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Powered by GitBook
On this page
  • Everything You Need To Become A Machine Learner
  • Natural language processing
  • Be familiar with how Machine Learning is applied at other companies
  • Be able to frame anMachine Learning problem
  • Be familiar with data ethics
  • Be able to import data from multiple sources
  • Be able to setup data annotation efficiently
  • Be able to manipulate data with Numpy
  • Be able to manipulate data with Pandas
  • Be able to manipulate data in spreadsheets
  • Be able to manipulate data in databases
  • Be able to use Linux
  • Be able to perform feature selection and engineering
  • Be able to experiment in a notebook
  • Be able to visualize data
  • Be able to model problems mathematically
  • Be able to setup project structure
  • Be able to version control code
  • Be able to setup model validation
  • Be familiar with inner working of models
  • Be able to improve models
  • Be familiar with fundamental Machine Learning concepts
  • Implement models in scikit-learn
  • Be able to implement models in Tensorflow and Keras
  • Be able to implement models in PyTorch
  • Be able to implement models using cloud services

Was this helpful?

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Machine Learner

PreviousLibrariesNextHere’s the expanded list:

Last updated 3 years ago

Was this helpful?

This list of resources is specifically targeted at Web Developers and Data Scientists…. so do with it what you will…This list borrows…


Everything You Need To Become A Machine Learner

This list of resources is specifically targeted at Web Developers and Data Scientists…. so do with it what you will…This list borrows heavily from multiple lists created by :

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.

The goal of AI is to create computer models that exhibit “intelligent behaviors” like humans, according to , a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.

Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer as “the field of study that gives computers the ability to learn without explicitly being programmed.”

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Natural language processing

Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.### Neural networksNeural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.

In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.

Be familiar with how Machine Learning is applied at other companies

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Be able to frame anMachine Learning problem


Be familiar with data ethics

Be able to import data from multiple sources

Be able to setup data annotation efficiently


Be able to manipulate data with Numpy

Be able to manipulate data with Pandas

Be able to manipulate data in spreadsheets

Be able to manipulate data in databases

Be able to use Linux

Resources:


Be able to perform feature selection and engineering

Be able to experiment in a notebook

Be able to visualize data

Be able to model problems mathematically

Be able to setup project structure

Be able to version control code

Be able to setup model validation


Be familiar with inner working of models


Be able to improve models


Be familiar with fundamental Machine Learning concepts

Implement models in scikit-learn


Be able to implement models in Tensorflow and Keras

Be able to implement models in PyTorch


Be able to implement models using cloud services

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of learning bash

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***Bays theorem is super interesting and applicable ==> — \[📰\]***

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Exported from on August 31, 2021.

AWS: Types of Machine Learning Solutions
Apply Machine Learning to your Business
Resilience and Vibrancy: The 2020 Data & AI Landscape
Software 2.0
Highlights from ICML 2020
A Peek at Trends in Machine Learning
How to deliver on Machine Learning projects
Data Science as a Product
Customer service is full of machine learning problems
Choosing Problems in Data Science and Machine Learning
Why finance is deploying natural language processing
The Last 5 Years In Deep Learning
Always start with a stupid model, no exceptions.
Most impactful AI trends of 2018: the rise ofMachine Learning Engineering
Building machine learning products: a problem well-defined is a problem half-solved.
Simple considerations for simple people building fancy neural networks
Maximizing Business Impact with Machine Learning
AI Superpowers: China, Silicon Valley, and the New World Order
A Human’s Guide to Machine Intelligence
The Future Computed
Machine Learning Yearning by Andrew Ng
Prediction Machines: The Simple Economics of Artificial Intelligence
Building Machine Learning Powered Applications: Going from Idea to Product
Coursera: AI For Everyone
Data Science for Everyone
Machine Learning with the Experts: School Budgets
Machine Learning for Everyone
Data Science for Managers
Facebook: Field Guide to Machine Learning
Introduction to Machine Learning Problem Framing
Pluralsight: How to Think About Machine Learning Algorithms
State of AI Report 2020
Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019
Hugging Face, Transformers | NLP Research and Open Source | Interview with Julien Chaumond
Vincent Warmerdam — Playing by the Rules-Based-Systems | PyData Eindhoven 2020
Building intuitions before building models
How to Detect Bias in AI
Netflix: Coded Bias
Netflix: The Great Hack
Netflix: The Social Dilemma
Practical Data Ethics
Lesson 1: Disinformation
Lesson 2: Bias & Fairness
Lesson 3: Ethical Foundations & Practical Tools
Lesson 4: Privacy and surveillance
Lesson 4 continued: Privacy and surveillance
Lesson 5.1: The problem with metrics
Lesson 5.2: Our Ecosystem, Venture Capital, & Hypergrowth
Lesson 5.3: Losing the Forest for the Trees, guest lecture by Ali Alkhatib
Lesson 6: Algorithmic Colonialism, and Next Steps
Lecture 9: Ethics (Full Stack Deep Learning — Spring 2021)
SE4AI: Ethics and Fairness
SE4AI: Security
SE4AI: Safety
Docs: Beautiful Soup Documentation
Importing Data in Python (Part 2)
Web Scraping in Python
Create A Synthetic Image Dataset — The “What”, The “Why” and The “How”
We need Synthetic Data
Weak Supervision for Online Discussions
Machine Learning Infrastructure Tools for Data Preparation
Exploring the Role of Human Raters in Creating NLP Datasets
Inter-Annotator Agreement (IAA)
How to compute inter-rater reliability metrics (Cohen’s Kappa, Fleiss’s Kappa, Cronbach Alpha, Krippendorff Alpha, Scott’s Pi, Inter-class correlation) in Python
Snorkel: Dark Data and Machine Learning — Christopher Ré
Training a NER Model with Prodigy and Transfer Learning
Training a New Entity Type with Prodigy — annotation powered by active learning
ECCV 2020 WSL tutorial: 4. Human-in-the-loop annotations
Active Learning: Why Smart Labeling is the Future of Data Annotation | Alectio
Lecture 8: Data Management (Full Stack Deep Learning — Spring 2021)
Lab 6: Data Labeling (Full Stack Deep Learning — Spring 2021)
Lecture 6: Data Management
SE4AI: Data Quality
SE4AI: Data Programming and Intro to Big Data Processing
SE4AI: Managing and Processing Large Datasets
A Visual Intro to NumPy and Data Representation
Good practices with numpy random number generators
NumPy Illustrated: The Visual Guide to NumPy
NumPy Fundamentals for Data Science and Machine Learning
Intro to Python for Data Science
Pluralsight: Working with Multidimensional Data Using NumPy
Spreadsheet basics
Data Analysis with Spreadsheets
Intermediate Spreadsheets for Data Science
Pivot Tables with Spreadsheets
Data Visualization in Spreadsheets
Introduction to Statistics in Spreadsheets
Conditional Formatting in Spreadsheets
Marketing Analytics in Spreadsheets
Error and Uncertainty in Spreadsheets
edX: Analyzing and Visualizing Data with Excel
Codecademy: SQL Track
Intro to SQL for Data Science
Introduction to MongoDB in Python
Intermediate SQL
Exploratory Data Analysis in SQL
Joining Data in PostgreSQL
Querying with TransactSQL
Introduction to Databases in Python
Reporting in SQL
Applying SQL to Real-World Problems
Analyzing Business Data in SQL
Data-Driven Decision Making in SQL
Database Design
SQL for Data Analysis
Intro to relational database
Database Systems Concepts & Design
Bash Proficiency In Under 15 Minutes
Cheat sheet and in-depth explanations located below main article contents… The UNIX shell program interprets user…
These Are The Bash Shell Commands That Stand Between Me And Insanity
I will not profess to be a bash shell wizard… but I have managed to scour some pretty helpful little scripts from Stack…
Bash Commands That Save Me Time and Frustration
Here’s a list of bash commands that stand between me and insanity.
Life Saving Bash Scripts Part 2
I am not saying they’re in any way special compared with other bash scripts… but when I consider that you can never…
What Are Bash Aliases And Why Should You Be Using Them!
A Bash alias is a method of supplementing or overriding Bash commands with new ones. Bash aliases make it easy for…
BASH CHEAT SHEET
My Bash Cheatsheet Index:
holy grail
Streamline your projects using Makefile
Understand Linux Load Averages and Monitor Performance of Linux
Command-line Tools can be 235x Faster than your Hadoop Cluster
Calmcode: makefiles
Calmcode: entr
Codecademy: Learn the Command Line
Introduction to Shell for Data Science
Introduction to Bash Scripting
Data Processing in Shell
MIT: The Missing Semester of CS Education
Lecture 1: Course Overview + The Shell (2020)
Lecture 2: Shell Tools and Scripting (2020)
Lecture 3: Editors (vim) (2020)
Lecture 4: Data Wrangling (2020)
Lecture 5: Command-line Environment (2020)
Lecture 6: Version Control (git) (2020)
Lecture 7: Debugging and Profiling (2020)
Lecture 8: Metaprogramming (2020)
Lecture 9: Security and Cryptography (2020)
Lecture 10: Potpourri (2020)
Lecture 11: Q&A (2020)
Thoughtbot: Mastering the Shell
Thoughtbot: tmux
Linux Command Line Basics
Shell Workshop
Configuring Linux Web Servers
Web Bos: Command Line Power User
GNU Parallel
Tips for Advanced Feature Engineering
Preparing data for a machine learning model
Feature selection for a machine learning model
Learning from imbalanced data
Hacker’s Guide to Data Preparation for Machine Learning
Practical Guide to Handling Imbalanced Datasets
Analyzing Social Media Data in Python
Dimensionality Reduction in Python
Preprocessing for Machine Learning in Python
Data Types for Data Science
Cleaning Data in Python
Feature Engineering for Machine Learning in Python
Importing & Managing Financial Data in Python
Manipulating Time Series Data in Python
Working with Geospatial Data in Python
Analyzing IoT Data in Python
Dealing with Missing Data in Python
Exploratory Data Analysis in Python
edX: Data Science Essentials
Creating an Analytical Dataset
AppliedMachine Learning 2020–04 — Preprocessing
AppliedMachine Learning 2020–11 — Model Inspection and Feature Selection
Securely storing configuration credentials in a Jupyter Notebook
Automatically Reload Modules with %autoreload
Calmcode: ipywidgets
Documentation: Jupyter Lab
Pluralsight: Getting Started with Jupyter Notebook and Python
William Horton — A Brief History of Jupyter Notebooks
I Like Notebooks
I don’t like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)
Ryan Herr — After model.fit, before you deploy| JupyterCon 2020
nbdev live coding with Hamel Husain
How to Use JupyterLab
Creating a Catchier Word Cloud Presentation
Effectively Using Matplotlib
Introduction to Data Visualization with Python
Introduction to Seaborn
Introduction to Matplotlib
Intermediate Data Visualization with Seaborn
Visualizing Time Series Data in Python
Improving Your Data Visualizations in Python
Visualizing Geospatial Data in Python
Interactive Data Visualization with Bokeh
AppliedMachine Learning 2020–02 Visualization and matplotlib
3Blue1Brown: Essence of Calculus
The Essence of Calculus, Chapter 1
The paradox of the derivative | Essence of calculus, chapter 2
Derivative formulas through geometry | Essence of calculus, chapter 3
Visualizing the chain rule and product rule | Essence of calculus, chapter 4
What’s so special about Euler’s number e? | Essence of calculus, chapter 5
Implicit differentiation, what’s going on here? | Essence of calculus, chapter 6
Limits, L’Hôpital’s rule, and epsilon delta definitions | Essence of calculus, chapter 7
Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8
What does area have to do with slope? | Essence of calculus, chapter 9
Higher order derivatives | Essence of calculus, chapter 10
Taylor series | Essence of calculus, chapter 11
What they won’t teach you in calculus
3Blue1Brown: Essence of linear algebra
Vectors, what even are they? | Essence of linear algebra, chapter 1
Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2
Linear transformations and matrices | Essence of linear algebra, chapter 3
Matrix multiplication as composition | Essence of linear algebra, chapter 4
Three-dimensional linear transformations | Essence of linear algebra, chapter 5
The determinant | Essence of linear algebra, chapter 6
Inverse matrices, column space and null space | Essence of linear algebra, chapter 7
Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8
Dot products and duality | Essence of linear algebra, chapter 9
Cross products | Essence of linear algebra, Chapter 10
Cross products in the light of linear transformations | Essence of linear algebra chapter 11
Cramer’s rule, explained geometrically | Essence of linear algebra, chapter 12
Change of basis | Essence of linear algebra, chapter 13
Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14
Abstract vector spaces | Essence of linear algebra, chapter 15
3Blue1Brown: Neural networks
But what is a Neural Network? | Deep learning, chapter 1
Gradient descent, how neural networks learn | Deep learning, chapter 2
What is backpropagation really doing? | Deep learning, chapter 3
Backpropagation calculus | Deep learning, chapter 4
A Visual Tour of Backpropagation
Entropy, Cross Entropy, and KL Divergence
Interview Guide to Probability Distributions
Introduction to Linear Algebra for Applied Machine Learning with Python
Entropy of a probability distribution — in layman’s terms
KL Divergence — in layman’s terms
Probability Distributions
Relearning Matrices as Linear Functions
You Could Have Come Up With Eigenvectors — Here’s How
PageRank — How Eigenvectors Power the Algorithm Behind Google Search
Interactive Visualization of Why Eigenvectors Matter
Cross-Entropy and KL Divergence
Why Randomness Is Information?
Basic Probability Theory
Math You Need to Succeed InMachine Learning Interviews
Basics of Linear Algebra for Machine Learning
Introduction to Statistics in Python
Foundations of Probability in Python
Statistical Thinking in Python (Part 1)
Statistical Thinking in Python (Part 2)
Statistical Simulation in Python
edX: Essential Statistics for Data Analysis using Excel
Computational Linear Algebra for Coders
Khan Academy: Precalculus
Khan Academy: Probability
Khan Academy: Differential Calculus
Khan Academy: Multivariable Calculus
Khan Academy: Linear Algebra
MIT: 18.06 Linear Algebra (Professor Strang)
1. The Geometry of Linear Equations
2. Elimination with Matrices.
3. Multiplication and Inverse Matrices
4. Factorization into A = LU
5. Transposes, Permutations, Spaces R^n
6. Column Space and Nullspace
9. Independence, Basis, and Dimension
10. The Four Fundamental Subspaces
11. Matrix Spaces; Rank 1; Small World Graphs
14. Orthogonal Vectors and Subspaces
15. Projections onto Subspaces
16. Projection Matrices and Least Squares
17. Orthogonal Matrices and Gram-Schmidt
21. Eigenvalues and Eigenvectors
22. Diagonalization and Powers of A
24. Markov Matrices; Fourier Series
25. Symmetric Matrices and Positive Definiteness
27. Positive Definite Matrices and Minima
29. Singular Value Decomposition
30. Linear Transformations and Their Matrices
31. Change of Basis; Image Compression
33. Left and Right Inverses; Pseudoinverse
StatQuest: Statistics Fundamentals
StatQuest: Histograms, Clearly Explained
StatQuest: What is a statistical distribution?
StatQuest: The Normal Distribution, Clearly Explained!!!
Statistics Fundamentals: Population Parameters
Statistics Fundamentals: The Mean, Variance and Standard Deviation
StatQuest: What is a statistical model?
StatQuest: Sampling A Distribution
Hypothesis Testing and The Null Hypothesis
Alternative Hypotheses: Main Ideas!!!
p-values: What they are and how to interpret them
How to calculate p-values
p-hacking: What it is and how to avoid it!
Statistical Power, Clearly Explained!!!
Power Analysis, Clearly Explained!!!
Covariance and Correlation Part 1: Covariance
Covariance and Correlation Part 2: Pearson’s Correlation
StatQuest: R-squared explained
The Central Limit Theorem
StatQuickie: Standard Deviation vs Standard Error
StatQuest: The standard error
StatQuest: Technical and Biological Replicates
StatQuest — Sample Size and Effective Sample Size, Clearly Explained
Bar Charts Are Better than Pie Charts
StatQuest: Boxplots, Clearly Explained
StatQuest: Logs (logarithms), clearly explained
StatQuest: Confidence Intervals
StatQuickie: Thresholds for Significance
StatQuickie: Which t test to use
StatQuest: One or Two Tailed P-Values
The Binomial Distribution and Test, Clearly Explained!!!
StatQuest: Quantiles and Percentiles, Clearly Explained!!!
StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explained
StatQuest: Quantile Normalization
StatQuest: Probability vs Likelihood
StatQuest: Maximum Likelihood, clearly explained!!!
Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0
Why Dividing By N Underestimates the Variance
Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!
Maximum Likelihood For the Normal Distribution, step-by-step!
StatQuest: Odds and Log(Odds), Clearly Explained!!!
StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
Live 2020–04–20!!! Expected Values
Eigenvectors and Eigenvalues
Linear Algebra Refresher
Statistics
Intro to Descriptive Statistics
Intro to Inferential Statistics
pydantic
Organizing machine learning projects: project management guidelines
Logging and Debugging in Machine Learning — How to use Python debugger and the logging module to find errors in your AI application
Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation
Configuring Google Colab Like A Pro
Stop using print, start using loguru in Python
Hypermodern Python
Hypermodern Python Chapter 2: Testing
Hypermodern Python Chapter 3: Linting
Hypermodern Python Chapter 4: Typing
Hypermodern Python Chapter 5: Documentation
Hypermodern Python Chapter 6: CI/CD
Push and pull: when and why to update your dependencies
Reproducible and upgradable Conda environments: dependency management with conda-lock
Options for packaging your Python code: Wheels, Conda, Docker, and more
Making model training scripts robust to spot interruptions
Calmcode: logging
Calmcode: tqdm
Calmcode: virtualenv
Coursera: Structuring Machine Learning Projects
Doc: Python Lifecycle Training
Introduction to Data Engineering
Conda Essentials
Conda for Building & Distributing Packages
Software Engineering for Data Scientists in Python
Designing Machine Learning Workflows in Python
Object-Oriented Programming in Python
Command Line Automation in Python
Creating Robust Python Workflows
Developing Python Packages
Treehouse: Object Oriented Python
Treehouse: Setup Local Python Environment
Writing READMEs
Lecture 1: Introduction to Deep Learning
Lecture 2: Setting Up Machine Learning Projects
Lecture 3: Introduction to the Text Recognizer Project
Lecture 4: Infrastructure and Tooling
Hydra configuration
Continuous integration
Data Engineering +Machine Learning + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16
OO Design and Testing Patterns for Machine Learning with Chris Gerpheide
Tutorial: Sebastian Witowski — Modern Python Developer’s Toolkit
Lecture 13:Machine Learning Teams (Full Stack Deep Learning — Spring 2021)
Lecture 5:Machine Learning Projects (Full Stack Deep Learning — Spring 2021)
Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning — Spring 2021)
Understanding Git (A Beginners Guide Containing Cheat Sheets & Resources) _Basic Git Work Flow._levelup.gitconnected.com
Github Repositories That Will Teach You How To Code For Free! _Update: here’s a repo full of helpful repos:_levelup.gitconnected.com
Mastering Git Stash Workflow
How to Become a Master of Git Tags
How to track large files in Github / Bitbucket? Git LFS to the rescue
Keep your git directory clean with git clean and git trash
Codecademy: Learn Git
Code School: Git Real
Introduction to Git for Data Science
Thoughtbot: Mastering Git
GitHub & Collaboration
How to Use Git and GitHub
Version Control with Git
045 Introduction to Git LFS
Git & Scripting
Evaluating a machine learning model
Validating your Machine Learning Model
Measuring Performance: AUPRC and Average Precision
Measuring Performance: AUC (AUROC)
Measuring Performance: The Confusion Matrix
Measuring Performance: Accuracy
ROC Curves: Intuition Through Visualization
Precision, Recall, Accuracy, and F1 Score for Multi-Label Classification
The Complete Guide to AUC and Average Precision: Simulations and Visualizations
Best Use of Train/Val/Test Splits, with Tips for Medical Data
The correct way to evaluate online machine learning models
Proxy Metrics
Accuracy as a Failure
AppliedMachine Learning 2020–09 — Model Evaluation and Metrics
Machine Learning Fundamentals: Cross Validation
Machine Learning Fundamentals: The Confusion Matrix
Machine Learning Fundamentals: Sensitivity and Specificity
Machine Learning Fundamentals: Bias and Variance
ROC and AUC, Clearly Explained!
Linear regression
Polynomial regression
Logistic regression
Decision trees
K-nearest neighbors
Support Vector Machines
Random forests
Boosted trees
Hacker’s Guide to Fundamental Machine Learning Algorithms with Python
Neural networks: activation functions
Neural networks: training with backpropagation
Neural Network from scratch-part 1
Neural Network from scratch-part 2
Perceptron to Deep-Neural-Network
One-vs-Rest strategy for Multi-Class Classification
Multi-class Classification — One-vs-All & One-vs-One
One-vs-Rest and One-vs-One for Multi-Class Classification
Deep Learning Algorithms — The Complete Guide
Machine Learning Techniques Primer
AWS: Understanding Neural Networks
Grokking Deep Learning
Make Your Own Neural Network
Coursera: Neural Networks and Deep Learning
Extreme Gradient Boosting with XGBoost
Ensemble Methods in Python
StatQuest: Machine Learning
StatQuest: Fitting a line to data, aka least squares, aka linear regression.
StatQuest: Linear Models Pt.1 — Linear Regression
StatQuest: StatQuest: Linear Models Pt.2 — t-tests and ANOVA
StatQuest: Odds and Log(Odds), Clearly Explained!!!
StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
StatQuest: Logistic Regression
Logistic Regression Details Pt1: Coefficients
Logistic Regression Details Pt 2: Maximum Likelihood
Logistic Regression Details Pt 3: R-squared and p-value
Saturated Models and Deviance
Deviance Residuals
Regularization Part 1: Ridge (L2) Regression
Regularization Part 2: Lasso (L1) Regression
Ridge vs Lasso Regression, Visualized!!!
Regularization Part 3: Elastic Net Regression
StatQuest: Principal Component Analysis (PCA), Step-by-Step
StatQuest: PCA main ideas in only 5 minutes!!!
StatQuest: PCA — Practical Tips
StatQuest: PCA in Python
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
StatQuest: MDS and PCoA
StatQuest: t-SNE, Clearly Explained
StatQuest: Hierarchical Clustering
StatQuest: K-means clustering
StatQuest: K-nearest neighbors, Clearly Explained
Naive Bayes, Clearly Explained!!!
Gaussian Naive Bayes, Clearly Explained!!!
StatQuest: Decision Trees
StatQuest: Decision Trees, Part 2 — Feature Selection and Missing Data
Regression Trees, Clearly Explained!!!
How to Prune Regression Trees, Clearly Explained!!!
StatQuest: Random Forests Part 1 — Building, Using and Evaluating
StatQuest: Random Forests Part 2: Missing data and clustering
The Chain Rule
Gradient Descent, Step-by-Step
Stochastic Gradient Descent, Clearly Explained!!!
AdaBoost, Clearly Explained
Part 1: Regression Main Ideas
Part 2: Regression Details
Part 3: Classification
Part 4: Classification Details
Support Vector Machines, Clearly Explained!!!
Support Vector Machines Part 2: The Polynomial Kernel
Support Vector Machines Part 3: The Radial (RBF) Kernel
XGBoost Part 1: Regression
XGBoost Part 2: Classification
XGBoost Part 3: Mathematical Details
XGBoost Part 4: Crazy Cool Optimizations
StatQuest: Fiitting a curve to data, aka lowess, aka loess
Statistics Fundamentals: Population Parameters
Principal Component Analysis (PCA) clearly explained (2015)
Decision Trees in Python from Start to Finish
Classification Models
Neural Networks from Scratch in Python
Neural Networks from Scratch — P.1 Intro and Neuron Code
Neural Networks from Scratch — P.2 Coding a Layer
Neural Networks from Scratch — P.3 The Dot Product
Neural Networks from Scratch — P.4 Batches, Layers, and Objects
Neural Networks from Scratch — P.5 Hidden Layer Activation Functions
AppliedMachine Learning 2020–03 Supervised learning and model validation
AppliedMachine Learning 2020–05 — Linear Models for Regression
AppliedMachine Learning 2020–06 — Linear Models for Classification
AppliedMachine Learning 2020–07 — Decision Trees and Random Forests
AppliedMachine Learning 2020–08 — Gradient Boosting
AppliedMachine Learning 2020–18 — Neural Networks
AppliedMachine Learning 2020–12 — AutoML (plus some feature selection)
Deep neural networks: preventing overfitting
Normalizing your data (specifically, input and batch normalization)
Batch Normalization
Are Deep Neural Networks Dramatically Overfitted?
In-layer normalization techniques for training very deep neural networks
Label Smoothing Explained using Microsoft Excel
Uncertainty Quantification Part 4: Leveraging Dropout in Neural Networks (CNNs)
Simple Ways to Tackle Class Imbalance
AppliedMachine Learning 2020–10 — Calibration, Imbalanced data
Lecture 10: Troubleshooting Deep Neural Networks
CNN
Connections: Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks
MLE and MAP — in layman’s terms
An overview of gradient descent optimization algorithms
Optimization for Deep Learning Highlights in 2017
Gradient descent
Setting the learning rate of your neural network
Cross-entropy for classification
Dismantling Neural Networks to Understand the Inner Workings with Math and Pytorch
AI Fundamentals
Foundations of Predictive Analytics in Python (Part 1)
Foundations of Predictive Analytics in Python (Part 2)
Elements of AI
edX: Principles of Machine Learning
edX: Data Science Essentials
Fast.ai: Deep Learning for Coder (2020)
Lesson 0
Lesson 1
Lesson 2
Lesson 3
Lesson 4
Lesson 5
Lesson 6
Lesson 7
Lesson 8
Deep Double Descent
Stacking made easy with Sklearn
Curve Fitting With Python
A Guide to Calibration Plots in Python
Calmcode: human-learn
Supervised Learning with scikit-learn
Machine Learning with Tree-Based Models in Python
Introduction to Linear Modeling in Python
Linear Classifiers in Python
Generalized Linear Models in Python
Notebook: scikit-learn tips
Pluralsight: Building Machine Learning Models in Python with scikit-learn
Video: human learn
dabl: Automatic Machine Learning with a Human in the Loop
Multilabel and Multioutput Classification -Machine Learning with TensorFlow & scikit-learn on Python
DABL: Automatic machine learning with a human in the loop- AI Latim American SumMIT Day 1
Coursera: Introduction to Tensorflow
Coursera: Convolutional Neural Networks in TensorFlow
Deeplizard: Keras — Python Deep Learning Neural Network API
Deep Learning with Python (Page: 276)
Deep Learning in Python
Convolutional Neural Networks for Image Processing
Introduction to TensorFlow in Python
Introduction to Deep Learning with Keras
Advanced Deep Learning with Keras
Machine Learning Crash Course
Pluralsight: Deep Learning with Keras
Intro to TensorFlow for Deep Learning
Keeping Up with PyTorch Lightning and Hydra
The One PyTorch Trick Which You Should Know
How does automatic differentiation really work?
7 Tips To Maximize PyTorch Performance
An introduction to PyTorch Lightning with comparisons to PyTorch
Converting From Keras To PyTorch Lightning
From PyTorch to PyTorch Lightning — A gentle introduction
Introducing PyTorch Lightning Sharded: Train SOTA Models, With Half The Memory
Sharded: A New Technique To Double The Size Of PyTorch Models
Understanding Bidirectional RNN in PyTorch
A developer-friendly guide to mixed precision training with PyTorch
A developer-friendly guide to model pruning in PyTorch
A developer-friendly guide to model quantization with PyTorch
Tricks for training PyTorch models to convergence more quickly
PyTorch Lightning Bolts — From Linear, Logistic Regression on TPUs to pre-trained GANs
Scaling Logistic Regression Via Multi-GPU/TPU Training
Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups
PyTorch Lightning 0.9 — synced BatchNorm, DataModules and final API!
PyTorch Lightning: Metrics
PyTorch Multi-GPU Metrics Library and More in PyTorch Lightning 0.8.1
Distributed model training in PyTorch using DistributedDataParallel
Distributed model training in PyTorch using DistributedDataParallel
EINSUM IS ALL YOU NEED — EINSTEIN SUMMATION IN DEEP LEARNING
Faster Deep Learning Training with PyTorch — a 2021 Guide
Fit More and Train Faster With ZeRO via DeepSpeed and FairScale
PyTorch Lightning V1.2.0- DeepSpeed, Pruning, Quantization, SWA
But what are PyTorch DataLoaders really?
Using PyTorch + NumPy? You’re making a mistake.
How Wadhwani AI Uses PyTorch To Empower Cotton Farmers
Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health
How to Build a Streaming DataLoader with PyTorch
Transform your ML-model to Pytorch with Hummingbird
PyTorch Loss Functions: The Ultimate Guide
Pad pack sequences for Pytorch batch processing with DataLoader
Model Parallelism
Notebook: Tensor Arithmetic
Notebook: Autograd
Notebook: Optimization
Notebook: Network modules
Notebook: Datasets and Dataloaders
Documentation: Pytorch Lightning
Introduction to Deep Learning with PyTorch
Deeplizard: Neural Network Programming — Deep Learning with PyTorch
Intro to Deep Learning with PyTorch
PyTorch Lightning 101
Training a classification model on MNIST with PyTorch
From PyTorch to PyTorch Lightning
Lightning Data Modules
PyTorch Dropout, Batch size and interactive debugging
Episode 4: Implementing a PyTorch Trainer: PyTorch Lightning Trainer and callbacks under-the-hood
SimCLR with PyTorch Lightning
PyTorch Performance Tuning Guide
Skin Cancer Detection with PyTorch
[PART 1] Skin Cancer Detection with PyTorch
[PART 2] Skin Cancer Detection with PyTorch
[PART 3] Skin Cancer Detection with PyTorch
Learn with Lightning
PyTorch Tutorial — RNN & LSTM & GRU — Recurren Neural Nets
Pytorch Zero to All
PyTorch Lecture 01: Overview
PyTorch Lecture 02: Linear Model
PyTorch Lecture 03: Gradient Descent
PyTorch Lecture 04: Back-propagation and Autograd
PyTorch Lecture 05: Linear Regression in the PyTorch way
PyTorch Lecture 06: Logistic Regression
PyTorch Lecture 07: Wide and Deep
PyTorch Lecture 08: PyTorch DataLoader
PyTorch Lecture 09: Softmax Classifier
PyTorch Lecture 10: Basic CNN
PyTorch Lecture 11: Advanced CNN
PyTorch Lecture 12: RNN1 — Basics
PyTorch Lecture 13: RNN 2 — Classification
PyTorch Developer Day 2020 | Full Livestream
Lightning Chat: How a Grandmaster Won a Kaggle Competition Using Pytorch Lightning
Production Inference Deployment with PyTorch
What is Automatic Differentiation?
JAX: accelerated machine learning research via composable function transformations in Python
AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription
AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker
AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications
AWS: Hands-on Rekognition: Automated Video Editing
AWS: Introduction to Amazon Comprehend
AWS: Introduction to Amazon Comprehend Medical
AWS: Introduction to Amazon Elastic Inference
AWS: Introduction to Amazon Forecast
AWS: Introduction to Amazon Lex
AWS: Introduction to Amazon Personalize
AWS: Introduction to Amazon Polly
AWS: Introduction to Amazon SageMaker Ground Truth
AWS: Introduction to Amazon SageMaker Neo
AWS: Introduction to Amazon Transcribe
AWS: Introduction to Amazon Translate
AWS: Introduction to AWS Marketplace — Machine Learning Category
AWS: Machine Learning Exam Basics
AWS: Neural Machine Translation with Sockeye
AWS: Process Model: CRISP-DM on the AWS Stack
AWS: Satellite Image Classification in SageMaker
Introduction to AWS Boto in Python
edX: Amazon SageMaker: Simplifying Machine Learning Application Development
Bryan Guner
August 30, 2021
Canonical link
Medium
sindresorhus
Boris Katz
Arthur Samuel
Delivering Happiness
Good to Great: Why Some Companies Make the Leap…And Others Don’t
Hello, Startup: A Programmer’s Guide to Building Products, Technologies, and Teams
How Google Works
Learn to Earn: A Beginner’s Guide to the Basics of Investing and Business
Rework
The Airbnb Story
The Personal MBA
Facebook: Digital marketing: get started
Facebook: Digital marketing: go further
Google Analytics for Beginners
Moz: The Beginner’s Guide to SEO
Smartly: Marketing Fundamentals
Treehouse: SEO Basics
App Monetization
App Marketing
How to Build a Startup
How Facebook uses super-efficient AI models to detect hate speech
Recent Advances in Google Translate
Cannes: HowMachine Learning saves us $1.7M a year on document previews
Machine Learning @ Monzo in 2020
How image search works at Dropbox
Real-world AI Case Studies
Andrej Karpathy on AI at Tesla (Full Stack Deep Learning — August 2018)
Jai Ranganathan at Data Science at Uber (Full Stack Deep Learning — August 2018)
John Apostolopoulos of Cisco discusses “Machine Learning in Networking”
Joaquin Candela, Director of Applied Machine Learning , Facebook in conversation with Esteban Arcaute
Eric Colson, Chief Algorithms Officer, Stitch Fix
Claudia Perlich, Advisor to Dstillery and Adjunct Professor NYU Stern School of Business
Jeff Dean, Google Senior Fellow and SVP Google AI — Deep Learning to Solve Challenging Problems
James Parr, Director of Frontier Development Lab (NASA), FDL Europe & CEO, Trillium Technologies
Daphne Koller, Founder & CEO of Insitro — In Conversation with Carlos Bustamante
Eric Horvitz, Microsoft Research — AI in the Open World: Advances, Aspirations, and Rough Edges
Tony Jebara, Netflix — Machine Learning for Recommendation and Personalization
Analyzing Police Activity with pandas
HR Analytics in Python: Predicting Employee Churn
Predicting Customer Churn in Python
How does YouTube recommend videos? — AI EXPLAINED!
How does Google Translate’s AI work?
Data Science in Finance
The Age of AI
How Far is Too Far? | The Age of A.I.
Healed through A.I. | The Age of A.I.
Using A.I. to build a better human | The Age of A.I.
Love, art and stories: decoded | The Age of A.I.
The ‘Space Architects’ of Mars | The Age of A.I.
Will a robot take my job? | The Age of A.I.
Saving the world one algorithm at a time | The Age of A.I.
How A.I. is searching for Aliens | The Age of A.I.
Using Intent Data to Optimize the Self-Solve Experience
Trillions of Questions, No Easy Answers: A (home) movie about how Google Search works
Google Machine Learning System Design Mock Interview
Netflix Machine Learning Mock Interview: Type-ahead Search
Machine Learning design: Search engine for Q&A
Engineering Systems for Real-Time Predictions @DoorDash
How Gmail Uses Iterative Design, Machine Learning and AI to Create More Assistive Features
Wayfair Data Science Explains It All: Human-in-the-loop Systems
Leaving the lab: Building NLP applications that real people can use
Machine Learning at Uber (Natural Language Processing Use Cases)
Google Wave: Natural Language Processing
Natural Language Understanding in Alexa
The Machine Learning Behind Alexa’s AI Systems
Ines Montani Keynote — Applied NLP Thinking
Lecture 9: Lukas Biewald
Lecture 13: Research Directions
Lecture 14: Jeremy Howard
Lecture 15: Richard Socher
Machine learning across industries with Vicki Boykis
Rachael Tatman — Conversational A.I. and Linguistics
Nicolas Koumchatzky — Machine Learning in Production for Self Driving Cars
Brandon Rohrer — Machine Learning in Production for Robots
[CVPR’21 WAD] Keynote — Andrej Karpathy, Tesla
Visualizing Pandas’ Pivoting and Reshaping Functions
A Gentle Visual Intro to Data Analysis in Python Using Pandas
Comprehensive Guide to Grouping and Aggregating with Pandas
8 Python Pandas Value_counts() tricks that make your work more efficient
pandas Foundations
Pandas Joins for Spreadsheet Users
Manipulating DataFrames with pandas
Merging DataFrames with pandas
Data Manipulation with pandas
Optimizing Python Code with pandas
Streamlined Data Ingestion with pandas
Analyzing Marketing Campaigns with pandas
edX: Implementing Predictive Analytics with Spark in Azure HDInsight
Modern Pandas
Modern Pandas (Part 1)
Modern Pandas (Part 2)
Modern Pandas (Part 3)
Modern Pandas (Part 4)
Modern Pandas (Part 5)
Modern Pandas (Part 6)
Modern Pandas (Part 7)
Modern Pandas (Part 8)
Naive Bayes classification