Huawei HCIE Big Data Data Mining 2 0

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Videos Files :
Introduction to Data Mining Introduction to Data Mining EBG2.mp4 (5.96 MB)
1.1.1 Applications of Data Mining 1.1 Data Mining Overview E.mp4 (13.08 MB)
1.1.2 Data Mining Overview 1.1 Data Mining Overview EBG2020C.mp4 (35.39 MB)
1.1.3 Data Mining Methods 1.1 Data Mining Overview EBG2020CC.mp4 (45.18 MB)
1.2.1 Data Mining Process 1.2 Data Mining Process EBG2020CCH.mp4 (63.02 MB)
1.3.1 Data and Attributes Overview 1.3 Data, Attributes, and.mp4 (28.83 MB)
1.3.2 Measurements Overview 1.3 Data, Attributes, and Measur.mp4 (40.26 MB)
1.4.1 Data Mining Tools 1.4 Data Mining Tools EBG2020CCHW110.mp4 (31.72 MB)
1.5.1 Data Mining Skills Learning Path 1.5 Data Mining Skill.mp4 (12.3 MB)
10.1.1 Huawei ML Studio 10.1 Huawei ML Studio EBG2020CCHW110.mp4 (16.62 MB)
10.2.1 Applying for ML Studio 10.2 Applying for ML Studio EB.mp4 (9.3 MB)
10.3.1 Creating an ML Studio Workflow 10.3 Creating an ML St.mp4 (18.78 MB)
10.4.1 Application of Typical Algorithms 10.4 Application of.mp4 (43.13 MB)
10.5.1 Machine Learning Platform FusionInsight Miner 10.5 Ma.mp4 (33.42 MB)
11.1.1 Big data architecture overview （1） 11.1 Big data arch.mp4 (84.69 MB)
11.1.2 Big data architecture overview （2） 11.1 Big data arch.mp4 (65.79 MB)
11.1.3 Big data architecture 11.1 Big data architecture EBG2.mp4 (131.25 MB)
11.2.1 Big data governance 11.2 Big data governance EBG2020C.mp4 (128.09 MB)
12.1.1 Data Mining Background 12.1 Data Mining Background EB.mp4 (15.5 MB)
12.2.1 Accurate Customer Portraits of Banks 12.2 Accurate Cu.mp4 (57.74 MB)
12.3.1 Credit Card Security Improvement 12.3 Credit Card Sec.mp4 (42.12 MB)
12.4.1 Urban Environmental Quality Analysis 12.4 Urban Envir.mp4 (56.35 MB)
2.1.1 Determinant 2.1 Linear Algebra EBG2020CCHW1100041 Cour.mp4 (38.74 MB)
2.1.2 Matrix and Transformation 2.1 Linear Algebra EBG2020CC.mp4 (33.11 MB)
2.1.3 Eigenvalue Decomposition 2.1 Linear Algebra EBG2020CCH.mp4 (34.31 MB)
2.1.4 Singular Value Decomposition 2.1 Linear Algebra EBG202.mp4 (20.56 MB)
2.1.5 Linear Transformation 2.1 Linear Algebra EBG2020CCHW11.mp4 (26.33 MB)
2.2.1 Events and Their Probabilities 2.2 Probability Theory.mp4 (30.28 MB)
2.2.2 Discrete Distributions 2.2 Probability Theory and Math.mp4 (37.91 MB)
2.2.3 Continuous Distributions 2.2 Probability Theory and Ma.mp4 (26.97 MB)
2.2.4 Random Vectors and Their Distributions 2.2 Probability.mp4 (44.78 MB)
2.2.5 Law of Large Numbers and Central Limit Theorem 2.2 Pro.mp4 (54.33 MB)
2.2.6 Sample and Sampling Distribution 2.2 Probability Theor.mp4 (37.8 MB)
2.2.7 Parameter Estimation and Hypothesis Testing 2.2 Probab.mp4 (64.91 MB)
2.3.1 Information Entropy and Gini Coefficient 2.3 Informati.mp4 (21.52 MB)
2.4.1 Gradient Descent Method 2.4 Optimization EBG2020CCHW11.mp4 (57.38 MB)
2.4.2 Constrained Optimization 2.4 Optimization EBG2020CCHW1.mp4 (36.41 MB)
2.5.1 Python Overview 2.5 Python Overview EBG2020CCHW1100041.mp4 (27.91 MB)
2.5.2 Python Basics 2.5 Python Overview EBG2020CCHW1100041 C.mp4 (32.41 MB)
2.5.3 Data Types in Python Number, String, List 2.5 Python.mp4 (39.21 MB)
2.5.4 Data Types in Python Tuple, Dictionary, Set 2.5 Pyth.mp4 (25.8 MB)
2.5.5 Conditionals and Loops 2.5 Python Overview EBG2020CCHW.mp4 (30.87 MB)
2.5.6 Functions and Object oriented Process 2.5 Python Overv.mp4 (61.57 MB)
2.5.7 Common Modules 2.5 Python Overview EBG2020CCHW1100041.mp4 (34.31 MB)
2.5.8 Regular Expression 2.5 Python Overview EBG2020CCHW1100.mp4 (37.6 MB)
2.5.9 File Operation 2.5 Python Overview EBG2020CCHW1100041.mp4 (32.36 MB)
2.6.1 Data Collection 2.6 Data Collection EBG2020CCHW1100041.mp4 (74.19 MB)
2.7.1 Data Visualization 2.7 Data Visualization EBG2020CCHW1.mp4 (42.17 MB)
3.1.1 Overview 3.1 Data Extraction, Transformation, and Load.mp4 (38.28 MB)
3.1.2 Extraction, Transformation and Loading 3.1 Data Extrac.mp4 (44.9 MB)
3.1.3 Introduction to ELT 3.1 Data Extraction, Transformatio.mp4 (28.91 MB)
3.2.1 Missing Value Treatment 3.2 Data Cleaning EBG2020CCHW1.mp4 (77.02 MB)
3.2.2 Outlier Treatment 3.2 Data Cleaning EBG2020CCHW1100041.mp4 (33.17 MB)
3.3.1 Feature Scaling 3.3 Feature Processing EBG2020CCHW1100.mp4 (39.46 MB)
3.3.2 Discretization 3.3 Feature Processing EBG2020CCHW11000.mp4 (70.2 MB)
3.3.3 Feature Encoding 3.3 Feature Processing EBG2020CCHW110.mp4 (48.48 MB)
3.4.1 Handling Imbalanced Data 3.4 Handling Imbalanced Data.mp4 (31.28 MB)
4.1.1 Overview of Feature Selection 4.1 Feature Selection EB.mp4 (49.73 MB)
4.1.2 Feature Selection Filter 4.1 Feature Selection EBG20 1.mp4 (64.67 MB)
4.1.2 Feature Selection Filter 4.1 Feature Selection EBG20.mp4 (66.52 MB)
4.1.3 Feature Selection Wrapper 4.1 Feature Selection EBG2.mp4 (37.74 MB)
4.1.4 Feature Selection Embedded 4.1 Feature Selection EBG 1.mp4 (43.91 MB)
4.1.4 Feature Selection Embedded 4.1 Feature Selection EBG.mp4 (40.95 MB)
4.1.5 Other Methods and Feature Expanding 4.1 Feature Select.mp4 (49.2 MB)
4.2.1 Overview of Dimension Reduction 4.2 Dimension Reductio.mp4 (38.76 MB)
4.2.2 Singular Value Decomposition 4.2 Dimension Reduction E.mp4 (50 MB)
4.2.3 Principal Component Analysis 4.2 Dimension Reduction E.mp4 (55.03 MB)
4.2.4 Linear Discriminant Analysis 4.2 Dimension Reduction E.mp4 (68.05 MB)
4.2.5 Locally Linear Embedding 4.2 Dimension Reduction EBG20.mp4 (65.34 MB)
5.1.1 Supervised Learning Background Knowledge 5.1 Supervise.mp4 (39.98 MB)
5.2.1 Linear Regression 5.2 Linear Regression EBG2020CCHW110.mp4 (40.57 MB)
5.3.1 Logistic Regression 5.3 Logistic Regression EBG2020CCH.mp4 (22.98 MB)
5.4.1 K Nearest Neighbor Algorithm 5.4 K Nearest Neighbor Al.mp4 (12.37 MB)
5.5.1 Naive Bayes 5.5 Naive Bayes EBG2020CCHW1100041 Coursew.mp4 (38.06 MB)
5.6.1 Linear SVM 5.6 SVM EBG2020CCHW1100041 Courseware Huawe.mp4 (57.27 MB)
5.6.2 Nonlinear SVM 5.6 SVM EBG2020CCHW1100041 Courseware Hu.mp4 (46.74 MB)
5.7.1 ID3 5.7 Decision Tree EBG2020CCHW1100041 Courseware Hu.mp4 (48.88 MB)
5.7.2 C4.5 5.7 Decision Tree EBG2020CCHW1100041 Courseware H.mp4 (48.7 MB)
5.7.3 CART 5.7 Decision Tree EBG2020CCHW1100041 Courseware H.mp4 (38.46 MB)
5.8.1 Bagging 5.8 Ensemble Algorithm EBG2020CCHW1100041 Cour.mp4 (58.07 MB)
5.8.2 Random Forest 5.8 Ensemble Algorithm EBG2020CCHW110004.mp4 (44.62 MB)
5.8.3 Boosting and AdaBoost 5.8 Ensemble Algorithm EBG2020CC.mp4 (46.97 MB)
5.8.4 GBDT 5.8 Ensemble Algorithm EBG2020CCHW1100041 Coursew.mp4 (28.5 MB)
5.8.5 XGBoost 5.8 Ensemble Algorithm EBG2020CCHW1100041 Cour.mp4 (40.51 MB)
6.1.1 Unsupervised Learning 6.1 Unsupervised Learning EBG202.mp4 (25.02 MB)
6.2.1 Concept of Cluster Analysis 6.2 Clustering Algorithm E.mp4 (61.05 MB)
6.2.2 K Means Algorithm 6.2 Clustering Algorithm EBG2020CCHW 1.mp4 (45.34 MB)
6.2.2 K Means Algorithm 6.2 Clustering Algorithm EBG2020CCHW.mp4 (41.55 MB)
6.2.3 K Mediods Algorithm 6.2 Clustering Algorithm EBG2020CC.mp4 (28.09 MB)
6.2.4 Hierarchical Clustering Algorithm 6.2 Clustering Algor.mp4 (51.46 MB)
6.2.5 BIRCH Algorithm 6.2 Clustering Algorithm EBG2020CCHW11 1.mp4 (42.81 MB)
6.2.5 BIRCH Algorithm 6.2 Clustering Algorithm EBG2020CCHW11.mp4 (36.3 MB)
6.3.1 Apriori Algorithm 6.3 Association Algorithm EBG2020CCH 1.mp4 (49.47 MB)
6.3.1 Apriori Algorithm 6.3 Association Algorithm EBG2020CCH.mp4 (37.84 MB)
6.3.2 FP Growth Algorithm 6.3 Association Algorithm EBG2020C 1.mp4 (61.4 MB)
6.3.2 FP Growth Algorithm 6.3 Association Algorithm EBG2020C.mp4 (47.08 MB)
7.1.1 Prerequisites for Model Evaluation and Optimization 7..mp4 (56.51 MB)
7.2.1 Overview of Optimization Models 7.2 Optimization Model.mp4 (31.05 MB)
7.2.2 Classification of Optimization Models 7.2 Optimization.mp4 (103.67 MB)
7.3.1 Model Evaluation Overview 7.3 Model Evaluation and Sel.mp4 (73.45 MB)
7.3.2 Regression, Classification and Clustering 7.3 Model Ev.mp4 (63.12 MB)
7.4.1 Regularization 7.4 Regularization EBG2020CCHW1100041 C.mp4 (16.77 MB)
8.1.1 Data Mining Process Overview 8.1 Data Mining Process E 1.mp4 (51.36 MB)
8.1.1 Data Mining Process Overview 8.1 Data Mining Process E.mp4 (31.28 MB)
8.1.2 Feature Engineering 8.1 Data Mining Process EBG2020CCH.mp4 (20.78 MB)
8.2.1 Model Selection and Evaluation 8.2 Case Study of Compr.mp4 (65.85 MB)
9.1.1 RDDs, DataFrames and Datasets 9.1 Spark MLlib Basics E.mp4 (39.42 MB)
9.1.2 Spark RDD Operators 9.1 Spark MLlib Basics EBG2020CCHW.mp4 (43.17 MB)
9.1.3 Matrix Vectors of Spark MLlib 9.1 Spark MLlib Basics E.mp4 (24.46 MB)
9.2.1 Basic Statistics and Analysis of Spark MLlib 9.2 Basic.mp4 (43 MB)
9.3.1 Feature Extraction and Transformation of Spark MLlib 9.mp4 (79.42 MB)
9.4.1 Classification and Regression Overview 9.4 Classificat.mp4 (57.07 MB)
9.4.2 Classification and Regression Models 9.4 Classificatio.mp4 (41.05 MB)
9.5.1 Clustering and Dimensionality Reduction of Spark Mllib.mp4 (36.17 MB)
9.6.1 Association Rules and Recommendation Algorithms of Spa.mp4 (34.23 MB)
9.7.1 Evaluation Matrix of Spark Mllib 9.7 Evaluation Matrix.mp4 (43.88 MB)

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