AI technology is significants because it allows softwares to do human functions—understanding, reasoning, planning, communication, and perception—increasingly effectively, efficiently, and affordably.

1. Pandas:

   Data processing and analysis may be performed with the help of the Python programming language using the Pandas software package. In particular, it provides the data structures and procedures necessary for the manipulation of numerical tables and time series. It is open-source software distributed with a three-clause BSD license. The phrase "panel," which is used in econometrics, is the origin of the word "panel," which refers to data sets that comprise observations across several time periods for the same persons. Its name is a pun on the term "Python data analysis," which is also included in the name.

2. NumPy:

   Numpy is a library for the Python programming language that adds support for huge, multi-dimensional arrays and matrices, in addition to a large number of high-level mathematical functions that can be used to work on these arrays. Numpy was developed by the Python Software Foundation. The predecessor of NumPy, known as Numeric, was first developed by Jim Hugunin with assistance from a number of other software developers. In 2005, Travis Oliphant developed NumPy by integrating aspects of a competitor product called Numarray into Numeric and making a number of other changes. NumPy is software that is freely available to the public and has several contributors.

3. SciPy:

   Scipy is a Python library that is used for technical and scientific computing. It is open-source and free to use. SciPy has modules for things like optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and a variety of other tasks that are common in engineering and science.

4. Matplotlib:

   A charting library for the Python programming language and the NumPy extension for numerical mathematics, Matplotlib is part of the Python standard library. It offers an object-oriented application programming interface (API) for embedding plots into programs using general-purpose graphical user interface toolkits like Tkinter, wxPython, Qt, or GTK. There is also a procedural interface available that is referred to as "Pylab." This interface is built on a state machine (much like OpenGL) and is designed to seem very similarly to MATLAB. However, the usage of this interface is not advised.

5. Seaborn

   Python users looking to make statistical visuals will find Seaborn to be a useful module. It is developed on top of Matplotlib and is strongly linked with the PyData stack, providing support for Numpy and Pandas data structures as well as statistical methods from Scipy and StatsModels. It offers a high-level interface for the creation of statistical visuals that are both appealing and useful.

6. Scikit-learn:

   The machine learning package known as scikit-learn, previously known as scikit-learn and also known as sklearn, is available for free as part of the Python programming language. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy, and it comes equipped with a variety of classification, regression, and clustering algorithms. Some of these algorithms include support vector machines, random forests, gradient boosting, k-means, and DBSCAN.

7. TensorFlow:

   TensorFlow is a software library that is open-source and free to use. It is used for machine learning and artificial intelligence. It may be put to use for a wide variety of jobs, but the instruction and inference of deep neural networks is where its primary emphasis lies. The Google Brain team created TensorFlow for internal usage inside Google, specifically for use in research and production. 2015 saw the publication of the inaugural version, which was done so under the Apache License 2.0. TensorFlow 2.0 was the name given by Google to the revised version of TensorFlow that was published in September 2019.

8. Keras:

   Keras is a Python-based artificial neural network interface that is provided by an open-source software package known as Keras. The function of Keras is to provide an interface for the TensorFlow library. Keras supported a number of different backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML, up to version 2.3. TensorFlow is the sole framework that is supported as of version 2.4. It has a strong emphasis on being user-friendly, modular, and extendable, with the goal of facilitating rapid experimentation with deep neural networks.

9. PyTorch

   PyTorch is an open-source machine learning library that is based on the Torch library. It is used for applications like computer vision and natural language processing, and it was largely created by Facebook's Artificial Intelligence Research Lab (FAIR). It is open-source software that is offered under a license based on the Modified BSD. PyTorch has both a Python and a C++ interface, with the former being more refined and the latter being the major focus of development.

10. Spark from the Apache Project

    Apache Spark is a free and open-source unified analytics engine designed for handling enormous amounts of data. Spark offers a programming interface for complete clusters that has implicit data parallelism and fault tolerance built in. The Spark codebase was first created at the AMPLab located on the campus of the University of California, Berkeley. Subsequently, it was given to the Apache Software Foundation, which has been responsible for its maintenance ever since.

11. OpenCV

    OpenCV, which stands for "Open Source Computer Vision Library," is a collection of programming functions primarily geared at real-time computer vision. It was first created by Intel, and later on, Willow Garage and Itseez provided support for it (which was later acquired by Intel). The library is available for free under the open-source Apache 2 License and is compatible with several operating systems. GPU acceleration for real-time tasks was added to OpenCV in 2011, and it has been available ever since.

12. Beautiful Soup

    Python's Beautiful Soup is a library that can process XML and HTML pages (including having malformed markup, i.e. non-closed tags, so named after tag soup). It generates a parse tree for the pages that have been parsed, which can then be used to extract data from HTML. This is helpful for web scraping. The company was founded by Leonard Richardson. He is still contributing to the project, and he does it with the assistance of Tidelift, which is a paid subscription to open-source maintenance.

13. NLTK

    The Natural Language Toolkit, or NLTK for short, is a collection of Python-based libraries and programs for symbolic and statistical natural language processing (NLP) for the English language. These libraries and tools are intended for use with the English language. At the University of Pennsylvania's Department of Computer and Information Science, faculty members Steven Bird and Edward Loper were the ones who first invented it. The NLTK library has graphical demos as well as sample data.

14. spaCy

    Written in both Python and Cython, the open-source programming languages Python and Cython, SpaCy is a software library for sophisticated natural language processing. The library is distributed under the MIT license, and its primary developers are Matthew Honnibal and Ines Montani, who are also the founders of the software firm Explosion.

15. Plotly

    Plotly is a technical computer firm with headquarters in Montreal, Quebec, that specializes in the development of web tools for data analysis and visualization. People and organizations may use the online graphing, analytics, and statistics tools provided by Plotly. In addition to that, it comes with scientific graphing libraries for the languages Python, R, MATLAB, Perl, Julia, Arduino, and REST.

16. Gensim

    Gensim is an open-source package that uses contemporary statistical machine learning to do unsupervised topic modeling and natural language processing. Gensim was developed by IBM Research. Python and Cython are the programming languages used to build Gensim for optimal performance. The majority of other machine learning software packages are intended to work solely with in-memory processing, however Gensim is built to handle big text collections utilizing data streaming and incremental online algorithms. This sets it apart from the majority of its competitors.

17. Selenium

    The Selenium project is an open-source umbrella project that contains a variety of web browser automation-related tools and technologies. A test scripting language is not required to be learned in order to use the replay tool that Selenium offers for functional test creation (Selenium IDE). In addition to this, it offers a test domain-specific language known as Selenese that can be used to create tests in a variety of well-known programming languages. These languages include JavaScript (Node.js), C#, Groovy, Java, Perl, PHP, Python, Ruby, and Scala.

Credit: Mejbah Ahammad

• The Development Of Neural Networks
• Receptive Field in CNN
• Standard Gaussian Distribution - Modelling Nature
• Convolution - Math Driving The Computer Vision
• Half Order Derivatives
• Fourier Transforms For Image Processing
• Singular Value Decomposition — Diagnolization of Square Matrix
• Can you find Inverse of Rectangular Matrix? YES, Go through this
• Intuitively Understanding Convolutions for Deep Learning
• Image Segmentation - Basics From TensorFlow
• UNet — Line by Line Explanation
• U-Net: Convolutional Networks for Biomedical Image Segmentation
• ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing
• Deep CNN for Removal of Salt and Aepper Noise
• A noise robust convolutional neural network for image classification
• Xception: Deep Learning with Depthwise Separable Convolutions

• Advanced Guide to Inception v3
• Inception V3 - Keras Blog
• Deep Residual Learning for Image Recognition

• Machine Learning Cheatsheet — be used to with ML terms
• Deep Learning Book
• Basic Image Processing — learn basics of image processing for image-preprocessing.
• Xgboost with Different Categorical Encoding Methods
• Linear Regression | Lasso Regression | Ridge Regession — details of regression concepts with thoery and code.
• Magic Behind, Gaussian Naive Bias Classification Algorithm
• The Theory and Code Behind K-Nearest Neighbors
• Learn About Decision Trees — Working and Methods in Layman's Term With Code
• Get Used With Logistic Regression — With Code and Math Running Behind This Algorithm
• Various Kinds of Distances in Data Mining and Machine Learning
• Bayes' Theorem

• Chapter I Vectors
• Chapter II Linear combinations, span, and basis vectors
• Chapter III Linear transformations and matrices
• Chapter IV Matrix multiplication as composition
• Chapter V Three-dimensional linear transformations
• Chapter VI The determinant
• Chapter VII Inverse matrices, column space and null space
• Chapter VIII Nonsquare matrices as transformations between dimensions
• Chapter IX Dot products and duality
• Chapter X Cross products
• Chapter XI Cross products in the light of linear transformations
• Chapter XII Cramer's rule, explained geometrically
• Chapter XIII Change of basis
• Chapter XIV Eigenvectors and eigenvalues
• Chapter XV A quick trick for computing eigenvalues
• Chapter XVI Abstract vector spaces

• Chapter I The Essense of Calculus
• Chapter II The paradox of the derivative

• Radon Transformation
• Fourier Transform
• Hankel Transformation
• Cross Correlation - Generalized Projection of Function Into Reference Vector
• Autocorrelation
• Convolution
• Correlation
• Laplace Transformation
• Kullback–Leibler Divergence

• Creating Neural Network From Scratch — Step By Step With Pythonic Code
• Learn About Bayesian Deep Learning
• Learn Neural Networks and Deep Learning From Scratch — Theory
• Learn BERT — Bidirectional Encoder Representations from Transformers — state-of-art NLP model
• Generative Pre-trained Transformer 3 (GPT-3) — revolutionary NLP model — 515 times more powerful than BERT
• XGBoost Tutorials — Docs from the creater themselves

• ML Ops: Machine Learning as an Engineering Discipline
• Rules of Machine Learning : Best Practices for ML Engineering

• Regular Expression — Official Python Regex Module
• Learn Regex
• Regex Made Easy With Real Python
• Regular Expressions Demystified

• Numpy Basics
• Numpy Fundamentals

• Dive Into Python
• Learn About Python's Pathlib — No Really, Python's Pathlib is Great
• Python 101
• Object Oriented With Python — Wholesome Blog For Learning OOP with Python 3
• Code Refactoring for Software Engineering
• Guide to Python Design Patterns
• Popular Python Design Patterns - Explicitely Python
• Learn Python By Doing Python

• Writing Pythonic Code — Transforming from messy code to beautiful pythonic code
• Write More Pythonic Code
• PEP 8 -- Style Guide for Python Code
• The Hitchhiker’s Guide to Python!

• Article: Securely storing configuration credentials in a Jupyter Notebook
• Article: Automatically Reload Modules with %autoreload
• Calmcode: ipywidgets
• Documentation: Jupyter Lab
• Pluralsight: Getting Started with Jupyter Notebook and Python
• Youtube: William Horton - A Brief History of Jupyter Notebooks
• Youtube: I Like Notebooks
• Youtube: I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)
• Youtube: Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
• Youtube: nbdev live coding with Hamel Husain
• Youtube: How to Use JupyterLab

• Article: Stacking made easy with Sklearn
• Article: Curve Fitting With Python
• Article: A Guide to Calibration Plots in Python
• Calmcode: human-learn
• Datacamp: Supervised Learning with scikit-learn
• Datacamp: Machine Learning with Tree-Based Models in Python
• Datacamp: Introduction to Linear Modeling in Python
• Datacamp: Linear Classifiers in Python
• Datacamp: Generalized Linear Models in Python
• Notebook: scikit-learn tips
• Pluralsight: Building Machine Learning Models in Python with scikit-learn
• Video: human learn
• Youtube: dabl: Automatic Machine Learning with a Human in the Loop 00:25:43
• Youtube: Multilabel and Multioutput Classification -Machine Learning with TensorFlow & scikit-learn on Python
• Youtube: DABL: Automatic machine learning with a human in the loop- AI Latim American SumMIT Day 1

• Learn Basic EDA with Pandas
• Article: Modern Pandas
  • Modern Pandas | PART 1 | Introduction
  • Modern Pandas | PART 2 | Method Chaining
  • Modern Pandas | PART 3 | Indexes
  • Modern Pandas | PART 4 | Performance
  • Modern Pandas | PART 5 | Tidy
  • Modern Pandas | PART 6 | Visualization
  • Modern Pandas | PART 7 | Time Series
  • Modern Pandas | PART 8 | Scaling

• Coursera: Introduction to Tensorflow
• Coursera: Convolutional Neural Networks in TensorFlow
• Deeplizard: Keras - Python Deep Learning Neural Network API
• Book: Deep Learning with Python (Page: 276)
• Datacamp: Deep Learning in Python
• Datacamp: Convolutional Neural Networks for Image Processing
• Datacamp: Introduction to TensorFlow in Python
• Datacamp: Introduction to Deep Learning with Keras
• Datacamp: Advanced Deep Learning with Keras
• Google: Machine Learning Crash Course
• Pluralsight: Deep Learning with Keras
• Udacity: Intro to TensorFlow for Deep Learning

• Version Control Via Git
• A Sucessful Git Branching Model
• Git & Github Crash Course
• Everything About Git & Gitbash

• Weight And Biases

• Introduction
• Preliminaries

• Lecture 1 | Introduction to Convolutional Neural Networks
• Lecture 2 | Image Classification
• Lecture 3 | Loss Functions and Optimizations
• Lecture 4 | Introduction to Neural Networks
• Lecture 5 | Convolutional Neural Networks
• Lecture 6 | Training Neural Networks I
• Lecture 7 | Training Neural Networks II
• Lecture 8 | Deep Learning Software
• Lecture 9 | CNN Architectures
• Lecture 10 | Recurrent Neural Networks
• Lecture 11 | Detection and Segmentation
• Lecture 12 | Visualizing and Understanding
• Lecture 13 | Generative Models
• Lecture 14 | Deep Reinforcement Learning
• Lecture 15 | Efficient Methods and Hardware for Deep Learning
• Lecture 16 | Adversarial Examples and Adversarial Training

• Linear Algebra
• Singular Value Decomposition
• Basic Pattern Recognition
• Reduce The Dimesnion — PCA
• Guide To Kalman Filtering
• Fourtier Transforms

• Online Course offered by Duke University on Coursera
• Image Processing With Scipy and Numpy

• Linear Discriminant Analysis
• Probability, Bayes rule, Maximum Likelihood, MAP
• Mixtures and Expectation-Maximization Algorithm
• Introductory level Statistical Learning
• Hidden Markov Models
• Support Vector Machines
• Genetic Algorithms
• Bayesian Networks

• Topics Covered | Introduction
• A Brief History of Imaging | Image Sensing

• StatQuest: Machine Learning
  • StatQuest: Fitting a line to data, aka least squares, aka linear regression. 0:09:21
  • StatQuest: Linear Models Pt.1 - Linear Regression 0:27:26
  • StatQuest: StatQuest: Linear Models Pt.2 - t-tests and ANOVA 0:11:37
  • StatQuest: Odds and Log(Odds), Clearly Explained!!! 0:11:30
  • StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!! 0:16:20
  • StatQuest: Logistic Regression 0:08:47
  • Logistic Regression Details Pt1: Coefficients 0:19:02
  • Logistic Regression Details Pt 2: Maximum Likelihood 0:10:23
  • Logistic Regression Details Pt 3: R-squared and p-value 0:15:25
  • Saturated Models and Deviance 0:18:39
  • Deviance Residuals 0:06:18
  • Regularization Part 1: Ridge (L2) Regression 0:20:26
  • Regularization Part 2: Lasso (L1) Regression 0:08:19
  • Ridge vs Lasso Regression, Visualized!!! 0:09:05
  • Regularization Part 3: Elastic Net Regression 0:05:19
  • StatQuest: Principal Component Analysis (PCA), Step-by-Step 0:21:57
  • StatQuest: PCA main ideas in only 5 minutes!!! 0:06:04
  • StatQuest: PCA - Practical Tips 0:08:19
  • StatQuest: PCA in Python 0:11:37
  • StatQuest: Linear Discriminant Analysis (LDA) clearly explained. 0:15:12
  • StatQuest: MDS and PCoA 0:08:18
  • StatQuest: t-SNE, Clearly Explained 0:11:47
  • StatQuest: Hierarchical Clustering 0:11:19
  • StatQuest: K-means clustering 0:08:57
  • StatQuest: K-nearest neighbors, Clearly Explained 0:05:30
  • Naive Bayes, Clearly Explained!!! 0:15:12
  • Gaussian Naive Bayes, Clearly Explained!!! 0:09:41
  • StatQuest: Decision Trees 0:17:22
  • StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data 0:05:16
  • Regression Trees, Clearly Explained!!! 0:22:33
  • How to Prune Regression Trees, Clearly Explained!!! 0:16:15
  • StatQuest: Random Forests Part 1 - Building, Using and Evaluating 0:09:54
  • StatQuest: Random Forests Part 2: Missing data and clustering 0:11:53
  • The Chain Rule 0:18:23
  • Gradient Descent, Step-by-Step 0:23:54
  • Stochastic Gradient Descent, Clearly Explained!!! 0:10:53
  • AdaBoost, Clearly Explained 0:20:54
  • Gradient Boost Part 1: Regression Main Ideas 0:15:52
  • Gradient Boost Part 2: Regression Details 0:26:45
  • Gradient Boost Part 3: Classification 0:17:02
  • Gradient Boost Part 4: Classification Details 0:36:59
  • Support Vector Machines, Clearly Explained!!! 0:20:32
  • Support Vector Machines Part 2: The Polynomial Kernel 0:07:15
  • Support Vector Machines Part 3: The Radial (RBF) Kernel 0:15:52
  • XGBoost Part 1: Regression 0:25:46
  • XGBoost Part 2: Classification 0:25:17
  • XGBoost Part 3: Mathematical Details 0:27:24
  • XGBoost Part 4: Crazy Cool Optimizations 0:24:27
  • StatQuest: Fiitting a curve to data, aka lowess, aka loess 0:10:10
  • Statistics Fundamentals: Population Parameters 0:14:31
  • Principal Component Analysis (PCA) clearly explained (2015) 0:20:16
  • Decision Trees in Python from Start to Finish 1:06:23

• MIT 6S191 | Deep Learning Lectures

  • Lecture 1 | Indroduction to Deep Leaning
  • Lecture 2 | Recurrent Neural Networks
  • lecture 3 | Convolutional Neural Network
  • Lexture 4 | Deep Generative Models
  • Lecture 5 | Reinforcement Learning

• Machine Learning Engineering for Production (MLOps) Specialization — COURSERA SPECIALIZATION

  • Course I | Introduction to Machine Learning in Production
  • Course II | Machine Learning Data Lifecycle in Production
  • Course III | Machine Learning Modeling Pipelines in Production
  • Course IV | Deploying Machine Learning Models in Production

• MIT: 18.06 Linear Algebra (Professor Strang)

  • 1. The Geometry of Linear Equations 0:39:49
  • 2. Elimination with Matrices. 0:47:41
  • 3. Multiplication and Inverse Matrices 0:46:48
  • 4. Factorization into A = LU 0:48:05
  • 5. Transposes, Permutations, Spaces R^n 0:47:41
  • 6. Column Space and Nullspace 0:46:01
  • 9. Independence, Basis, and Dimension 0:50:14
  • 10. The Four Fundamental Subspaces 0:49:20
  • 11. Matrix Spaces; Rank 1; Small World Graphs 0:45:55
  • 14. Orthogonal Vectors and Subspaces 0:49:47
  • 15. Projections onto Subspaces 0:48:51
  • 16. Projection Matrices and Least Squares 0:48:05
  • 17. Orthogonal Matrices and Gram-Schmidt 0:49:09
  • 21. Eigenvalues and Eigenvectors 0:51:22
  • 22. Diagonalization and Powers of A 0:51:50
  • 24. Markov Matrices; Fourier Series 0:51:11
  • 25. Symmetric Matrices and Positive Definiteness 0:43:52
  • 27. Positive Definite Matrices and Minima 0:50:40
  • 29. Singular Value Decomposition 0:40:28
  • 30. Linear Transformations and Their Matrices 0:49:27
  • 31. Change of Basis; Image Compression 0:50:13
  • 33. Left and Right Inverses; Pseudoinverse 0:41:52

• Fast.ai: Deep Learning for Coder (2020)

  • Lesson 1
  • Lesson 2
  • Lesson 3
  • Lesson 4
  • Lesson 5
  • Lesson 6
  • Lesson 7
  • Lesson 8

• Full Stack Deep Learning

  • Setting Up Machine learning Projects
  • Infrastructure And Tooling
  • Data Management
  • Machine Learning Teams
  • Training And Debugging
  • Testing And Deployment

• CNN For Visual Recognition — cs231n

  • Lecture 1 | Introduction to Convolutional Neural Networks
  • Lecture 2 | Image Classification
  • lecture 3 | Loss Function and Optimization
  • lecture 4 | Introduction to Neural Networks
  • Lecture 5 | Convulutional Neural Network
  • Lecture 6 | Training Neural Network I
  • Lecture 7 | Training Neural Network II
  • Lecture 8 | Deep learning Software
  • Lecture 9 | CNN Architectures
  • Lecture 10 | Recurrent Neural Networks
  • Lecture 11 | Detection and Segmentation
  • Lecture 12 | Visualizing and Understanding
  • Lecture 13 | Generative Models
  • Lecture 14 | Deep Reinforcement Learning
  • Lecture 15 | Efficient Methods and Hardware for Deep Learning
  • Lecture 16 | Adversarial Examples and Adversarial Training

• Learn eXtreme Gradient Boosting - State-of-art ML Algorithm for Kaggle Contest till date.

  • Part I | Introduction and Regression with XGBoost
  • Part II | Classification with XGboost
  • Part III | Mathematical Details of XGBoost
  • Part IV | Cool Optimization with XGBoost

• The Twelve Factors
• Book "Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations", 2018 by Nicole Forsgren et.al
• Book "The DevOps Handbook" by Gene Kim, et al. 2016
• State of DevOps 2019
• Clean Code concepts adapted for machine learning and data science.
• School of SRE

• Machine Learning Operations: You Design It, You Train It, You Run It!
• MLOps SIG Specification
• ML in Production
• Awesome production machine learning: State of MLOps Tools and Frameworks
• Udemy “Deployment of ML Models”
• Full Stack Deep Learning
• Engineering best practices for Machine Learning
• 🚀 Putting ML in Production
• Stanford MLSys Seminar Series
• IBM ML Operationalization Starter Kit
• Productize ML. A self-study guide for Developers and Product Managers building Machine Learning products.
• MLOps (Machine Learning Operations) Fundamentals on GCP
• ML full Stack preparation
• Machine Learing Engineering in Production | DeepLearning AI

• AI Infrastructure for Everyone: DeterminedAI
• Deploying R Models with MLflow and Docker
• What Does it Mean to Deploy a Machine Learning Model?
• Software Interfaces for Machine Learning Deployment
• Batch Inference for Machine Learning Deployment
• AWS Cost Optimization for ML Infrastructure - EC2 spend
• CI/CD for Machine Learning & AI
• Itaú Unibanco: How we built a CI/CD Pipeline for machine learning with online training in Kubeflow
• 101 For Serving ML Models
• Deploying Machine Learning models to production — Inference service architecture patterns
• Serverless ML: Deploying Lightweight Models at Scale
• ML Model Rollout To Production. Part 1 | Part 2
• Deploying Python ML Models with Flask, Docker and Kubernetes
• Deploying Python ML Models with Bodywork

• Building dashboards for operational visibility (AWS)
• Monitoring Machine Learning Models in Production
• Effective testing for machine learning systems
• Unit Testing Data: What is it and how do you do it?
• How to Test Machine Learning Code and Systems (Accompanying code)
• Wu, T., Dong, Y., Dong, Z., Singa, A., Chen, X. and Zhang, Y., 2020. Testing Artificial Intelligence System Towards Safety and Robustness: State of the Art. IAENG International Journal of Computer Science, 47(3).
• Multi-Armed Bandits and the Stitch Fix Experimentation Platform
• A/B Testing Machine Learning Models
• Data validation for machine learning. Polyzotis, N., Zinkevich, M., Roy, S., Breck, E. and Whang, S., 2019. Proceedings of Machine Learning and Systems
• Testing machine learning based systems: a systematic mapping
• Explainable Monitoring: Stop flying blind and monitor your AI
• WhyLogs: Embrace Data Logging Across Your ML Systems
• Evidently AI. Insights on doing machine learning in production. (Vendor blog.)
• The definitive guide to comprehensively monitoring your AI
• Introduction to Unit Testing for Machine Learning
• Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance
• Test-Driven Development in MLOps Part 1

• MLOps Infrastructure Stack Canvas
• Rise of the Canonical Stack in Machine Learning. How a Dominant New Software Stack Will Unlock the Next Generation of Cutting Edge AI Apps
• AI Infrastructure Alliance. Building the canonical stack for AI/ML
• Linux Foundation AI Foundation
• ML Infrastructure Tools for Production | Part 1 — Production ML — The Final Stage of the Model Workflow | Part 2 — Model Deployment and Serving
• The MLOps Stack Template (by valohai)

• CS 10 - The Beauty and Joy of Computing - Spring 2015 - Dan Garcia - UC Berkeley InfoCoBuild
• 6.0001 - Introduction to Computer Science and Programming in Python - MIT OCW
• 6.001 - Structure and Interpretation of Computer Programs, MIT
• CS 50 - Introduction to Computer Science, Harvard University (cs50.tv)
• CS 61A - Structure and Interpretation of Computer Programs [Python], UC Berkeley
• CPSC 110 - Systematic Program Design [Racket], University of British Columbia
• CS50's Understanding Technology
• CSE 142 Computer Programming I (Java Programming), Spring 2016 - University of Washington
• CS 1301 Intro to computing - Gatech
• CS 106A - Programming Methodology, Stanford University (Lecture Videos)
• CS 106B - Programming Abstractions, Stanford University (Lecture Videos)
• CS 106X - Programming Abstractions in C++ (Lecture Videos)
• CS 107 - Programming Paradigms, Stanford University
• CmSc 150 - Introduction to Programming with Arcade Games, Simpson College
• LINFO 1104 - Paradigms of computer programming, Peter Van Roy, Université catholique de Louvain, Belgium - EdX
• FP 101x - Introduction to Functional Programming, TU Delft
• Introduction to Problem Solving and Programming - IIT Kanpur
• Introduction to programming in C - IIT Kanpur
• Programming in C++ - IIT Kharagpur
• Python Boot Camp Fall 2016 - Berkeley Institute for Data Science (BIDS)
• CS 101 - Introduction to Computer Science - Udacity
• 6.00SC - Introduction to Computer Science and Programming (Spring 2011) - MIT OCW
• 6.00 - Introduction to Computer Science and Programming (Fall 2008) - MIT OCW
• 6.01SC - Introduction to Electrical Engineering and Computer Science I - MIT OCW
• Modern C++ Course (2018) - Bonn University
• Modern C++ (Lecture & Tutorials, 2020, Vizzo & Stachniss) - University of Bonn

• Object Oriented Design
  • Object-oriented Program Design and Software Engineering - Aduni
  • OOSE - Object-Oriented Software Engineering, Dr. Tim Lethbridge
  • Object Oriented Systems Analysis and Design (Systems Analysis and Design in a Changing World)
  • CS 251 - Intermediate Software Design (C++ version) - Vanderbilt University
  • OOSE - Software Dev Using UML and Java
  • Object-Oriented Analysis and Design - IIT Kharagpur
  • CS3 - Design in Computing - Richard Buckland UNSW
  • Informatics 1 - Object-Oriented Programming 2014/15- University of Edinburgh
  • Software Engineering with Objects and Components 2015/16- University of Edinburgh
• Software Engineering
  • Computer Science 169- Software Engineering - Spring 2015 - UCBerkeley
  • CS 5150 - Software Engineering, Fall 2014 - Cornell University
  • Introduction to Service Design and Engineering - University of Trento, Italy
  • CS 164 Software Engineering - Harvard
  • System Analysis and Design - IISC Bangalore
  • Software Engineering - IIT Bombay
  • Dependable Systems (SS 2014)- HPI University of Potsdam
  • Software Testing - IIT Kharagpur
  • Informatics 2C - Software Engineering 2014/15- University of Edinburgh
• Software Architecture
  • CS 411 - Software Architecture Design - Bilkent University
  • MOOC - Software Architecture & Design - Udacity

• Efficient Estimation of Word Representations in Vector Space — Word2Vec
• eXtreme Gradient Boosting — A Scalable Tree Boosting System
• Paper: A Neural Probabilistic Language Model
• Paper: Efficient Estimation of Word Representations in Vector Space
• Paper: Sequence to Sequence Learning with Neural Networks
• Paper: Neural Machine Translation by Jointly Learning to Align and Translate
• Paper: Attention Is All You Need
• Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding
• Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
• Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering
• Paper: Collaborative Filtering for Implicit Feedback Datasets
• Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback
• Paper: Factorization Machines
• Paper: Wide & Deep Learning for Recommender Systems
• Paper: Multiword Expressions: A Pain in the Neck for NLP
• Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
• Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
• Paper: A Simple Framework for Contrastive Learning of Visual Representations
• Paper: Self-Supervised Learning of Pretext-Invariant Representations
• Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
• Paper: Self-Labelling via Simultalaneous Clustering and Representation Learning
• Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification
• Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
• Paper: Zero-shot Text Classification With Generative Language Models
• Paper: How to Fine-Tune BERT for Text Classification?
• Paper: Universal Sentence Encoder
• Paper: Enriching Word Vectors with Subword Information
• Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList
• Paper: Temporal Ensembling for Semi-Supervised Learning
• Paper: Boosting Self-Supervised Learning via Knowledge Transfer
• Paper: Follow-up Question Generation
• Paper: The Hardware Lottery
• Paper: Question Generation via Overgenerating Transformations and Ranking
• Paper: Good Question! Statistical Ranking for Question Generation
• Paper: Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
• Paper: Neural Text Generation: A Practical Guide
• Paper: Pest Management In Cotton Farms: An AI-System Case Study from the Global South
• Paper: BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
• Paper: On the surprising similarities between supervised and self-supervised models
• Paper: All-but-the-Top: Simple and Effective Postprocessing for Word Representations
• Paper: Simple and Effective Dimensionality Reduction for Word Embeddings
• Paper: AutoCompete: A Framework for Machine Learning Competitions
• Paper: Cost-effective Deployment of BERT Models in Serverless Environment
• Paper: Evaluating Large Language Models Trained on Code
• Paper: What Does BERT Learn about the Structure of Language?
• Paper: What do RNN Language Models Learn about Filler–Gap Dependencies?
• Paper: Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world
• Paper: MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
• Paper: Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs
• Paper: Show and Tell: A Neural Image Caption Generator
• Paper: The Curious Case of Neural Text Degeneration
• Paper: Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
• Paper : Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery
• Fine Tuning Unet For Ultrasound Image Segmentation

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• How to Write a Git Commit Message
• Awesome Git
• Git Cheatsheet

• A Tour of 10 Useful Github Features
• Automate your data science project structure in three easy steps
• Building a compelling Data Science Portfolio with writing
• My favorite tools for managing, organizing, and reading research papers

• Don’t just take notes — turn them into articles and share them with others-An interview with Alexey Grigorev, author of the book- Machine Learning Bookcamp
• You do not become better by employing fancy techniques but by working on the fundamentals
• Publishing Is Powerful as It Serves as a Catalyst for Scope and Writing Decisions

• Increasing the amount and diversity of data using scikit-image in Python
• Creating custom image datasets for Deep Learning projects
• Vegetation Index calculation from Satellite Imagery
• Face Detection with Python using OpenCV

• Visualizing Decision Trees with Pybaobabdt
• Render Interactive plots with Matplotlib
• Increase the cuteness quotient of your charts
• Create GitHub’s style contributions plot for your Time Series data
• A better way to visualize Decision Trees with the dtreeviz library
• Get Interactive plots directly with pandas
• Cluster Analysis in Tableau
• Quadrant Analysis in Tableau
• Visualizing large datasets with H2O
• 10 Free tools to get started with Data Visualisation-Easily & Instantly
• 5 ‘More’ Open Source tools to get started with Data Visualisation, easily
• Advanced plots in Matplotlib - Part 1
• Advanced plots in Matplotlib — Part 2
• Recreating Gapminder in Tableau: A Humble tribute to Hans Rosling

• Overcoming ImageNet dataset biases with PASS
• What you see is what you’ll get: Twitter’s new strategy for displaying Images on the timeline
• My favorite tools for managing, organizing, and reading research papers

• H2O AI Hybrid Cloud: Democratizing AI for every person and every organization
• Automate your Model Documentation using H2O AutoDoc
• A Deep dive into H2O’s AutoML

• The curious case of Simpson’s Paradox
• Reducing memory usage in pandas with smaller datatypes
• 5 Real World datasets for honing your Exploratory Data Analysis skills
• Getting started with Time Series using Pandas
• Awesome JupyterLab Extensions
• Import HTML tables into Google Sheets effortlessly
• Getting Datasets for Data Analysis tasks - Useful sites for finding datasets
• Getting Datasets for Data Analysis tasks — Advanced Google Search
• 10 Simple hacks to speed up your Data Analysis in Python

• Explain Your Machine Learning Model Predictions with GPU-Accelerated SHAP
• Interpretable or Accurate? Why not both?
• Shapley summary plots: the latest addition to the H2O.ai’s Explainability arsenal
• Interpretable Machine Learning

• From the game of Go to Kaggle: The story of a Kaggle Grandmaster from Taiwan
• What does it take to win a Kaggle competition? Let’s hear it from the winner himself
• What it takes to become a World No 1 on Kaggle
• Meet the Data Scientist who just cannot stop winning on Kaggle
• The inspiring journey of the ‘Beluga’ of Kaggle World 🐋
• Learning from others is imperative to success on Kaggle says this Turkish GrandMaster
• Getting ‘More’ out of your Kaggle Notebooks
• How a passion for numbers turned this Mechanical Engineer into a Kaggle Grandmaster
• Geek Girls Rising: Myth or Reality
• Meet Yauhen: The first and the only Kaggle Grandmaster from Belarus
• The Data Scientist who rules the ‘Data Science for Good’ competitions on Kaggle
• From Academia to Kaggle: How a Physicist found love in Data Science
• A Data Scientist’s journey from Sudoku to Kaggle

• From clipboard to DataFrame with Pandas
• Get Interactive plots directly with Pandas
• There is more to ‘pandas.read_csv()’ than meets the eye
• A hands-on guide to ‘sorting’ dataframes in Pandas
• Reducing memory usage in pandas with smaller datatypes
• Loading large datasets in Pandas
• Extracting information from XML files into a Pandas dataframe
• PandasGUI: Analyzing Pandas dataframes with a Graphical User Interface
• Beware of the Dummy variable trap in pandas
• Pandas Plot: Deep Dive Into Plotting Directly with Pandas

• Five wonderful uses of ‘f- Strings’ in Python
• Use Colab more efficiently with these hacks
• Enabling notifications in your Jupyter notebooks for cell completion
• Using Python’s datatable library seamlessly on Kaggle
• Basics of BASH for Beginners
• Useful pip commands for Data Science
• Getting more value from the Pandas’ value_counts()
• Speed up your Data Analysis with Python’s Datatable package
• Useful String Methods in Python
• Elements of Functional Programming in Python
• An Overview of Python’s Datatable package
• Python’s Collections Module — High-performance container data types

• Reviewing the TensorFlow Decision Forests library
• Tensors are all you need
• Five Open-Source Machine learning libraries worth checking out
• Understanding Decision Trees
• Alternative Python libraries for Data Science
• Demystifying Neural Networks: A Mathematical Approach (Part 1)
• Demystifying Neural Networks: A Mathematical Approach (Part 2)

• Reviewing the recently released HuggingFace 🤗 Course

• My Top Five Data Science Reads of 2018

• A quick guide to understanding Vectors Norms

• Analysis of Emotion Data: A Dataset for Emotion Recognition Tasks
• Building a Simple Chatbot from Scratch in Python (using NLTK)
• Simplifying Sentiment Analysis using VADER in Python (on Social Media Text)
• Free hands-on tutorials to get started in Natural Language Processing

• Programming with Databases in Python using SQLite
• SQL Interface within JupyterLab

• Visualizing India’s Seismic activity
• Interactive Geographical maps with GeoPandas

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