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What's the difference between Machine Learning and AI?

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  What's the difference between Machine Learning and AI? Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably, but they are not the same. AI is a broad concept that involves machines simulating human intelligence, while ML is a subset of AI that focuses on learning from data and improving performance without explicit programming. Both are shaping the future of technology, impacting industries from healthcare to finance. What is Artificial Intelligence (AI)? AI refers to the development of systems that can perform tasks typically requiring human intelligence. These tasks include problem-solving, decision-making, language understanding, and even perception. AI aims to create intelligent systems that can think, reason, and act based on input data. Types of AI Narrow AI (Weak AI): Designed for specific tasks (e.g., voice assistants like Siri and Alexa). Does not have general intelligence. General AI (Strong AI): Can perform any intellectual task that a...
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  Types of Machine Learning Machine learning classes in Chandigarh can be broadly categorized into three main types based on the nature of the learning process and the availability of labeled data: Supervised Learning: In supervised learning, the algorithm is trained on a labeled dataset, where each input data point is associated with a corresponding target or label. The goal of supervised learning is to learn a mapping from input features to output labels, such that the model can accurately predict the correct label for new, unseen data points. Examples of supervised learning tasks include classification, where the goal is to assign input data points to predefined classes or categories, and regression, where the goal is to predict a continuous target variable. Common algorithms used in supervised learning include linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. Unsupervised Learning: In unsupervised learni...
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  How does machine learning work? Machine learning is a subset of artificial intelligence (AI) that enables computers to learn from data and improve their performance on specific tasks over time without being explicitly programmed. Here's an overview of how machine learning works: Data Collection: The first step in machine learning involves gathering and preparing data relevant to the task at hand. This data can come from various sources, such as databases, sensors, APIs, or manual collection. The quality and quantity of data are crucial factors that can significantly impact the performance of the machine learning model. Data Preprocessing: Once the data is collected, it needs to be cleaned, formatted, and preprocessed to remove noise, handle missing values, and standardize features. This step ensures that the data is suitable for analysis and modeling by the machine learning algorithms. Feature Engineering: Feature engineering involves selecting, extracting, or creating relevant f...
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  Challenges of machine learning Machine learning institute in Chandigarh presents several challenges that researchers, practitioners, and organizations must address to realize its full potential and ensure responsible and ethical use. Some of the key challenges of machine learning include: Data Quality and Quantity : Machine learning algorithms require large volumes of high-quality data to train effectively. However, obtaining labeled data can be costly, time-consuming, or challenging, especially in domains where data is scarce, biased, or unreliable. Ensuring data quality, diversity, representativeness, and balance is crucial to avoid biased or inaccurate models. Data Privacy and Security : Machine learning involves handling sensitive or personal data, raising concerns about privacy, security, and ethical considerations. Protecting sensitive information, ensuring compliance with data protection regulations (e.g., GDPR, HIPAA), and preventing unauthorized access or misuse of data...
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