
    • Google Cloud AI Platform: https://cloud.google.com/ai-platform
    • Microsoft Azure Machine Learning: https://azure.microsoft.com/en-us/services/machine-learning/

Phase 4: Advanced Deep Learning

25. Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) & Stable Diffusion:

    • Study GANs and VAEs, two powerful techniques in the domain of generative modeling.
    • Stable Diffusion: https://course.fast.ai/Lessons/lesson9.html
    • Generative Adversarial Networks (GANs) by Ian Goodfellow et al.: https://arxiv.org/abs/1406.2661
    • Auto-Encoding Variational Bayes (VAE) by Kingma and Welling: https://arxiv.org/abs/1312.6114

26. Transfer Learning and Model Fine-Tuning:

    • Learn how to leverage pre-trained models and fine-tune them for specific tasks.
    • Fast.ai's course on Transfer Learning: https://course.fast.ai/Lessons/lesson1.html

27. Advanced Topics and Research Papers:

    • Start reading research papers and exploring advanced topics in deep learning.
    • arXiv.org is a great resource for accessing research papers in the field: https://arxiv.org/
    • Google Scholar: https://scholar.google.com/

28. Contributing to Open Source Projects:

    • Contribute to open-source deep learning projects on GitHub. This will help you gain practical experience and collaborate with others in the community.
    • A Beginners Guide to Open Source: https://dev.to/arindam_1729/a-beginners-guide-to-open-source-4nc5

Phase 5: Real-world Projects and Specializations

29. Machine Learning Projects and Competitions:

    • Participate in Kaggle competitions and create real-world ML projects to build your portfolio.
    • Kaggle: https://www.kaggle.com/

30. Deep Learning Specializations:

    • Enroll in specialized deep learning courses and certifications to gain expertise in specific areas like computer vision, NLP, etc.
    • DeepLearning.AI's TensorFlow Developer Professional Certificate: https://www.coursera.org/professional-certificates/tensorflow-in-practice
    • Coursera's AI for Medicine Specialization: https://www.coursera.org/specializations/ai-for-medicine
    • DeepLearning.AI's Deep Learning Specialization: https://www.coursera.org/specializations/deep-learning

31. Research Internship or Master's Degree (Optional):

    • 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.

32. Joining ML/DL Communities and Conferences:

    • 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.

33. Building a Portfolio and Personal Projects:

    • Showcase your skills by creating a portfolio of your projects on GitHub and personal website.
    • Collaborate on open-source projects or create your own projects to solve real-world problems and demonstrate your expertise.

34. Continuous Learning and Staying Updated:

    • 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.