The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate)

Module 1 challenge :The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025

. 1. Supervised vs Unsupervised ML ✔ In unsupervised machine learning, data professionals ask the model to give them information without telling the model what the answer should be.✔ Supervised machine learning uses labeled datasets to train algorithms to classify or predict outcomes.✔ Data professionals use supervised machine learning for prediction.❌ In supervised machine learning,… <a href="https://codeshala.io/platform/coursera/course/the-nuts-and-bolts-of-machine-learning-google-advanced-data-analytics-professional-certificate/assignment/module-1-challenge-the-nuts-and-bolts-of-machine-learning-google-advanced-data-analytics-professional-certificate-answers-2025/" rel="bookmark">Module 1 challenge :The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025</a>

Module 2 challenge :The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025

1. Feature engineering ❌ Feature engineering does not involve using a data professional’s statistical knowledge.✔ Feature engineering may involve transforming the properties of raw data.✔ Feature selection involves choosing features that contribute the most to predicting the response.✔ Feature extraction involves creating new features from multiple existing ones to improve accuracy. 2. Class imbalance resolution… <a href="https://codeshala.io/platform/coursera/course/the-nuts-and-bolts-of-machine-learning-google-advanced-data-analytics-professional-certificate/assignment/module-2-challenge-the-nuts-and-bolts-of-machine-learning-google-advanced-data-analytics-professional-certificate-answers-2025/" rel="bookmark">Module 2 challenge :The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025</a>

Module 3 challenge:The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025

1. Key aspects of k-means ❌ The clustering process has four steps that repeat until the model disperses evenly.✔ K-means organizes data into clusters by creating a logical scheme to make sense of it.✔ Poor clustering can be caused by local minima, which means the model has converged in a sub-optimal way.✔ K-means groups unlabeled… <a href="https://codeshala.io/platform/coursera/course/the-nuts-and-bolts-of-machine-learning-google-advanced-data-analytics-professional-certificate/assignment/module-3-challengethe-nuts-and-bolts-of-machine-learning-google-advanced-data-analytics-professional-certificate-answers-2025/" rel="bookmark">Module 3 challenge:The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025</a>

Module 4 challenge :The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025

1. Nodes examined in tree-based learning ❌ Root✔ Decision❌ Leaf❌ Branch 2. Benefits of decision trees ✔ Decision trees enable data professionals to make predictions about future events based on currently available information.✔ Very little preprocessing is required.✔ No assumptions regarding distribution of data.❌ Overfitting is unlikely (Overfitting is actually a common issue with decision… <a href="https://codeshala.io/platform/coursera/course/the-nuts-and-bolts-of-machine-learning-google-advanced-data-analytics-professional-certificate/assignment/module-4-challenge-the-nuts-and-bolts-of-machine-learning-google-advanced-data-analytics-professional-certificate-answers-2025/" rel="bookmark">Module 4 challenge :The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025</a>

Assess your Course 6 end-of-course project :The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025

1. Applicable packages and libraries were imported to the code notebook.✔ Yes❌ No 2. Categorical variables were encoded as binary variables.✔ Yes❌ No 3. A target variable was assigned.✔ Yes❌ No 4. An evaluation metric was chosen.✔ Yes❌ No 5. The data was split into training and testing sets.✔ Yes❌ No 6. The following steps… <a href="https://codeshala.io/platform/coursera/course/the-nuts-and-bolts-of-machine-learning-google-advanced-data-analytics-professional-certificate/assignment/assess-your-course-6-end-of-course-project-the-nuts-and-bolts-of-machine-learning-google-advanced-data-analytics-professional-certificate-answers-2025/" rel="bookmark">Assess your Course 6 end-of-course project :The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025</a>