Without knowing these Terms, ML/AI will always feel Futuristic/Alien.
Similarities & Differences
🎯AI vs. ML:
AI is the broader concept that encompasses the idea of machines performing tasks that would typically require human intelligence (e.g., reasoning, decision-making).
ML is a “subset of AI” that specifically focuses on using data-driven algorithms to learn from and make predictions based on data.
🎯ML vs. Deep Learning:
Deep Learning is a specialized area within ML.
While traditional ML algorithms, might use “structured data” (like tables)
Deep Learning models are designed to, automatically learn hierarchical features from raw data, especially unstructured data like images and text.
Deep learning typically requires much larger datasets and computing power than traditional ML.
Now, dont loose the momentum, complete the last most important chunk of the blog, “THE MODEL”
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When we hear, “Train the Model”, “Model fine-tuning” etc etc
So How the MODEL Looks ?👀
A model itself isn’t a file you can open (like an Excel sheet).
Instead, it consists of:
The learned parameters (weights and biases): These are the “knowledge” the model has acquired during training.
In linear regression, these would be the coefficients.
In deep learning, these are the weights in the neural network layers.
The architecture: The structure of the model, which defines how the data flows through the model.
For example, a neural network has an architecture with multiple layers, each containing nodes (neurons) connected to each other.
Model as a Saved File:
Once a model is trained, it’s saved into a file. For example:
TensorFlow/Keras: .h5 file
PyTorch: .pth file
Scikit-learn: Pickle file (.pkl)
These files store all the necessary information about the model, such as:
Model architecture (if applicable)
Learned parameters (weights, biases)
Hyperparameters used during training (like learning rate, number of epochs)
Once saved, the model can be used to make predictions on new data without needing to retrain it.
Last part: How Does the Model Make Predictions?
Once trained and saved, the model is ready for inference (making predictions):
You load the trained model from the file.
You feed new, unseen data into the model (like an image of a cat or a house with specific features).
The model uses its learned parameters to output a prediction. For example:
🌾In the case of the house price model, you input the square footage and the number of bedrooms, and the model predicts the house price.
🌾In the case of the image classification model, you input an image, and the model classifies it as “cat” or “dog.”🐈🐶
Summary:
🚀A machine learning model isn’t an Excel file or a chunk of code; it’s a trained mathematical entity (typically stored as a file) that encapsulates learned knowledge.
🚀The model learns patterns from the data during training and is then able to predict outcomes when given new data.
🚀In simple ML models (e.g., linear regression), the model might look like a mathematical equation, while in more complex models (e.g., neural networks), it consists of learned weights and biases stored in specialized formats like .h5, .pth, or .pkl.
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If you’re worried about AI replacing jobs or questioning your ability to keep up, remember that every expert was once a beginner.
Stay curious & motivated — keep learning & moving forward.
Keep Calm, Keep Aware, Keep the Chin and Thinking UP !! You will do it !!
If you want any personal suggestion or a one-to-one call with me, will be more then happy to have one🌿
Let’s connect on Linkedin for a Hi !!
Now, Take a deep breathe and Go Learn🌏
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