You can not have avoided having read about Machine Learning (ML) in the news, many so-called “experts” predict that it will change the world. Many organizations are jumping onboard machine learning, to get better insights from their organizational data, to be able to predict the future of the organization.
I’m going in the simplest way possible to me, clear up some misconceptions and explain the terms Artificial Intelligence (AI) and Machine Learning (ML), that often seem to be used interchangeably, they are not quite the same things. People have the perception that they are, and can sometimes lead to some confusion.
What are AI and ML
Let me give you the short answer:
Artificial Intelligence is the concept of machines being able to perform tasks, that in a way would be considered “smart” and are the characteristic of human intelligence. This is very general, the tasks include things like planning, understanding language, recognizing objects and sounds, learning, and problem-solving.
Machine Learning at its core, is a simple way of achieving Artificial Intelligence (AI), based on the idea that a machine would be able to learn without being programmed.
You can get AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees.
So instead of hard-coding software routines with specific instructions to accomplish a particular task, machine learning is a way of “training” an algorithm so that it can learn how. “Training” involves feeding huge amounts of data to the algorithm and allowing the algorithm to adjust itself and improve.
AI is nothing new, it has been talked about from the dawn of the computer age. It just being hyped in the last few years, as we start seeing solutions on real problems. I found this article interesting the most common AI myths.
With self-driving cars and ships, as well as life-saving medical advances, some really cool development is underway, companies are looking at adding voice-enabled interfaces to their existing point-and-click dashboards and systems. Natural language recognition will make computers better at understanding us and talking to us in a way we understand. Type of implementations will be Bots that uses Machine Learning to train and improve in naturally communicating with us.
More and more robots are developed for the health sector, these robots will initially be aids to the doctors for making better diagnostics, the robots will work more in the background, image recognition will be used to spot warning signs buried in medical images and even hand-written doctor’s’ notes.
The speed of technological change is only going to increase. Many CEOs and CTO, will initiate new projects with a focus on Machine Learning, the projects most likely to succeed are those which are envisaged from the start with a clear strategy, and with results clearly tied to bottom-line KPIs such as revenue growth and customer satisfaction scores.
How to get involved
How will you get into the exciting space of Data Scientist, ML, and AI, I have added a list of resource which you can get started with.
Depending on your level, there are some learning resources from beginners to experts to improve skills in ML and AI.
TensorFlow tutorials cover topics image recognition, machine learning problems dealing with sequence data and data representations that can be used in TensorFlow.
Pull your Twitter Stream, use the Google Natural Language API to understand the context of the data, and since our data is in Swedish (and the Natural Language API doesn’t support the Swedish language yet), the Google Natural Translate API is used to translate from Swedish to English. BigQuery – To collect a lot of data.
This GitHub repository is built up, on multiple contributions over the years with links to resources ranging from getting-started guides, infographics to people to follow on social networking sites like twitter, Facebook, Instagram, etc.
This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
This GitHub repository consists of the commonly used tools and techniques compiled in the form of cheat sheets. The cheat sheets range from very simple tools like pandas to techniques like Deep Learning. There are different types of cheat sheets for pandas, numpy, scikit learn, matplotlib, ggplot, dplyr, tidyr, pySpark and Neural Networks.
This GitHub repository based on Oxford NLP Lectures takes the education of NLP to the next level. A practical course, these lectures cover the techniques and terminologies to advance material such as using RNNs for Language Modeling, Speech Recognition, Text to Speech, etc. This repository is a one-stop-shop for all the materials of the Oxford Lectures providing Lecture materials to Practical assignments.
This GitHub repository contains codes for Deep Learning tasks ranging from learning basics of creating a Neural Network in PyTorch to coding RNNs, GANs and Neural Style Transfers. Most of the models have been implemented with as few as 30 lines of code.
CourseDuck has put together a list of the best machine learning courses & tutorials in 2020. CourseDuck makes finding the right course fast and easy.
Google Cloud’s AI provides modern machine learning services, with pre-trained models and service to generate your own tailored models.
Watson is the AI platform for business.
Open Source Softwares
Google TensorFlow is one of the top Machine Learning / Deep Learning libraries. TensorFlow is a complete library for building Deep Learning models. Although TensorFlow majorly supports Python, it also provides support for languages such as C, C++, Java and many more.
Turi Create simplifies the development of custom machine learning models. You don’t have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app. TuriCreate is developed especially for python. One of the best features that TuriCreate provides is its easy deployability of machine learning models to Core ML (another open source software by Apple)for use in iOS, macOS, watchOS, and tvOS apps
OpenPose represents the first real-time multi-person system to jointly detect a human body, hand, and facial key points (in total 130 key points) on single images. OpenPose has a C++ API that can be used to access the library. But it also has a simple command-line interface to process images or videos.
Project DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques, based on Baidu’s Deep Speech research paper. Project DeepSpeech uses Google’s TensorFlow project to make the implementation easier.
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.
A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more.
Here are some useful articles to deepen your understanding of Machine Learning and Artificial Intelligence:
- What Is The Difference Between Artificial Intelligence And Machine Learning?
- The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning
- The Key Differences Between Machine Learning and AI
I have listed resources that have been trending on GitHub. If you have seen more such useful repositories in the past. What’s your view on ML and AI, I like to hear from you, you could comment in the comment box below or contact me through the contact form.