Innovations have come to such extent where we are very used to machines and robots. We live in a digitalized world where our every task is performed via machines. We hardly do our tasks manually. Due to this, it is very easy for beginners to develop simple yet skill building projects in machines and machine learning. And, thus it provides the beginner hands-on practice in gaining proficiency and mastery over various networks and algorithms in machine learning.
In order to learn simple basic machine learning apps and programming, there are some ways that could guide to master in designing basic ML projects:
i.Classifying images:
The primary step of gaining in designing machine learning apps and learning image classification via ML is to follow "An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks". This provides with basic guidelines to users who have limited or no knowledge in Machine Learning and Artificial Intelligence. It teaches to build such classification network that distinguishes between the images of dolphins and seahorses. It guides step-by-step, right from setting up a network to its deployment.
ii.Classifying Sentences:
NLP (Natural Language Processing) has an interesting application which classifies sentences into categories such as negative or positive. This is also called sentiment analysis.
Categorization of sentence is very difficult, when the structure of sentences is formed complex. A open project "Convolutional Neural Network for Text Classification" aims to overcome such complex issues using CNN and build an efficient text-based classifier.
iii.Object Detection
Machine learning deep down makes a user learn about it more. There is a project named "Object detection with deep learning and OpenCV" which helps beginners in building an application that detecting objects and correctly identifies them. The combination of MobileNets and Single Shot Detectors allows to quickly detect objects, along with the use of DNN module of OpenCV to integrate detection network.
iv. Gaming Bots
Teaching bots to play games is a way in which deep reinforcement learning is being taken to an entirely new level. A unique architecture combining various current techniques, such as a Deep Q-Network for navigation and a Deep Recurrent Q-Network, for tracking opponent movements and predicting where to shoot, was created to train the bots. A bot built on deep reinforcement learning could perhaps do a better job than humans. The open project, "Using Deep Q-Network to Learn How To Play Flappy Bird" can help in build a bot using Deep Q-Network and beat the game.
v.Text Correction
The systems that check spellings, basically context-sensitive are widely used to correct errors in SMS messages, and emails, among others. These auto-correction systems are not very successful when it comes to correcting grammatical errors. "Deep Text Corrector" is one such project which aims to construct tools that are capable of correcting errors automatically via the training of sequence-to-sequence models. This helps in building a grammar correcting tool that can be trained using grammatically correct samples, and later small error ridden ones to enable production of input-output pairs.