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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-machine-learning-ds-600-real-interview-questions/
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课程名称：机器学习与数据科学：600个真实面试问题 课程概述：该课程汇聚了600多个技术公司常问的机器学习和数据科学面试问题，旨在帮助学习者掌握机器学习与数据科学的知识。通过这个全面的课程，参与者将获得在数据科学职业生涯中所需的知识和自信。课程包含超过600个真实面试问题及详细解读，让学习者深入理解核心概念、实践技能和高级技术。 学习内容包括： - 机器学习背后的基础数学知识，包括代数、微积分、统计和概率。 - 数据收集、整理和预处理技术，使用强大的工具如Pandas和NumPy。 - 关键机器学习算法，如回归、分类、决策树和模型评估。 - 深度学习基础，包括神经网络、计算机视觉和自然语言处理。 这个课程适合初学者和希望提升技能的专业人士，提供实用知识、真实案例以及面试准备策略，帮助学员在竞争激烈的数据科学领域脱颖而出。 示例问题： 1. 在构建客户流失预测模型时，如果数据集高度失衡，您会采用什么技术来改善模型评估，确保模型不受失衡类别的影响？ A) 使用k折交叉验证评估模型在所有数据分割上的表现。 B) 在交叉验证中使用分层抽样，以保持每个折中的类别分布。 C) 在训练模型之前使用随机过采样来平衡类别。 D) 使用自助抽样法随机抽样数据，进行多次训练。 2. 您在使用交叉验证训练模型时，注意到模型的性能指标（如准确率）在不同折之间波动很大。您可以采取什么方法来减少这些估计的方差，从而获得更可靠的模型评估？ A) 应用自助抽样法生成多个随机样本。 B) 使用更多的折进行交叉验证（例如，使用10折而不是5折）。 C) 通过添加更多特征来增加数据集的大小。 D) 在每个折上训练模型多次并平均结果。 今天就报名，提升自己在机器学习和数据科学世界中成功的知识和实践，通过掌握领先科技公司提出的真实问题，为您的职业生涯打下基础！
This course features 600+ Real and Most Asked Interview Questions for Machine Learning and Data Science that leading tech companies have asked. Are you ready to master machine learning and data science? This comprehensive course, Master Machine Learning and Data Science: 600+ Real Interview Questions is designed to equip you with the knowledge and confidence needed to excel in your data science career. With over 600 real interview questions and detailed explanations, you'll gain a deep understanding of core concepts, practical skills, and advanced techniques.What You'll Learn:The essential maths behind machine learning, including algebra, calculus, statistics, and probability.Data collection, wrangling, and preprocessing techniques using powerful tools like Pandas and NumPy.Key machine learning algorithms such as regression, classification, decision trees, and model evaluation.Deep learning fundamentals, including neural networks, computer vision, and natural language processing.Whether you're a beginner or a professional looking to sharpen your skills, this course offers practical knowledge, real-world examples, and interview preparation strategies to help you stand out in the competitive field of data science. Join us and take the next step toward mastering machine learning and data science!Sample Questions:Question 1:You are building a predictive model for customer churn using a dataset that is highly imbalanced, with a much larger number of non-churning customers than churning ones. What technique would you apply to improve model evaluation and ensure that the model is not biased by the imbalanced classes?A) Use k-fold cross-validation to assess model performance across all data splits. B) Use stratified sampling in your cross-validation to maintain the class distribution in each fold. C) Use random oversampling to balance the classes before training the model. D) Use bootstrapping to randomly sample the data and train the model on multiple iterations.Question 2:You are training a model using cross-validation and notice that the model's performance metrics, such as accuracy, fluctuate significantly across different folds. What method can you apply to reduce the variance of these estimates and obtain a more reliable evaluation of your model?A) Apply bootstrapping to generate multiple random samples of the dataset. B) Use a larger number of folds for cross-validation (e.g., 10-fold instead of 5-fold). C) Increase the size of the dataset by adding more features. D) Train the model on each fold multiple times and average the results.Enroll today and equip yourself with the knowledge and practice needed to succeed in the world of Machine Learning and Data Science by mastering real questions that leading tech companies have asked.
