The list of things I've finished so far on the way of learning by myself Machine Learning and Data Science.

• My raw notes: rawnote.dinhanhthi.com (quickly capture ideas from the courses).
• My main notes: dinhanhthi.com/notes (well-written notes, not only for me).
• My learning log.

• Setting up a café in Ho Chi Minh City — find a best place to setting up a new business — article — source.
• Titanic: Machine Learning from Disaster (from Kaggle) — predicts which passengers survived the Titanic shipwreck — source.

I also do some mini-projects for understanding the concepts. You can find the html files (exported from the corresponding Jupyter Notebook files) and "Open in Colab" files for below mini projects here.

• Image compression using K-Means — source — Open in Colab — my note
• Example to understand the idea of PCA — source — Open in Colab.
• Image compression using PCA — source — Open in Colab.
• PCA without scikit-learn — source — Open in Colab.
• Face Recognition using SVM — source — Open in Colab.
• XOR problem using SVM to see the effect of gamma and C in the case of using RBF kernel — source — Open in Colab.

• Anomaly Detection. — my note
• Data Aggregation — my note
• Data Overview. — my note
• Data Visualization.
• Model evaluation.
• Preprocessing (texts, images, dates & times, structured data). — my note
• Testing. — my note
• Web Scraping.

• GraphQL — an open-source data query and manipulation language for APIs, and a runtime for fulfilling queries with existing data.
• Python — an interpreted, high-level, general-purpose programming language — my note.
• R — a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing.
• Scala — a general-purpose programming language providing support for functional programming and a strong static type system.
• SQL — a domain-specific language used in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management system.

• Apache Airflow — my note
• Docker — a set of platform as a service products that use OS-level virtualization to deliver software in packages called containers — my note
• Google Colab — a free cloud service, based on Jupyter Notebooks for machine-learning education and research — my note.
• Google Kubernetes
• Hadoop — a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation.
• Kaggle — an online community of data scientists and machine learners, owned by Google.
• PostgreSQL (Postgres) — a free and open-source relational database management system emphasizing extensibility and technical standards compliance.
• Spark — an open-source distributed general-purpose cluster-computing framework.

• Bash — my note
• Git — a distributed version-control system for tracking changes in source code during software development — my note.
• Markdown — a lightweight markup language with plain text formatting syntax — my note.
• Jupyter Notebook — an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text — my note.
• Trello — a web-based Kanban-style list-making application.

The "ticked" libraries don't mean that I've known/understand whole of them (but I can easily use them with their documentation)!

• D3js — a JavaScript library for producing dynamic, interactive data visualizations in web browsers.
• Keras — an open-source neural-network library written in Python.
• Matplotlib — a plotting library for the Python programming language and its numerical mathematics extension NumPy. — my note
• Numpy — a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. — my note
• OpenCV — a library of programming functions mainly aimed at real-time computer vision.
• Pandas — a software library written for the Python programming language for data manipulation and analysis. -- my note
• Plotly -- the front-end for ML and data science models.
• PyTorch -- my note
• Seaborn — a Python data visualization library based on matplotlib.
• Scikit-learn — a free software machine learning library for the Python programming language.
• TensorFlow — a free and open-source software library for dataflow and differentiable programming across a range of tasks.

The "non-checked" courses are under the way to be finished!

• Advanced Data Science with IBM Specialization on Coursera.
• Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization by Google Cloud Training on Coursera.
• Advanced Statistics for Data Science Specificaton by Johns Hopkins University on Coursera.
• Anomaly Detection in Time Series Data with Keras by Coursera Project Network. -- my certificate
• CS231n: Convolutional Neural Networks for Visual Recognition by Stanford.
• Data Science Path on Codecademy. It contains 27 sub-courses covering all necessary knowledge about data science — my certificate — notes & codes.
• Data Scientist path & Data Engineer path on Dataquest. Both of them contain many sub-courses covering all about Data Science — my note — my certificate
• Deep Learning Specialization by Andrew NG on Coursera. It contains 5 courses covering the foundations of Deep Learning (CNN, RNN, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization,...). Many case studies projects are proposed — my note -- my certificate.
• fast.ai's courses for Machine Learning and Deep Learning..
• IBM AI Engineering Professional Certificate on Coursera.
• IBM Data Professional Certificate specialization on Coursera. It contains 9 sub-courses covering fundamental knowledge about data science — my note — my certificate.
• Introduction to Statistics with NumPy on Codecademy — my certificate.
• Learn Python 3 on Codecademy — my note — my certificate.
• Learn SQL on Codecademy — my certificate.
• Machine Learning by Andew NG on Coursera. It introduces a general idea about ML and some commonly used algorithms — my note — my certificate.
• Machine Learning Crash Course by Google.
• Machine Learning with TensorFlow on Google Cloud Platform Specialization by Google Cloud Training on Coursera.
• MIT Deep Learning
• Natural Language Processing by HSE University on Coursera. -- my note -- my certificate
• Natural Language Processing Specialization by deeplearning.ai on Coursera.
• Shervine Amidi's courses about Machine Learning, Deep Learning, AI, Stats (Stanford University)
• TensorFlow: Data and Deployment Specialization by deeplearning.ai on Coursera.
• TensorFlow in Practice Specialization by deeplearning.ai on Coursera. -- mynote -- my cerficate.
• TensorFlow Tutorials.

The "non-checked" books are under the way to be finished!

• An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirami.
• Deep Learning with Python by François Chollet.
• Dive into Deep Learning — An interactive deep learning book with code, math, and discussions, based on the NumPy interface. — Github.
• Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd edition) by Aurélien Géron.
• Machine Learning Yearing by Andew NG.
• Practical Machine Learning: A New Look at Anomaly Detection -- Ted Dunning & Ellen Friedman
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman.

• Awesome's lists:
  • Awesome Anomaly Detection -- A curated list of awesome anomaly detection resources.
  • Awesome Big Data — A curated list of awesome big data frameworks, ressources and other awesomeness.
  • Awesome Data Engineering — A curated list of data engineering tools for software developers.
  • Awesome Deep Learning — A curated list of awesome Deep Learning tutorials, projects and communities.
  • Awesome Deep learning papers and other resources — Deep Learning and deep reinforcement learning research papers and some codes.
  • Awesome Machine Learning — A curated list of awesome Machine Learning frameworks, libraries and software.
  • Awesome Public Datasets — A topic-centric list of HQ open datasets.
• 120 Data Science Interview Questions — Answers to 120 commonly asked data science interview questions.
• A Machine Learning Course with Python — Machine Learning Course with Python. Refer to the course page for step-by-step explanations.
• Python Data Science Handbook — Python Data Science Handbook: full text in Jupyter Notebooks.
• Homemade Machine Learning — Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.
• TensorFlow-Course — Simple and ready-to-use tutorials for TensorFlow.
• Machine Learning & Deep Learning Tutorials — ML and DL tutorials, articles and other resources.
• 100-Days-Of-ML-Code.
• Data science blogs — A curated list of data science blogs.
• data-science-ipython-notebooks — DS Python notebooks: DL (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

• Papers With Code — a free and open resource with Machine Learning papers, code and evaluation tables.
• Chris Albon's notes — Notes On Using Data Science & Artificial Intelligence To Fight For Something That Matters.
• Seeing Theory — A visual introduction to probabilities and statistics.
• Collection of useful articles for understanding concepts in ML, AI and DS.

The descriptions of terms in this site are borrowed from Wikipedia.