3_Applications of ML.pptx bfdgvxchnbvvv.
- 1. Example applications of ML • Machine Learning technology has widely changed the lifestyle of human beings as we are highly dependent on this technology. • It is the subset of Artificial Intelligence, and we all use it knowingly or unknowingly. • For example, we use Google Assistant to employ ML concepts, we take help from online customer support, which is also an example of machine learning, and many more. • Machine Learning uses statistical techniques to make a computer more intelligent. It helps fetch entire business data and utilize it automatically as per requirement. • Today, companies use Machine Learning to improve business decisions, increase productivity, detect disease, forecast weather, and do many more things.
- 2. • There are so many example applications of Machine Learning in the real world, which are as follows: 1. Image Recognition 2. Speech Recognition 3. Recommender Systems 4. Fraud Detection 5. Self Driving Cars 6. Medical Diagnosis 7. Stock Market Trading 8. Language Translation 9. Traffic Alerts 10. Social Media Features
- 3. 1. Image Recognition What It Is: Identifying objects, people, or patterns in images. How Machine Learning Is Used: • ML models are trained on large datasets of labeled images to detect features like faces, objects, or scenes. • Used for facial recognition, object detection, and image classification. Machine learning algorithms are used for image recognition: • CNNs (Convolutional Neural Networks): Find patterns like shapes and objects in pictures. • KNN (K-Nearest Neighbors): Compares an image to others and finds the closest match. Example: Facebook: Automatically tagging friends in photos using facial recognition.
- 4. 2. Speech Recognition What It Is: Converting spoken language into text. How Machine Learning Is Used: • ML models process audio signals to recognize words, accents, and context. • Used in virtual assistants, transcription services, and voice commands. Machine learning algorithms are used for Speech Recognition: • Deep Neural Networks (DNN): Improves speech recognition by learning patterns in sound. • Recurrent Neural Networks (RNN): Helps understand speech over time, considering past sounds. Example: • Google Assistant: Responding to voice commands like “What’s the weather today?”
- 5. 3. Recommendation Systems What It Is: Suggesting items based on user behavior and preferences. How Machine Learning Is Used: • ML algorithms analyze user data (e.g., browsing history, ratings) to predict and recommend relevant items. • Collaborative filtering, content-based filtering, and hybrid approaches are common. Machine learning algorithms are used for Recommendation Systems: • Collaborative Filtering Recommends items based on what similar users liked or what similar items have been liked. Used by platforms like Netflix and Amazon. • Deep learning uses complex models to understand patterns in user data. Great for large, complex data. Examples: • Movie recommendation systems (like Netflix), • Product recommendations (like Amazon), and • Content suggestions (like Google Search, YouTube).
- 7. 4. Fraud Detection What It Is: Identifying suspicious or fraudulent activities. How Machine Learning Is Used: • ML models detect unusual patterns or anomalies in transaction data. • Models are trained on historical fraud data to flag potentially fraudulent transactions. Machine learning algorithms are used for Fraud Detection: • Logistic Regression: Helps detect if a transaction is normal or fraudulent. • Decision Trees: Classifies transactions by checking multiple conditions. Example: Banks: Flagging a credit card transaction for review if it deviates from usual spending behavior.
- 8. 5. Self Driving Cars What It Is: Vehicles navigating roads without human intervention. How Machine Learning Is Used: • ML processes data from sensors and cameras to detect objects, predict traffic movement, and make driving decisions. • Reinforcement learning helps vehicles learn optimal driving strategies. Machine learning algorithms are used for Fraud Detection: • Convolutional Neural Networks (CNNs): Helps cars recognize objects like pedestrians, vehicles, and traffic signs from camera images. • Reinforcement Learning (RL): Teaches cars to make decisions, like stopping or turning, by learning from trial and error. Example: • Tesla Autopilot: Assisting in lane changes, parking, and
- 9. 6. Medical Diagnosis What It Is: Assisting in detecting and diagnosing diseases. How Machine Learning Is Used: • ML models analyze patient data, medical images, and health records to identify diseases. • Deep learning is used for tasks like detecting tumors in X-rays or MRIs. Machine learning algorithms are used for Medical Diagnosis: • Logistic Regression: Predicts if a person has a disease (yes or no). • Support Vector Machines (SVM): Classifies medical data, like detecting if an image shows a tumor. Example: • AI in Radiology: Detecting early-stage lung cancer
- 10. 7. Stock Market Trading What It Is: Predicting stock price movements and automating trades. How Machine Learning Is Used: • ML models analyze historical price data, news, and market sentiment to predict trends. • Used for high-frequency trading and portfolio management. Machine learning algorithms are used for Medical Diagnosis: Linear Regression: Predicts stock prices based on past trends. Logistic Regression: Helps predict if stock prices will go up or down. Example: Algorithmic Trading: Hedge funds using ML to execute trades within milliseconds.
- 11. 8. Language Translation What It Is: Translating text or speech from one language to another. How Machine Learning Is Used: • ML models like neural machine translation learn linguistic patterns and semantics from large datasets. • Used in real-time apps and document translation. Machine learning algorithms are used for Language Translation: • Sequence-to-Sequence (Seq2Seq): Converts sentences from one language to another using a neural network. • Transformers: Helps translate by understanding the meaning of words in context. Example: Google Translate. • Recurrent Neural Networks (RNN): Used to translate longer sentences by remembering previous words. Example: • Google Translate: Converting an English sentence into Spanish instantly.
- 12. 9. Traffic Alerts What It Is: Providing real-time traffic updates and route optimizations. How Machine Learning Is Used: • ML analyzes GPS data, traffic patterns, and user reports to predict congestion and suggest alternate routes. • Helps estimate travel times and avoid delays. Machine learning algorithms are used for Traffic Alerts: • Decision Trees: Helps predict traffic patterns and alert about congestion or accidents based on historical data. • K-Nearest Neighbors (KNN)Analyzes traffic data from nearby areas to predict traffic conditions and give alerts. Example: • Google Maps: Suggesting a faster route due to an accident ahead.
- 13. 10. Social Media Features What It Is: Enhancing user experience through content recommendations and personalization. How Machine Learning Is Used: • ML recommends posts, filters harmful content, and suggests connections based on user preferences. • Used for sentiment analysis and feed ranking. Machine learning algorithms are used for Social Media Features: • Clustering (e.g., K-Means): Groups similar users or content together for better recommendations. • K-Nearest Neighbors (KNN): Suggest similar content or users based on your activity. Example: • Instagram: Suggesting reels and posts based on your interactions.