What's included

• Image Classification Model

  Week 1-2: Project Definition and Data Collection Define the scope of the image classification task. Identify the categories/classes for classification. Collect and preprocess the dataset, ensuring proper labeling and quality. Week 3-4: Data Preprocessing and Model Selection Perform data augmentation and transformation to increase dataset diversity. Select a suitable pre-trained deep learning model (e.g., ResNet, Inception) for transfer learning. Week 5-6: Model Fine-Tuning and Training Load the pre-trained model and adjust its architecture for the new task. Train the model on the preprocessed dataset, monitoring loss and accuracy. Week 7-8: Model Evaluation and Optimization Evaluate the model using validation data and metrics like accuracy and F1-score. Fine-tune hyperparameters and experiment with different optimization techniques. Week 9-10: Deployment and Documentation Deploy the trained model to a web service or application. Document the entire process, from data collection to deployment, for future reference.

• Natural Language Processing (NLP) Sentiment Analysis Model

  Week 1-2: Problem Definition and Data Collection Define the sentiment analysis problem (positive/negative/neutral) and target text sources. Collect and preprocess a dataset of labeled text samples. Week 3-4: Text Preprocessing and Feature Extraction Clean and tokenize the text data. Apply techniques like TF-IDF or word embeddings for feature extraction. Week 5-6: Model Selection and Training Choose a suitable NLP model architecture like LSTM or BERT. Train the model on the preprocessed text data, adjusting hyperparameters. Week 7-8: Model Evaluation and Fine-Tuning Evaluate the model using validation data and metrics like accuracy and F1-score. Fine-tune the model's architecture and hyperparameters for better performance. Week 9-10: Deployment and Documentation Create a user-friendly interface for users to input text. Deploy the model as a web or app feature and document the process.

• Recommender System

  Week 1-2: Problem Definition and Data Collection Define the type of recommender system (content-based, collaborative filtering, hybrid). Gather user-item interaction data and item features. Week 3-4: Data Preprocessing and Featurization Preprocess user-item interaction data and item features. Create user-item interaction matrices or feature vectors. Week 5-6: Model Selection and Training Choose a recommender algorithm like Matrix Factorization or Neural Collaborative Filtering. Train the model on the preprocessed data and evaluate its initial performance. Week 7-8: Model Refinement and Evaluation Fine-tune the model parameters and regularization. Evaluate the model's performance using metrics like Precision@K and Mean Average Precision. Week 9-10: Deployment and Documentation Implement the recommender system in a platform or application. Document the model's architecture, training process, and deployment steps.