ROHIT's Smart Class Room

 

  v   Some canonical learning problems: - Ø In machine learning, canonical learning problems are standard tasks that serve for developing and evaluating algorithms. Ø These canonical problems provide a framework for understanding and applying machine learning techniques across various domains. Ø These problems help in understanding the strengths and limitations of various approaches. Some of the most common canonical learning problems include:   1.      Classification : Assigning inputs to predefined categories. For example, determining whether an email is spam or not.   Binary Classification: trying to predict a simple yes/no response. For instance, predict whether a user review of the newest Apple product is positive or negative about the product.   Multiclass Classification: trying to put an example into one of a number of classes. For instance, predict whether a news story is about entertainment, sports, politics, re...

  v   Applications of Machine Learning: - 1.       Image Recognition 2.       Speech Recognition 3.       Traffic prediction 4.       Product recommendations 5.       Self-driving cars 6.       Email Spam and Malware Filtering 7.       Virtual Personal Assistant 8.       Online Fraud Detection 9.       Stock Market trading 10.   Medical Diagnosis 11.   Automatic Language Translation   1)   Image Recognition: - Ø Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is,  Automatic friend tagging suggestion .   Ø Facebook provides us a feature...

  v   Learning (or) working process of machine: - Ø A machine learning algorithm works by learning patterns and relationships from data to make predictions or decisions without being explicitly programmed for each task. Ø Learning process of machine learning algorithm works: 1.       Data Collection 2.       Data Preprocessing 3.       Choosing a right Model 4.       Training the Model 5.       Evaluating the Model 6.       Fine-tuning and Optimizing 7.       Prediction and Deployment   1)   Data Collection: - Ø First, relevant data is collected. Data can be collected from various sources such as databases, text files, numeric data, images, audio files, etc from the web. Ø This data could include examples, features, or attributes that are important for the task. Ø...