You’re trying to improve your pattern recognition algorithms. What tools should you be using?

Suppose I want to create a cat detection AI. To do this, I need to feed it 1 million images of various cats in different poses and breeds that I collected from the internet. However, I discovered that among this training data, there are 20 images of dogs. If I use this data to train my algorithm, the resulting AI will not be perfect, and its accuracy in identifying cats will be reduced. Those 20 images of dogs are called Noise. To remove the noise we have to perform data pre-processing. Normalization: It helps make the data more consistent and easier to compare. Scaling: It helps make the data more uniform for the algorithm. Transformation: It helps turn the data into a form that the algorithm can understand

Choosing the right algorithm for recognizing patterns, depends on problem at hand: 1. If I'm building a Cat Identifier AI, I'll need an algo that works best with images, such as CNN (Convolutional Neural Networks) 2. If I want to build chat AI like chatGPT I need an algo like word_vec - unsupervised algorithms * Neural networks: Inspired by the human brain, learn how to recognize patterns by looking at Images * Support vector machines: To find boundaries between different classes of data, good for classifying objects * Decision trees: Based on data by asking a series of questions * K-means clustering: Group data into different clusters based on their similarity * Hidden Markov models: To find patterns in sequential data and text

* Choose a familiar language and framework with strong pattern recognition support, like Python for its simplicity and libraries. * Start with basic algorithms like KNN or decision trees to enhance understanding and confidence. * Leverage existing libraries and frameworks like Scikit-learn, TensorFlow, or PyTorch to simplify your coding process and access pre-built functions for common pattern recognition tasks. * Test various algorithms, models, and settings to tailor the solution to your unique challenge, enhancing performance. * Understanding basic math behind algorithms aids in making informed decisions and solving problems, though deep expertise isn't mandatory. * Use real-world datasets for example and practice on them.

* Confusion Matrix: A classification model's accuracy, including true positives, false positives, true negatives, and false negatives. * Precision: (TP/(TP+FP))—the rate of correct positive predictions among all positive classifications. * F1-score is a balance between precision and recall, ideal for evaluating classification models, calculated as 2TP/(2TP+FP+FN). *MSE: Mean Squared Error is the accuracy of a regression model by averaging the squared differences between predicted and actual values. Lower MSE means better model performance. --------Example Taking my cat example further if I had chosen the wrong algorithm or if I had not performed the data pre-processing part - MSE of my algorithm would come out high.

* Grid Search: Like trying every key on a keychain to unlock a door, testing all hyperparameter combinations to find the best one. * Random Search: Similar to a lottery draw, randomly selecting hyperparameters, can surprisingly lead to a winning combination. * Bayesian Optimization: Like a wise old sage predicting the future, it uses past knowledge to make smarter hyperparameter choices. * AutoML (AutomatedML) is a technique that automates the ML process, including hyperparameter optimization. 1. Preprocessing Data 2. Re-Split Data 3. Train the model with diff sets of hyperparameter combinations. 4. Evaluate the Model 5. Based on the evaluation results, adjust the hyperparameters 6. Re-train the Model using the adjusted hyperparameters