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…


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 : sindresorhus

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 Boris Katz, 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 Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.”

- \[📖\] 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 - \[🅤\]ꭏ App Monetization - \[🅤\]ꭏ App Marketing - \[🅤\]ꭏ How to Build a Startup


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


Be able to frame anMachine Learning problem

· [ ] 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

· [G] 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


Be familiar with data ethics

Be able to import data from multiple sources

Be able to setup data annotation efficiently

[📰] Create A Synthetic Image Dataset — The “What”, The “Why” and The “How”


Be able to manipulate data with Numpy

Be able to manipulate data with Pandas

- **\[📰\]** 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)

Be able to manipulate data in spreadsheets

Be able to manipulate data in databases

Be able to use Linux

Resources:

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 of learning bash

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

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

Be able to setup model validation


Be familiar with inner working of models

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


Be able to improve models


Be familiar with fundamental Machine Learning concepts

CNN

[ ] 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

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

By Bryan Guner on August 30, 2021.

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Exported from Medium on August 31, 2021.

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