├── assets └── banner.png ├── tutorials ├── neural-networks-and-deep-learning │ └── neural-network │ │ └── neural_network.py └── core-machine-learning-algorithms │ ├── mean-squared-error │ └── mean_squared_error.py │ ├── mean-absolute-error │ └── mean_absolute_error.py │ ├── activation-functions │ └── activation_functions.py │ ├── linear-regression │ └── linear_regression.py │ ├── knn │ └── knn.py │ └── logistic-regression │ └── logistic_regression.py ├── LICENSE └── README.md /assets/banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amitshekhariitbhu/build-your-own-x-machine-learning/HEAD/assets/banner.png -------------------------------------------------------------------------------- /tutorials/neural-networks-and-deep-learning/neural-network/neural_network.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | class NeuralNetwork: 4 | -------------------------------------------------------------------------------- /tutorials/core-machine-learning-algorithms/mean-squared-error/mean_squared_error.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def mean_squared_error(y_true, y_pred): 4 | 5 | # Calculate MSE: mean of squared differences 6 | mse = np.mean((y_true - y_pred) ** 2) 7 | return mse 8 | 9 | # Example usage 10 | if __name__ == "__main__": 11 | # Generate synthetic data 12 | np.random.seed(0) 13 | X = np.random.randn(100, 1) # 100 samples, 1 feature 14 | y_true = 3 * X.flatten() + 2 + np.random.randn(100) * 0.5 # y = 3x + 2 + noise 15 | 16 | # Simulate predictions 17 | # Assume a slightly off model: y_pred = 2.8x + 2.2 18 | y_pred = 2.8 * X.flatten() + 2.2 19 | 20 | # Calculate MSE 21 | mse = mean_squared_error(y_true, y_pred) 22 | 23 | # Print results 24 | print("Actual values (first 5):", y_true[:5]) 25 | print("Predicted values (first 5):", y_pred[:5]) 26 | print("Mean Squared Error:", mse) 27 | -------------------------------------------------------------------------------- /tutorials/core-machine-learning-algorithms/mean-absolute-error/mean_absolute_error.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def mean_absolute_error(y_true, y_pred): 4 | 5 | # Calculate MAE: mean of absolute differences 6 | mae = np.mean(np.abs(y_true - y_pred)) 7 | return mae 8 | 9 | # Example usage 10 | if __name__ == "__main__": 11 | # Generate synthetic data 12 | np.random.seed(0) 13 | X = np.random.randn(100, 1) # 100 samples, 1 feature 14 | y_true = 3 * X.flatten() + 2 + np.random.randn(100) * 0.5 # y = 3x + 2 + noise 15 | 16 | # Simulate predictions 17 | # Assume a slightly off model: y_pred = 2.8x + 2.2 18 | y_pred = 2.8 * X.flatten() + 2.2 19 | 20 | # Calculate MAE 21 | mae = mean_absolute_error(y_true, y_pred) 22 | 23 | # Print results 24 | print("Actual values (first 5):", y_true[:5]) 25 | print("Predicted values (first 5):", y_pred[:5]) 26 | print("Mean Absolute Error:", mae) 27 | -------------------------------------------------------------------------------- /tutorials/core-machine-learning-algorithms/activation-functions/activation_functions.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def sigmoid(x): 4 | return 1 / (1 + np.exp(-x)) 5 | 6 | def tanh(x): 7 | exp_x = np.exp(x) 8 | exp_neg_x = np.exp(-x) 9 | return (exp_x - exp_neg_x) / (exp_x + exp_neg_x) 10 | 11 | def relu(x): 12 | return np.maximum(0, x) 13 | 14 | def leaky_relu(x, alpha=0.01): 15 | return np.where(x > 0, x, alpha * x) 16 | 17 | def softmax(x): 18 | # Subtract max for numerical stability 19 | exp_x = np.exp(x - np.max(x, axis=-1, keepdims=True)) 20 | return exp_x / np.sum(exp_x, axis=-1, keepdims=True) 21 | 22 | # Example usage 23 | if __name__ == "__main__": 24 | # Generate synthetic input data 25 | np.random.seed(0) 26 | 27 | # For Sigmoid, Tanh, ReLU, Leaky ReLU: single array 28 | x = np.random.randn(5) # 5 scalar inputs 29 | 30 | # For Softmax: simulate scores for 3 classes 31 | x_softmax = np.random.randn(5, 3) # 5 samples, 3 classes 32 | 33 | # Calculate activations 34 | sigmoid_output = sigmoid(x) 35 | tanh_output = tanh(x) 36 | relu_output = relu(x) 37 | leaky_relu_output = leaky_relu(x, alpha=0.01) 38 | softmax_output = softmax(x_softmax) 39 | 40 | # Print results 41 | print("Input (x):", x) 42 | print("Sigmoid:", sigmoid_output) 43 | print("Tanh:", tanh_output) 44 | print("ReLU:", relu_output) 45 | print("Leaky ReLU (alpha=0.01):", leaky_relu_output) 46 | print("Softmax Input (5 samples, 3 classes):", x_softmax) 47 | print("Softmax Output:", softmax_output) 48 | -------------------------------------------------------------------------------- /tutorials/core-machine-learning-algorithms/linear-regression/linear_regression.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | class LinearRegression: 4 | def __init__(self, learning_rate=0.01, n_epochs=1000): 5 | self.learning_rate = learning_rate 6 | self.n_epochs = n_epochs 7 | self.weights = None 8 | self.bias = None 9 | 10 | def fit(self, X, y): 11 | # Initialize parameters 12 | n_samples, n_features = X.shape 13 | self.weights = np.zeros(n_features) 14 | self.bias = 0 15 | 16 | # Gradient descent 17 | for _ in range(self.n_epochs): 18 | 19 | y_predicted = np.dot(X, self.weights) + self.bias 20 | 21 | # Calculate gradients 22 | dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y)) 23 | db = (1 / n_samples) * np.sum(y_predicted - y) 24 | 25 | # Update parameters 26 | self.weights -= self.learning_rate * dw 27 | self.bias -= self.learning_rate * db 28 | 29 | # Calculate loss 30 | loss = np.mean((y_predicted - y) ** 2) 31 | 32 | def predict(self, X): 33 | return np.dot(X, self.weights) + self.bias 34 | 35 | # Example usage 36 | if __name__ == "__main__": 37 | # Generate sample data 38 | np.random.seed(0) 39 | X = 2 * np.random.rand(100, 1) 40 | y = 3 * X.flatten() + 4 + np.random.randn(100) 41 | 42 | # Create and train model 43 | model = LinearRegression(learning_rate=0.01, n_epochs=1000) 44 | model.fit(X, y) 45 | 46 | # Make predictions 47 | predictions = model.predict([[5], [10], [15], [20]]) 48 | 49 | # Print results 50 | print("Predictions:", predictions) 51 | print("Learned weights:", model.weights) 52 | print("Learned bias:", model.bias) 53 | -------------------------------------------------------------------------------- /tutorials/core-machine-learning-algorithms/knn/knn.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | class KNN: 4 | def __init__(self, k=3): 5 | self.k = k 6 | self.X_train = None 7 | self.y_train = None 8 | 9 | def fit(self, X, y): 10 | #Store the training data. 11 | self.X_train = X 12 | self.y_train = y 13 | 14 | def predict(self, x): 15 | # Calculate Euclidean distances to all training samples 16 | distances = np.sqrt(np.sum((self.X_train - x) ** 2, axis=1)) 17 | 18 | # Get indices of k nearest neighbors 19 | k_indices = np.argsort(distances)[:self.k] 20 | 21 | # Get labels of k nearest neighbors 22 | k_nearest_labels = self.y_train[k_indices] 23 | 24 | # Return most common label (majority vote) 25 | return np.bincount(k_nearest_labels).argmax() 26 | 27 | 28 | # Example usage 29 | if __name__ == "__main__": 30 | # Generate sample data (simple linear boundaries for 3 classes) 31 | np.random.seed(0) 32 | X = np.random.rand(150, 2) * 2 # 150 samples, 2 features in [0, 2] 33 | # Assign labels based on two linear boundaries 34 | y = np.zeros(150, dtype=int) 35 | y[(X[:, 0] + X[:, 1] > 1.5)] = 1 # Class 1 if x_1 + x_2 > 1.5 36 | y[(X[:, 0] + X[:, 1] < 0.5)] = 2 # Class 2 if x_1 + x_2 < 0.5 37 | # Class 0 for 0.5 <= x_1 + x_2 <= 1.5 38 | 39 | # Create and train model 40 | model = KNN(k=3) 41 | model.fit(X, y) 42 | 43 | # Predict 44 | test_point_1 = np.array([1.5, 0.5]) # x_1 + x_2 = 2 > 1.5, should be Class 1 45 | prediction_1 = model.predict(test_point_1) 46 | test_point_2 = np.array([0.2, 0.5]) # 0.5 <= x_1 + x_2 <= 1.5, should be class 0 47 | prediction_2 = model.predict(test_point_2) 48 | 49 | # Print results 50 | print("Test point 1:", test_point_1) 51 | print("Predicted class for Test point 1:", prediction_1) 52 | print("Test point 2:", test_point_2) 53 | print("Predicted class for Test point 2:", prediction_2) 54 | -------------------------------------------------------------------------------- /tutorials/core-machine-learning-algorithms/logistic-regression/logistic_regression.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def sigmoid(z): 4 | # Sigmoid function to map values to (0, 1) 5 | return 1 / (1 + np.exp(-z)) 6 | 7 | class LogisticRegression: 8 | def __init__(self, learning_rate=0.01, n_epochs=1000): 9 | self.learning_rate = learning_rate 10 | self.n_epochs = n_epochs 11 | self.weights = None 12 | self.bias = None 13 | 14 | def fit(self, X, y): 15 | # Initialize parameters 16 | n_samples, n_features = X.shape 17 | self.weights = np.zeros(n_features) 18 | self.bias = 0 19 | 20 | # Gradient descent 21 | for _ in range(self.n_epochs): 22 | linear_model = np.dot(X, self.weights) + self.bias 23 | y_predicted = sigmoid(linear_model) 24 | 25 | # Calculate gradients 26 | dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y)) 27 | db = (1 / n_samples) * np.sum(y_predicted - y) 28 | 29 | # Calculate parameters 30 | self.weights -= self.learning_rate * dw 31 | self.bias -= self.learning_rate * db 32 | 33 | # Calculate loss (binary cross-entropy) 34 | loss = -np.mean(y * np.log(y_predicted) + (1 - y) * np.log(1 - y_predicted )) 35 | 36 | def predict_probability(self, X): 37 | # Predict probabilities 38 | linear_model = np.dot(X, self.weights) + self.bias 39 | return sigmoid(linear_model) 40 | 41 | def predict(self, X, threshold=0.5): 42 | # Predict class labels (0 or 1) based on threshold 43 | probabilities = self.predict_probability(X) 44 | return (probabilities >= threshold).astype(int) 45 | 46 | # Example usage 47 | if __name__ == "__main__": 48 | # Generate sample data (binary classification) 49 | np.random.seed(0) 50 | X = np.random.randn(100, 2) # 100 samples, 2 features 51 | y = (X[:, 0] + X[:, 1] > 0).astype(int) # Class 1 if sum of features > 0, else 0 52 | 53 | # Create and train model 54 | model = LogisticRegression(learning_rate=0.1, n_epochs=1000) 55 | model.fit(X, y) 56 | 57 | # Make predictions 58 | predictions = model.predict([[-0.03, -1.16], [1.49, 0.43]]) 59 | 60 | # Print results 61 | print("Predictions:", predictions) 62 | print("Learned weights:", model.weights) 63 | print("Learned bias:", model.bias) 64 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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2 | Build your own X - Machine Learning 3 |

4 | 5 | # Build your own X - Machine Learning 6 | 7 | > Master machine learning by building everything from scratch. It aims to cover everything from linear regression to deep learning to large language models (LLMs). 8 | 9 | ## Table of Contents 10 | * [Core Machine Learning Algorithms](#core-machine-learning-algorithms) 11 | * [Neural Networks and Deep Learning](#neural-networks-and-deep-learning) 12 | * [Recommendation Systems](#recommendation-systems) 13 | * [Computer Vision Applications](#computer-vision-applications) 14 | * [Natural Language Processing](#natural-language-processing) 15 | * [Time Series and Forecasting](#time-series-and-forecasting) 16 | * [Anomaly Detection](#anomaly-detection) 17 | * [Sentiment and Text Analysis](#sentiment-and-text-analysis) 18 | * [Miscellaneous Applications](#miscellaneous-applications) 19 | 20 | ### Prepared and maintained by the **Founder** of [Outcome School](https://outcomeschool.com): Amit Shekhar 21 | 22 | ### Follow Amit Shekhar 23 | 24 | - [X/Twitter](https://twitter.com/amitiitbhu) 25 | - [LinkedIn](https://www.linkedin.com/in/amit-shekhar-iitbhu) 26 | - [GitHub](https://github.com/amitshekhariitbhu) 27 | 28 | ### Follow Outcome School 29 | 30 | - [YouTube](https://youtube.com/@OutcomeSchool) 31 | - [X/Twitter](https://x.com/outcome_school) 32 | - [LinkedIn](https://www.linkedin.com/company/outcomeschool) 33 | - [GitHub](http://github.com/OutcomeSchool) 34 | 35 | --- 36 | 37 | > **Note: We will keep updating this with new tutorials.** 38 | 39 | --- 40 | 41 | ### Core Machine Learning Algorithms 42 | 43 | - [Implement Linear Regression from scratch](https://github.com/amitshekhariitbhu/build-your-own-x-machine-learning/blob/main/tutorials/core-machine-learning-algorithms/linear-regression/linear_regression.py) 44 | - [Implement Logistic Regression from scratch](https://github.com/amitshekhariitbhu/build-your-own-x-machine-learning/blob/main/tutorials/core-machine-learning-algorithms/logistic-regression/logistic_regression.py) 45 | - [Implement K-Nearest Neighbors (KNN)](https://github.com/amitshekhariitbhu/build-your-own-x-machine-learning/blob/main/tutorials/core-machine-learning-algorithms/knn/knn.py) 46 | - Implement Naive Bayes 47 | - Implement Decision Tree 48 | - Implement Random Forest 49 | - Implement Support Vector Machines (SVM) 50 | - Implement K-Means Clustering from scratch 51 | - Implement Principal Component Analysis (PCA) 52 | - Implement Perceptron from scratch 53 | - Implement Gradient Descent from scratch 54 | - Implement Gradient Boosting from scratch 55 | - [Implement Mean Squared Error (MSE) cost function](https://github.com/amitshekhariitbhu/build-your-own-x-machine-learning/blob/main/tutorials/core-machine-learning-algorithms/mean-squared-error/mean_squared_error.py) 56 | - [Implement Mean Absolute Error (MAE) cost function](https://github.com/amitshekhariitbhu/build-your-own-x-machine-learning/blob/main/tutorials/core-machine-learning-algorithms/mean-absolute-error/mean_absolute_error.py) 57 | - [Implement Sigmoid, Tanh, ReLU, LeakyReLU, and Softmax Activation Functions](https://github.com/amitshekhariitbhu/build-your-own-x-machine-learning/blob/main/tutorials/core-machine-learning-algorithms/activation-functions/activation_functions.py) 58 | - Implement SGD, Adam, RMSprop, and Adagrad Optimizers 59 | - Implement AdaBoost from scratch 60 | - Implement Linear Discriminant Analysis (LDA) from scratch 61 | - Implement Ridge and Lasso Regression from scratch 62 | - Implement Polynomial Regression 63 | - Implement ElasticNet Regression 64 | - Implement Bayesian Regression 65 | - Implement Mean-Shift Clustering 66 | - Implement Spectral Clustering 67 | - Implement Independent Component Analysis 68 | - Implement Factor Analysis 69 | - Implement Singular Value Decomposition 70 | - Implement Gaussian Mixture Models from scratch 71 | - Implement Hierarchical Clustering 72 | - Implement DBSCAN Clustering 73 | - Implement Isolation Forest for Anomaly Detection 74 | - Implement One-Class SVM for Anomaly Detection 75 | - Implement Local Outlier Factor (LOF) 76 | 77 | ### Neural Networks and Deep Learning 78 | 79 | - Build your own Neural Network from scratch 80 | - Build your own Convolutional Neural Network from scratch 81 | - Build a Transformer-based Large Language Model from scratch. 82 | - Build an LSTM Neural Network from scratch 83 | - Implement an Autoencoder from scratch 84 | - Implement Backpropagation from scratch 85 | - Build a mini framework like TensorFlow using NumPy from scratch 86 | - Build a mini framework like PyTorch using NumPy from scratch 87 | - Implement Reinforcement Learning from scratch 88 | - Implement a Diffusion Model from scratch 89 | - Implement Generative Adversarial Network (GAN) from scratch 90 | - Develop Feedforward Neural Networks from scratch 91 | - Build a Recurrent Neural Network from scratch 92 | - Implement Gated Recurrent Unit (GRU) from scratch 93 | - Develop a Variational Autoencoder Model 94 | - Implement AlexNet Neural Network Architecture from scratch 95 | - Build a Multilayer Perceptron from scratch 96 | 97 | ### Recommendation Systems 98 | 99 | - Build a Movie Recommendation System 100 | - Build a YouTube Video Recommendation System 101 | - Build a YouTube Video Search System 102 | - Build a Personalized Content Feed System 103 | - Implement Similar Listings on Airbnb 104 | - Implement Replacement Product Recommendation System 105 | - Build an Event Recommendation System 106 | - Build a Product Recommendation System 107 | - Build a Friends Recommendation System 108 | - Build a Book Recommendation System 109 | - Implement Collaborative Filtering for Recommendation Systems 110 | - Develop a Fashion Recommendation System 111 | - Build a Netflix Recommendation System 112 | - Build a TED Talk Recommendation System 113 | - Develop an Instagram and Pinterest Image Filter Recommendation System 114 | - Build an Article Recommendation System 115 | - Build a Restaurant Recommendation System 116 | - Develop a Music and Audio Recommendation System 117 | 118 | ### Computer Vision Applications 119 | 120 | - Build a traffic sign classifier using CNN 121 | - Build an app for Optical Character Recognition (OCR) 122 | - Implement Handwritten Digit Recognition from scratch 123 | - Implement Brain Tumor Detection from scratch 124 | - Implement Pneumonia Detection using Deep Learning 125 | - Build a real-time object detection application 126 | - Implement Face Mask Detection 127 | - Build a pedestrian detection system 128 | - Implement Handwritten Character Recognition from scratch 129 | - Build a License Plate Recognition System 130 | - Build a Real-Time Hand Gesture Recognition System 131 | - Implement Bird Species Identification Model 132 | - Build a Cats vs Dogs Classification Model 133 | - Build a Human Face Detection System 134 | - Build an Image Cartoonification System 135 | - Implement Fake Currency Detection Model 136 | - Implement Flower Recognition Model 137 | - Implement Image Colorization Model 138 | - Build a Distracted Driver Detection System 139 | - Develop a Human Activity Recognition Model 140 | - Build an Image Segmentation System 141 | - Build a Human Face Recognition System 142 | - Build a Human Pose Estimation System 143 | - Develop a Dog Breed Identification App 144 | - Build a Vehicle Counting System for Traffic Management 145 | - Implement Instance Segmentation System 146 | - Implement Text Extraction from Images 147 | - Develop Bone X-Ray Analysis Model 148 | - Build an Image Classification Model 149 | - Implement Image Captioning Model 150 | - Build a Visual Question Answering System 151 | - Implement Style Transfer Model 152 | - Develop Landmark Detection Model 153 | - Develop a Face Landmark Detection Model 154 | - Build a Satellite Imagery Processing System for Automated Tagging 155 | - Build a model for Object Counting in Images 156 | - Implement an Image Recognition system 157 | - Develop HOG Feature Extraction System 158 | 159 | ### Natural Language Processing 160 | 161 | - Build a Language Translator from scratch 162 | - Build a Text Summarizer from scratch 163 | - Implement a BPE Tokenizer from scratch 164 | - Build Word Embedding from scratch 165 | - Build Word2Vec from scratch 166 | - Implement Spam Email Classifier from scratch 167 | - Develop a Sentiment Analysis System from scratch 168 | - Build a Harmful Content Detection System 169 | - Develop a Chatbot System 170 | - Implement Speech Emotion Recognition from scratch 171 | - Implement Emoji Prediction System 172 | - Develop an Automatic Music Generation App 173 | - Implement Sign Language Recognition System 174 | - Build a Named Entity Recognition System from scratch 175 | - Build a Next Word Prediction Model 176 | - Develop a Smart Proofreader System 177 | - Implement a Text Processing Pipeline for Indian Languages from scratch 178 | - Develop an Automated Essay Grading System 179 | - Implement Handwriting Recognition for Forms 180 | - Develop a Document Layout Analysis System 181 | - Implement Table Detection and Extraction from Documents 182 | - Build a News Article Classification Model 183 | - Build a Code Syntax Highlighting System using ML 184 | - Develop a Spelling Correction System using a Language Model 185 | - Develop a Question-to-SQL Query Generator System 186 | - Implement Topic Modeling for Customer Complaints from scratch 187 | - Build a Text-to-Speech System 188 | - Implement Personalized News Generation System 189 | - Develop a Calendar Scheduling System from Text 190 | - Build an Email Organization System using ML 191 | - Implement Text Extraction from PDFs from scratch 192 | - Build a Topic Modeling System 193 | - Develop a Language Classification Model 194 | - Implement Text Classification Model 195 | - Implement Text and Annotation Analysis System 196 | - Build a Medical Chatbot 197 | - Build an AI Room Booking Chatbot 198 | 199 | ### Time Series and Forecasting 200 | 201 | - Implement LSTM Models for Time Series Forecasting 202 | - Implement Multivariate Time Series Forecasting Model 203 | - Implement Univariate Time Series Forecasting Model 204 | - Develop an Automated Time Series Forecasting System 205 | - Develop an ARIMA Model 206 | - Build a Prophet Forecasting Model 207 | - Implement Exponential Smoothing for Time Series 208 | - Build an Anomaly Detection System using ARIMA 209 | - Implement Stock Price Prediction Model 210 | - Build a Customer Churn Prediction System 211 | - Implement Crop Yield Prediction Model 212 | - Implement Daily Birth Forecasting Model 213 | - Implement Rainfall Prediction Model 214 | - Build a Product Demand Prediction Model 215 | - Implement Electricity Price Prediction Model 216 | - Develop a Future Sales Prediction Model 217 | - Implement a Gold Price Prediction Model 218 | - Build a Bitcoin Price Prediction Model 219 | - Build a Currency Exchange Rate Prediction Model 220 | - Implement Profit Prediction Model 221 | - Develop a Sales Prediction Model 222 | - Build a Demand and Load Forecasting Model 223 | - Implement a Cricket Score Prediction Model 224 | - Develop an Order Volume Prediction Model 225 | - Build a Waiter Tip Prediction Model 226 | - Implement Calorie Burn Prediction Model 227 | - Implement House Price Prediction Model 228 | - Develop a Car Price Prediction Model 229 | - Build a Fuel Efficiency Prediction Model 230 | - Implement the COVID-19 Case Prediction Model 231 | - Develop a Migration Prediction Model 232 | - Implement COVID-19 Analysis System 233 | - Implement Uber Trip Analysis System 234 | 235 | ### Anomaly Detection 236 | 237 | - Build an Anomaly Detection System 238 | - Implement a Credit Card Fraud Detection System 239 | - Build a Customer Segmentation Model 240 | - Implement a Fake Product Review Detection Model 241 | - Build an Insurance Claim Fraud Detection System 242 | - Implement Online Payment Fraud Detection Model 243 | - Develop a Consumer Credit Risk Prediction Model 244 | 245 | ### Sentiment and Text Analysis 246 | 247 | - Implement Twitter Hashtag Prediction Model 248 | - Implement Deepfake Detection Model 249 | - Build an SMS Spam Detection App 250 | - Implement WhatsApp Chat Sentiment Analysis System 251 | - Develop an Omicron Variant Sentiment Analysis System 252 | - Build a Squid Game Sentiment Analysis System 253 | - Develop a Google Play Store Sentiment Analysis System 254 | - Build an Amazon Alexa Review Sentiment Analysis System 255 | - Develop a Hotel Review Sentiment Analysis System 256 | - Build a Hate Speech Detection System 257 | - Build a Fake News Detection System 258 | - Implement a Social Media Ad Classification Model 259 | - Implement a Real-Time Sentiment Analysis System 260 | - Implement a Comparison System for Classification Algorithms 261 | - Implement a Streaming Service Analysis System 262 | - Develop a Sarcasm Detection System 263 | - Implement an Amazon Product Review Sentiment Analysis System 264 | - Develop a Google Search Analysis System 265 | - Implement a Financial Budget Analysis System 266 | - Develop a Click-Through Rate Prediction Model 267 | - Build a YouTube Trending Video Analysis System 268 | - Implement a Netflix Data Analysis System 269 | 270 | ### Miscellaneous Applications 271 | 272 | - Build a system to detect Parkinson’s Disease 273 | - Implement Breast Cancer Classification System 274 | - Develop a Gender and Age Detection System 275 | - Implement the Driver Drowsiness Detection System 276 | - Develop a Video Surveillance System 277 | - Develop an Earthquake Prediction System 278 | - Implement the Iris Flower Classification System 279 | - Build a Diabetes Prediction System 280 | - Develop a Heart Disease Prediction Model 281 | - Build a Hypothyroidism Prediction Model 282 | - Develop an Election Prediction Model 283 | - Build a Tinder Match Prediction Model 284 | - Develop a Worldwide Billionaires Analysis System 285 | - Build an Unemployment Analysis System 286 | - Develop a Life Expectancy Analysis System 287 | - Develop a Highest-Paid Athlete Analysis System 288 | - Build a Birth Rate Analysis System 289 | - Build a Social Media Follower Prediction Model 290 | - Build a Health Insurance Premium Prediction Model 291 | - Implement a Mobile Price Classification Model 292 | - Implement a Bar Chart Race Visualization 293 | - Develop a Keyword Research System 294 | - Implement a Contact Tracing System 295 | - Build a Titanic Survival Analysis System 296 | - Develop a Student Grade Prediction Model 297 | - Implement a Q-Learning Algorithm from scratch 298 | - Develop a Deep Q-Network (DQN) 299 | - Implement policy gradient methods 300 | - Implement Actor-Critic Methods 301 | - Implement the SARSA Algorithm 302 | - Build a Proximal Policy Optimization Algorithm 303 | - Build a Monte Carlo Tree Search Algorithm 304 | - Build a Network Security Analysis System 305 | - Develop a Genetic Algorithm 306 | - Build a Healthcare Data Analysis System 307 | - Build a Resume Parser System 308 | - Implement an Autocorrect Keyboard System from scratch 309 | - Build a Barcode and QR Code Reader System 310 | - Develop an Amazon Bestselling Books Analysis System 311 | - Implement a Video Game Sales Prediction Model 312 | - Develop a President Height Analysis System 313 | - Implement a Movie Rating Analysis System 314 | - Build an Ad Click Prediction Model 315 | - Build a model to Estimate Delivery Time 316 | - Build an Image Search System 317 | - Implement Music Genre Classification 318 | - Implement Nationality Classification Model 319 | - Develop an Employee Turnover Prediction Model 320 | - Implement Wine Quality Prediction Model 321 | - Develop a Visualization System for a Machine Learning Algorithm 322 | - Build an Automated Tagging System for StackOverflow Questions 323 | - Build a Title Generation System 324 | - Develop an Online Shopping Intention Analysis System 325 | - Build a Stress Detection System 326 | - Implement Insurance Prediction Model 327 | - Develop a Blood Donation Prediction Model 328 | - Build a Cross-Validation System 329 | - Implement Grid Search and Random Search for Hyperparameter Tuning 330 | - Implement a Confusion Matrix System 331 | - Write code to calculate the F1-score 332 | - Develop a Bagging Ensemble Model 333 | - Build a Boosting Ensemble Model 334 | - Implement a Stacking Ensemble Model 335 | - Develop a Voting Classifier Model 336 | - Implement t-SNE Dimensionality Reduction 337 | - Implement UMAP Dimensionality Reduction 338 | - Implement Newton’s Method Optimization 339 | - Implement Bayesian Networks from scratch 340 | - Develop Hidden Markov Models 341 | - Build Self-Organizing Maps 342 | - Build a Self-Training Model 343 | - Implement Contrastive Learning 344 | - Develop a Large-Scale Video Understanding System 345 | - Build a Video Summarization System 346 | 347 | ### License 348 | ``` 349 | Copyright (C) 2025 Outcome School 350 | 351 | Licensed under the Apache License, Version 2.0 (the "License"); 352 | you may not use this file except in compliance with the License. 353 | You may obtain a copy of the License at 354 | 355 | http://www.apache.org/licenses/LICENSE-2.0 356 | 357 | Unless required by applicable law or agreed to in writing, software 358 | distributed under the License is distributed on an "AS IS" BASIS, 359 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 360 | See the License for the specific language governing permissions and 361 | limitations under the License. 362 | ``` 363 | --------------------------------------------------------------------------------