Compute Cost Function Next define the cost function, which is the cross-entropy loss, used to measure the error between predicted probabilities and actual labels.
@sumanth_077
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Sigmoid Activation Function: Computing Class Probability in Machine Learning
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Define Sigmoid Activation Function: This is used to compute the probability of the target class. Sigmoid function takes a linear combination of input features and returns values between 0 and 1, which represent the probability that a given sample belongs to the positive class.
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LogisticRegression Class Implementation with Five Essential Methods
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Define LogisticRegression class to encapsulate the functionality of Logistic Regression The class has 5 methods: __init__, sigmoid, compute_cost, fit and predict. First let's Initialize learning rate, epochs, weights and bias attributes using the __init__ method.
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Logistic Regression Implementation Guide from Scratch
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Logistic Regression clearly explained and implemented from scratch in Python:
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Microsoft Launches Free 18-Lesson Generative AI Course on GitHub
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Microsoft launched the best course on Generative AI! The free 18 lesson course is available on Github and will teach you everything you need to know to start building Generative AI applications.
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Free Learning Resources for Python, ML, and Data Science
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Learn for FREE: Python → http://
cs50.harvard.edu/python/2022/ Linear Algebra & Statistics → http://
khanacademy.org SQL → http://
sqlbolt.com Machine Learning → http://
deeplearning.ai MLOps → http://
madewithml.com Practice → http://
Kaggle.com -
Choosing Between Euclidean and Manhattan Distance Metrics
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Hi Amit, It depends on the use case, Euclidean distance is prefered if you have a continuous and normally distributed data and Manhattan if your input variables are not similar in type i.e categorical features.
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Wrapping up Python, MLOps, ML and LLMs content sharing
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That's a wrap! Every day, I share and simply content around Python, MLOps, Machine Learning & LLMs. Find me →
@Sumanth_077 Like/RT the first tweet and help this reach more people. -
Implementing _predict_single Method for k-NN Classification
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Finally a '_predict_single' method as mentioned it predicts the class label for a single data point This is done by: 1. Calculating the distance between one point to all training examples 2. Selecting the k-nearest Neighbours, & returning the most common class label among them
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Predict Method for Class Labels Using Single Point Prediction
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A 'predict' method that predicts the class labels for a set of data points. It calls '_predict_single' method that actually predicts the class label for a single data point.