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How to use machine learning models for organic searches (organic traffic insights). Choose the right features to target keywords | How to target keywords (SEO) Using NLP and web scraping. Extracting data from a html page (using Beautiful Soup) | Extracting data from a html page. How to use data to drive decisions | Use data to make business decisions. Mining data from the web Extracting data from a web page | Using Beautiful Soup. How to run machine learning models on the web | How to run machine learning models. How to extract data from the web | How to extract data from the web. Overfitting Overfitting vs underfitting | Weights and biases in machine learning (BAG) Multivariate linear regression: avoiding overfitting | How to avoid overfitting (L1 and L2 regularisation) Using Multiple Models Use multiple models in your workflow | Use multiple models. Combine multiple models into a workflow. Make models and data work together in the right way. | How to combine multiple models and data Using Data with Machine Learning Models Using data with machine learning models | Using data with machine learning models. Data augmentation. Creating More Data Creating data | Data to machine learning models. How to create data from scratch | How to create data from scratch. Building a Machine Learning Workflow Build a machine learning workflow. Select multiple models. Use data to drive decisions. Use data to make business decisions. Use multiple models in a workflow. Overcoming Problems Bias In statistical bias, systematic errors in the data or algorithms that make a model biased toward a particular outcome. Weights and biases in machine learning (BAG). Generalised Linear Models Generalised linear models: classification | Generalised linear models. Generalised linear models: regression | Generalised linear models. Generalised linear models: logistic regression. Generalised linear models: Poisson regression. Generalised linear models: regression with one response and one predictor. Generalised linear models: regression with more than one response. Generalised linear models: logistic regression with more than one predictor. Classification vs regression. The Basics Random Forest Random forest | Random Forest. Random Forest regression. Random forest regression (Titanic dataset). Automatic Learning Automatic learning: Gradient boosting machines. Selecting Models Selecting models: hyperparameter

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