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7 Steps of the Machine Learning Process Data Collection: The process of extracting raw datasets for the machine learning task. This data can come from a variety of places, ranging from open-source online resources to paid crowdsourcing. The first step of the machine learning process is arguably the most important. If the data you collect is poor quality or irrelevant, then the model you train will be poor quality as well. Data Processing and Preparation: Once you’ve gathered the relevant data, you need to process it and make sure that it is in a usable format for training a machine learning

✅ Deep Learning Models You Should Know 🧠📚 1️⃣ Feedforward Neural Networks (FNN) – Basic neural networks for structured/tabular data – Example: Classification or regression on tabular datasets 2️⃣ Convolutional Neural Networks (CNN) – Specialized for image and spatial data – Example: Image classification, object detection 3️⃣ Recurrent Neural Networks (RNN) – Processes sequential data – Example: Time series forecasting, text generation 4️⃣ Long Short-Term Memory (LSTM) – A type of RNN for long-range dependencies – Example: Stock price prediction, language modeling 5️⃣ Gated Recurre