If you are a machine learning beginner and looking to finally get started Machine Learning Projects I would suggest first to go through A.I experiments by Google which you should not miss out.
Google Launches A.I. Experiments to Feature Machine Learning Projects
Google reported another site called A.I. Experiments that will work correspondingly to how the Android Experiments site functions. Along these lines, in the event that you’ve at any point needed to tinker with machine process, you can take a look at singular projects and see how they use the technology. They as of now have various projects up the present moment that show things like visualizing bird sounds, a game that will let the computer think about what you’re drawing, an application that takes things it sees and transforms them into lyrics of a tune, and the sky is the limit from there.
Each of these projects have a video to give you a demo demonstrating to you what it does. At that point, at the upper in that spot’s a catch for propelling the examination alongside a catch that takes you to the GitHub page. So you can get this open source code and begin looking through precisely how these engineers pulled it off. You would then be able to perceive what resources they used and start to implement this technology into a project of your own.
It’s an extremely energizing & best time to be an engineer now-a-days and we’re seeing this machine learning innovation utilized as a part of a wide range of regions like language, music, nature, and more. Google is finding more ways to use A.I. and neural networks to improve their services, and it’s only going to get more impressive as time goes by.
AI Tests is a grandstand for basic trials that make it less demanding for anybody to begin investigating machine learning, through pictures, drawings, language, music, and more.
Below is the List of 40 Fun Machine Learning Projects for Beginners.
1.) TEACHABLE MACHINE by Google Creative Lab
Teachable Machine is a trial that makes it less demanding for anybody to begin investigating how machine learning functions. It gives you a chance to educate a machine utilizing your camera – live in the program, no coding required. It’s worked with a library called deeplearn.js, which makes it less demanding for any web developer to get into machine learning, via preparing and running neural nets ideal in the program.
Access code Here: https://github.com/googlecreativelab/teachable-machine
2.) Quick, Draw! By Google Creative Lab
A game where a neural net tries to think about what you’re drawing.
This is a game built with machine learning. You draw, and a neural network tries to guess what you’re drawing. Obviously, it doesn’t generally work. Yet, the more you play with it, the more it will learn. It’s only one case of how you can utilize machine learning for fun only ways.
Worked by Jonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim, Nick Fox-Gieg, with companions at Google Creative Lab and Data Arts Team.
3.) ROCK-PAPER-SCISSORS MACHINE by Kaz Sato
A mid year science project in light of a great game.
Kaz Sato and his 12-year-old child constructed a machine that plays rock paper-scissors with you. It identifies your hand motions by utilizing a mix of sensors joined to a glove and an exceptionally basic machine learning calculation fueled by TensorFlow. At that point, the machine chooses the suitable hand posture to react with.
Access code Here: https://github.com/kazunori279/ml-misc/tree/master/glove-sensor
5.) FONT MAP by Kevin Ho
Utilizing machine learning to surface new relationships between fonts.
Text style choice is a standout amongst the most well-known visual decisions creators make—and most fall back on old top picks, or scan for a textual style inside classes. By utilizing machine learning and convolutional neural systems to spot visual examples, Text style Guide enables creators to comprehend and see connections crosswise over more than 750 web textual styles.
Worked by Kevin Ho utilizing Tensorflow and D3.js with the help of Tobias Toft, Jochen Maria Weber, and the plan group at IDEO.
6.) SKETCH-RNN DEMOS by Ha / Jongejan / Johnson
This investigation gives you a chance to draw together with an intermittent neural system show called Sketch-RNN. This neural net shows us to draw via preparing it on a great many doodles gathered from the Quick, Draw! game. When you begin drawing a question, Sketch-RNN will concoct numerous conceivable approaches to keep drawing this protest in view of the last known point of interest. The model can likewise impersonate your illustrations and deliver comparable doodles. It’s simply one more case of how you can utilize machine learning for the sake of fun and innovative ways.
7.) VISUALIZING HIGH-DIMENSIONAL SPACE by Smilkov / Viégas / Wattenberg
This trial gives you a look into how machine learning functions, by visualizing high-dimensional information. It’s accessible for anybody to attempt on the web. It is likewise publicly released as a component of TensorFlow, with the goal that coders can utilize these perception systems to investigate their own particular information.
Worked by Daniel Smilkov, Fernanda Viégas, Martin Wattenberg, and the Big Picture group at Google.
Access code Here: https://github.com/tensorflow/tensorflow
8.) NSYNTH: SOUND MAKER by Yotam Mann
This trial gives you a chance to play with new sounds made with machine learning. It’s built utilizing Nsynth, an exploration project that prepared a neural system on more than 300,000 instrument sounds. NSynth can consolidate sounds, similar to a bass and flute, into a new, hybrid bass-flute sound. This examination gives anybody a chance to investigate these sounds and make music with them.
Worked by the Maroon and Imaginative Lab groups at Google. NSynth is built utilizing Tensorflow, Tone.js and a Wavenet-style autoencoder.
Access code Here: https://github.com/googlecreativelab/aiexperiments-sound-maker
9.) THE INFINITE DRUM MACHINE by Manny Tan & Kyle McDonald
Sounds are intricate and fluctuate broadly. This trial utilizes machine learning to arrange a huge number of ordinary sounds. The PC wasn’t given any descriptions or labels – just the sound. Utilizing a procedure called t-SNE, the PC put comparative sounds nearer together. You can utilize the map to investigate neighborhoods of comparable sounds and even make beats utilizing the drum sequencer.
Worked by Kyle McDonald, Manny Tan, Yotam Mann, and companions at Google Imaginative Lab.
Access code Here: https://github.com/googlecreativelab/aiexperiments-drum-machine
10.) Objectifier Spatial Programming By Bjørn Karmann
Objectifier Spatial Programming (OSP) engages individuals to train objects in their day by day condition to react to their one of a kind practices. It gives an ordeal of training an artificial intelligence; a move from a detached consumer to a dynamic, fun loving executive of domestic innovation. Collaborating with Objectifier is much similar to training a dog – you show it just what you need it to think about. Much the same as a dog, it sees and comprehends its condition.
Worked by Bjørn Karmann utilizing RaspberryPi, OpenFrameworks, ML4A, Node.js, and P5.js. Along with: Ruben van der Vleuten, David A. Mellis, Francis Tseng, Patric Hebron and Andreas Refsgaard.
11.) Bird Sounds By Manny Tan & Kyle McDonald
Bird sounds fluctuate generally. This test utilizes machine learning to arrange a large number of bird sounds. The PC wasn’t given labels or the birds’ names – just the sound. Utilizing a method called t-SNE, the PC made this guide, where comparative sounds are set nearer together.
Worked by Kyle McDonald, Manny Tan, Yotam Mann, and companions at Google Inventive Lab. On account of Cornell Lab of Ornithology for their help. The sounds are accessible in the Macaulay Library’s Basic Set for North America.
Access code Here: https://github.com/googlecreativelab/aiexperiments-bird-sounds
12.) Handwriting with a Neural Net By Shan Carter, David Ha, Ian Johnson, Chris Olah
There’s a ton of enthusiasm for attempting to comprehend and visualize neural systems. This analysis gives you a chance to play with a neural network that can create strokes in light of your handwriting style. Through interactive visualizations, you can see and investigate how the neural net functions.
By Shan Carter, David Ha, Ian Johnson, and Chris Olah. The model was prepared utilizing Tensorflow.
Access code Here: https://github.com/distillpub/post–handwriting
13.) What Neural Networks See By Gene Kogan
This investigation gives you a chance to turn on your camera to investigate what neural nets see, live, utilizing your camera. Watch the video explainer above to perceive how each layer of the neural net works.
Worked by Quality Kogan as a feature of an accumulation of open-source OpenFrameworks applications.
Access code Here: https://github.com/ml4a/ml4a-ofx
14.) Giorgio Cam By Eric Rosenbaum & Yotam Mann
This is an examination worked with machine learning that gives you a chance to make music with the PC just by taking a photo. It utilizesimage recognition to name what it sees, at that point it transforms those labels into lyrics of a melody.
Worked by Eric Rosenbaum, Yotam Mann, and companions at Google Innovative Lab utilizing MaryTTS, Tone.js, and Google Cloud Vision Programming interface. Highlights music by Giorgio Moroder.
Access code Here: https://github.com/googlecreativelab/aiexperiments-giorgio-cam
15.) Thing Translator By Dan Motzenbecker
This examination gives you a chance to take a photo of a something how to state it in an alternate language. It’s only one case of what you can make utilizing Google’s machine learning API’s, without expecting to plunge into the points of interest of machine learning.
Worked by Dan Motzenbecker with companions at Google Innovative Lab, utilizing Google’s Cloud Vision Programming interface and Decipher Programming interface.
Access code Here: https://github.com/dmotz/thing-translator
16.) AI Duet By Yotam Mann
This trial gives you a chance to play a two part harmony with the PC. Simply play a few notes, and the PC will react to your song. You don’t need to know how to play piano—it’s enjoyable to simply squeeze some keys and tune in to what returns. You can tap the console, utilize your PC keys, or even module a MIDI console. It’s only one case of how machine learning can move individuals to be imaginative in new ways.
Worked by Yotam Mann with companions on the Magenta and Innovative Lab groups at Google. It’s worked with Tensorflow, Tone.js, and open-source devices from the Magenta undertaking.
Access code Here: https://github.com/googlecreativelab/aiexperiments-ai-duet
17.) AutoDraw By Google Creative Lab
AutoDraw is another sort of drawing instrument that combines the enchantment of machine learning with illustrations from gifted artists to help everybody make anything visual, quick.
Source: Google Developers Blog
Other Interesting Fun Projects you can do now which are popular:
18.) Stock Predictions
19.) Laptop Battery Life
20.) Time Series: Predict the Web Traffic
21.) Day 7: Temperature Predictions
22.) Basic Statistics Warmup
23.) Using a motion sensor and an Inertial Measurement Unit (IMU) to track proper sleep motion.
24.) Using a motion sensor and an Inertial Measurement Unit (IMU) to track posture while sitting at a desk.
25.) Using the Leap Motion to track how you are playing an instrument to improve performance
26.) Improving video game performance through neural nets
27.) Detecting Emotions through hand gestures
28.) Comparing human motion with robot and machine motion
29.) Creating test methods on how to improve teamwork between lacrosse players
30.) Synchronized swimming
31.) Sudden movements
32.) Allowing ML technologies to have Linux support
33.) Send information to wearable technologies
34.) A Virtual Piano that uses the Leap Motion sensor to generate notes based on finger movement and hand height.
35.) Using the Myo to access your computer without having to touch it
36.) Making sure people do not waste food
37.) Tracking mental disorders through motion and speech recognition
38.) Typing on a QWERTY keyboard
39.) Colorization techniques
40.) Satellite images to predict well-being of country etc