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AI ML and deep learning
Artificial Intelligence vs. Machine Learning vs. Deep Learning: A Gardening Analogy Technology is rapidly evolving, and terms like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are frequently used interchangeably. However, they represent different concepts, much like different levels of gardening. Let’s break it down using a simple garden analogy. Artificial Intelligence (AI): Managing the Entire Garden AI is like managing an entire garden. It involves all aspects of planning what to plant, organizing watering schedules, and ensuring everything thrives. AI is the broad concept of machines performing tasks that typically require human intelligence, such as decision-making and problem-solving. Examples of AI in daily life include voice assistants (like Siri or Alexa), self-driving cars, and recommendation systems on streaming platforms. Machine Learning (ML): Learning from Gardening Experience Machine Learning is like a gardener learning over time. Suppose you notice that tomatoes grow better with specific soil conditions and watering schedules. Through trial and error, you develop a system for better plant growth. Similarly, ML enables machines to learn from data, identify patterns, and improve performance without being explicitly programmed for every task. For instance, if you are growing different vegetables and track their growth based on weather conditions, an ML model would learn from past successes and suggest the best times to plant new crops.
Research Blooms: The Lifecycle of a Data Project
Research Blooms: The Lifecycle of a Data Project 🌱📊 Just like a flourishing garden thrives through care, precision, and nurturing, a successful research project matures through deliberate stages to yield valuable insights. "Research Blooms: The Lifecycle of a Data Project" takes you on a journey through the essential steps of a quantitative research process, drawing parallels with the art of gardening
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