Online courses
This course describes the basic knowledge of data mining. On completion of this course, you will be able to know what data mining is, understand the differences between data mining and data analysis, master the data mining process, and understand the relationships between data, attributes, and measurement.
Introduction to Data Mining
Introduction to Data Mining
This course describes the basic mathematical knowledge used in data mining, unconstrained optimization, constrained optimization and their applications. Besides, this course describes the basic knowledge of Python and how to use Python in data collection and data visualization.
Basic Knowledge of Mathematics and Python
Basic Knowledge of Mathematics and Python
This course describes the methods used in common data preprocessing technologies, including missing value processing, outlier processing, feature scaling, value discretization, and imbalanced data processing. It expounds on the basic concept, usage, and application scenario of these methods.
Data Preprocessing
Data Preprocessing
This course describes the feature selection and dimension reduction approaches. As important processes in data mining, feature selection and dimension reduction can improve the performance, availability, and running efficiency of models, and are also important for modeling and understanding data.
Feature Selection and Dimension Reduction
Feature Selection and Dimension Reduction
Supervised learning involve regression algorithms and classification algorithms. These algorithms cover linear regression, logistic regression, KNN, Naive Bayes, SVM, decision tree (ID3, C4.5, and CART), and ensemble algorithms (AdaBoost, GBDT, and XGBoost). This course describes the principles and processes of popular supervised learning algorithms in detail.
Supervised Learning
Supervised Learning
HCIE-Big Data Data Mining V2.0 Series. The common knowledge of unsupervised learning algorithms includes clustering algorithm and association rule algorithm. On the clustering algorithm, we focus on representative algorithms under three different partitioning methods. For association algorithms, we focus on Apriori and FP-Growth.
Unsupervised Learning
Unsupervised Learning
The course describes how to optimize and evaluate selected models in an actual data mining project. It mainly involves the optimization and evaluation methods applicable to different models.
Model Evaluation and Optimization
Model Evaluation and Optimization
This course integrates the knowledge acquired related to data mining, ensuring good command of the general data mining process and comprehensive data mining application experiments.
Comprehensive Application of Data Mining
Comprehensive Application of Data Mining
This course describes the basics of Spark MLlib, as well as data mining content such as Spark MLlib model evaluation.
Spark MLlib Data Mining
Spark MLlib Data Mining
This course introduces Huawei Machine Learning Development Tools.
Huawei Machine Learning Development Tools
Huawei Machine Learning Development Tools
This course describes how to construct the big data architecture and how to select an appropriate architecture platform. It also provides a brief overview of big data governance.
Big Data Architecture and Governance
Big Data Architecture and Governance
The chapter describes three data mining cases to enhance the understanding of the data mining theory and its application to businesses.
Big Data Mining
Big Data Mining