- Machine Learning With Python
- Python Ecosystem
- Methods for Machine Learning
- Data Loading for ML Projects
- Understanding Data with Statistics
- Understanding Data with Visualization
- Preparing Data
- Data Feature Selection
- ML Algorithms - Classification
- Logistic Regression
- Support Vector Machine (SVM)
- Decision Tree
- Naïve Bayes
- Random Forest
- ML Algorithms - Regression
- Random Forest
- Linear Regression
- ML Algorithms - Clustering
- K-means Algorithm
- Mean Shift Algorithm
- Hierarchical Clustering
- ML Algorithms - KNN Algorithm
- Finding Nearest Neighbors
- Performance Metrics
- Automatic Workflows
- Improving Performance of ML Models
- Improving Performance of ML Model (Contd…)
- ML With Python - Resources
- Machine Learning With Python - Quick Guide
- Machine Learning with Python - Resources
- Machine Learning With Python - Discussion
Machine Learning with Python - Applications
Artificial Intelligence (AI) and Machine Learning are everywhere. Chances are that you are using them and not even aware about that. In Machine Learning (ML), computers, software, and devices perform via cognition similar to human brain.
Typical successful applications of machine learning include programs that decode handwritten text, face recognition, voice recognition, speech recognition, pattern recognition, spam detection programs, weather forecasting, stock market analysis and predictions, and so on. This chapter discusses these applications in detail.
Virtual Personal Assistants
Siri, Google Now, Alexa are some of the common examples of virtual personal assistants. These applications assist in finding information, when asked over voice. All that is needed is activating them and asking questions like for example “What are my appointments for today?”, “What are the flights from Delhi to New York”. For answering such queries, the application looks out for the information, recalls your previous queries, and accesses other resources to collect relevant information. You can even tell these assistants to do certain tasks like “Set an alarm for 5.30 AM next morning”, “Remind me to visit Passport office tomorrow at 10.30 am”.
Traffic Congestion Analysis and Predictions
GPS navigation services monitor the user’s location and velocities and use them to build a map of current traffic. This helps in preventing the traffic congestions. Machine learning in such scenarios helps to estimate the regions where congestion can be found based on previous records.
Automated Video Surveillance
Video surveillance systems nowadays are powered by AI and machine learning is the technology behind this that makes it possible to detect and prevent crimes before they occur. They track odd and suspicious behavior of people and sends alerts to human attendants, who can ultimately help accidents and crimes.
Facebook continuously monitors the friends that you connect with, your interests, workplace, or a group that you share with someone etc. Based on continuous learning, a list of Facebook users is given as friend suggestions.
You upload a picture of you with a friend and Facebook instantly recognizes that friend. Machine learning works at the core of Computer Vision, which is a technique to extract useful information from images and videos. Pinterest uses computer vision to identify objects or pins in the images and recommend similar pins to its users.
Email Spam and Malware Filtering
Machine learning is being extensively used in spam detection and malware filtering and the databases of such spams and malwares keep on getting updated so these are handled efficiently.
Online Customer Support
In several websites nowadays, there is an option to chat with customer support representative while users are navigating the site. In most of the cases, instead of a real executive, you talk to a chatbot. These bots extract information from the website and provide it to the customers to assist them. Over a period of time, the chatbots learn to understand the user queries better and serve them with better answers, and this is made possible by machine learning algorithms.
Refinement of Search Engine Results
Google and similar search engines are using machine learning to improve the search results for their users. Every time a search is executed, the algorithms at the backend keep a watch at how the users respond to the results. Depending on the user responses, the algorithms working at the backend improve the search results.
If a user purchases or searches for a product online, he/she keeps on receiving emails for shopping suggestions and ads about that product. Based on previous user behavior, on a website/app, past purchases, items liked or added to cart, brand preferences etc., the product recommendations are sent to the user.
Detection of Online frauds
Machine learning is used to track monetary frauds online. For example - Paypal is using ML to prevent money laundering. The company is using a set of tools that helps them compare millions of transactions and make a distinction between legal or illegal transactions taking place between the buyers and sellers.
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