Podcast
Podcast
Podcast
Something went wrong
Questions and Answers
Questions and Answers
Machine Learning (ML) is a subset of which field?
Machine Learning (ML) is a subset of which field?
- Computer Programming
- Statistical Analysis
- Data Science
- Artificial Intelligence (AI) (correct)
ML algorithms require explicit programming for every specific scenario they encounter.
ML algorithms require explicit programming for every specific scenario they encounter.
False (B)
In the context of spam filters, what is one key difference between traditional programming and using machine learning?
In the context of spam filters, what is one key difference between traditional programming and using machine learning?
Traditional programming requires manual identification and rule-writing for spam patterns, while machine learning algorithms automatically learn these patterns.
Traditional spam filters identify spam patterns ______.
Traditional spam filters identify spam patterns ______.
Match the following steps to their correct order in the traditional programming technique for problem-solving:
Match the following steps to their correct order in the traditional programming technique for problem-solving:
Which of the following is a key advantage of using machine learning (ML) in spam filters?
Which of the following is a key advantage of using machine learning (ML) in spam filters?
In a machine learning technique, evaluation occurs before training the ML algorithm.
In a machine learning technique, evaluation occurs before training the ML algorithm.
Describe the role of 'data' in the machine learning problem-solving technique.
Describe the role of 'data' in the machine learning problem-solving technique.
The ability of machine learning techniques to adapt to changes can be largely ______.
The ability of machine learning techniques to adapt to changes can be largely ______.
Match the following applications with the appropriate Machine Learning tool mentioned:
Match the following applications with the appropriate Machine Learning tool mentioned:
Which of the following techniques is used for analyzing images on a production line to classify products?
Which of the following techniques is used for analyzing images on a production line to classify products?
RNNs, CNNs, and Transformers are not suitable tools for speech recognition tasks.
RNNs, CNNs, and Transformers are not suitable tools for speech recognition tasks.
Name three tools that can be used for forecasting in predictive analytics.
Name three tools that can be used for forecasting in predictive analytics.
______ is/are used for creating a chatbot or personal assistant.
______ is/are used for creating a chatbot or personal assistant.
Match each Machine Learning type to their sub components:
Match each Machine Learning type to their sub components:
Which of the following machine learning types involves learning from labeled data?
Which of the following machine learning types involves learning from labeled data?
In unsupervised learning, the training data includes the desired solutions or labels.
In unsupervised learning, the training data includes the desired solutions or labels.
What is the main goal in supervised learning?
What is the main goal in supervised learning?
The term ______ refers to set of training examples where the desired output signals are already known.
The term ______ refers to set of training examples where the desired output signals are already known.
Match the following algorithms to the type of supervised learning:
Match the following algorithms to the type of supervised learning:
Questions and Answers
Something went wrong
Flashcards
Flashcards
Machine Learning (ML)
Machine Learning (ML)
Machine Learning (ML) is a subset of Artificial Intelligence (AI) focused on building systems that learn from data and improve automatically through experience without being explicitly programmed.
Traditional Spam Filter
Traditional Spam Filter
In traditional programming for spam filters, patterns are identified manually, rules are written for each, and these rules are continually tested, updated, and maintained, which can be complex and hard to manage.
ML-Based Spam Filters
ML-Based Spam Filters
ML-based spam filters automatically learn patterns by analyzing both spam and normal emails, accurately detecting new patterns, making them shorter, easier to maintain, and more accurate.
Computer Vision
Computer Vision
Signup and view all the flashcards
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs)
Signup and view all the flashcards
Predictive Analytics
Predictive Analytics
Signup and view all the flashcards
Regression
Regression
Signup and view all the flashcards
Natural Language Processing (NLP)
Natural Language Processing (NLP)
Signup and view all the flashcards
RNNs and Transformers
RNNs and Transformers
Signup and view all the flashcards
Supervised Learning
Supervised Learning
Signup and view all the flashcards
Labels (Supervised Learning)
Labels (Supervised Learning)
Signup and view all the flashcards
Supervised Learning Algorithms
Supervised Learning Algorithms
Signup and view all the flashcards
Unsupervised Learning
Unsupervised Learning
Signup and view all the flashcards
Clustering
Clustering
Signup and view all the flashcards
Flashcards
Something went wrong
Study Notes
Study Notes
Machine Learning Overview
- Machine Learning (ML) is a subset of Artificial Intelligence (AI).
- ML focuses on building systems that learn from data.
- ML algorithms improve automatically through experience, without explicit programming.
Traditional Programming vs. ML for Spam Filters
- Traditional programming identifies spam patterns manually, such as specific words.
- Rules are written for each pattern, and the rules are tested, updated and maintained continually.
- Traditional approach is complex and hard to maintain.
- ML-based spam filters automatically learn patterns by analyzing spam and normal emails.
- ML systems can detect new patterns, even with character replacements.
- ML systems are shorter, easier to maintain, and more accurate.
Traditional Technique
- The traditional technique involves studying the problem, after this, the system writes rules.
- Next the solution is evaluated and if the solution is not satisfactory the errors are analysed.
Machine Learning Technique
- The machine learning technique involves studying the problem, then using data to train ML algorithms.
- Next the solution is evaluated and if the solution is not satisfactory the errors are analysed.
Machine Learning - Adapting to Change
- Machine learning adapts to new data and is automatically updated in response to changes.
Applications of Machine Learning
- These include Computer Vision, Predictive Analysis, and Natural Language Processing (NLP).
Computer Vision
- It can be used to analyze images to classify products using image classification, and convolutional neural networks (CNNs).
- It can be used to detect tumors in brain scans, using semantic segmentation, and CNNs.
Predictive Analytics
- Useful for forecasting.
- It uses regression for predicting continuous values, including linear regression, polynomial regression, and support vector machines.
- Other tools include random forest regression, artificial neural networks (ANNs), recurrent neural networks (RNNs), CNNs, and transformers.
Natural Language Processing (NLP)
- NLP is useful for automatically classifying news articles, using text classification, recurrent neural networks (RNNs), CNNs, or transformers.
- NLP is useful for summarizing long documents, using text summarization, and transformers.
- NLP is useful for creating a chatbot or personal assistant, using natural language understanding (NLU), question answering, and a combination of NLP modules.
- Speech recognition for voice commands can use speech recognition, RNNs, CNNs, or transformers for audio sequence processing.
Types of Machine Learning Systems
- Supervised learning algorithms include linear regression, logistic regression, support vector machines (SVMs), decision trees, random forests and neural networks.
- Unsupervised learning algorithms include clustering, visualization and dimensionality reduction.
- Reinforcement learning algorithms include game AI, and robot navigation.
Supervised Learning
- Supervised learning involves training algorithms on datasets that include desired solutions, called labels.
- The goal is to learn a model from labeled training data to make predictions about unseen data.
- The term "supervised" refers to a set of training examples (data inputs) where the desired output signals or labels are already known.
- Supervised learning models the relationship between data inputs and labels.
- Supervised learning can be thought of as "label learning".
- The most important supervised learning algorithms are K-Nearest Neighbors, Linear Regression, Logistic Regression, Support Vector Machines (SVMs), Decision Trees and Random Forests, and Neural networks.
Supervised Learning Process
- Training data and labels are supplied to a machine learning algorithm, which generates a predictive model, that is eventually used to generate predicted labels.
Unsupervised Learning
- Unsupervised learning uses training sets that are unlabeled.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Study Notes
Something went wrong