Скачать или смотреть How Does Machine Learning Works? A Step-by-step processes

Certainly! Here's a detailed step-by-step summary of the machine learning process:
Step 1: Data Collection
• Gather data from various sources like databases, text files, images, audio files, or web scraping.
• Ensure data quality and quantity as they directly impact model performance.
Step 2: Data Preprocessing
• Clean the data by removing duplicates and correcting errors.
• Handle missing data by either removing or filling it in.
• Normalize the data by scaling it to a standard format.
Step 3: Choosing the Right Model
• Select a machine learning model based on the nature of the data and the problem at hand.
• Consider factors like data size and type, problem complexity, and available computational resources.
Step 4: Training the Model
• Feed the prepared data into the chosen model.
• Allow the model to adjust its internal parameters to better predict the output.
• Be cautious of overfitting and underfitting during the training process.
Step 5: Evaluating the Model
• Assess the model's performance on new data it hasn't seen during training.
• Use metrics such as accuracy, precision, recall, or mean squared error, depending on the type of problem.
Step 6: Hyperparameter Tuning and Optimization
• Adjust the model's hyperparameters to improve its performance.
• Utilize techniques like grid search or cross-validation to find optimal parameter values.
Step 7: Predictions and Deployment
• Use the trained and optimized model to make predictions on new data.
• Integrate the model into a production environment for real-time processing and insights (MLOps).
This step-by-step process highlights the iterative nature of machine learning, as steps like hyperparameter tuning and model evaluation may need to be revisited to achieve the best results.