آموزش Coursera Mining Massive Datasets

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آموزش پیاده سازی الگوریتم ها و فرمول های ریاضی Data Mining بر روی مجموعه داده های بسیار بزرگ - با زیرنویس انگلیسی

تولید کننده: Coursera

شناسه کالا: 51080449CO178

قیمت: 5,500 تومان
تعداد:
ویدیوی نمونه آموزش Coursera Mining Massive Datasets

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داده هایی که متخصصان تحلیل داده ، هوش تجاری ، بیگ دیتا و هوش مصنوعی باید بر روی آن ها کار کنند هر روز بزرگ و وسیع تر می شود ، با مشاهده این مجموعه آموزش بسیار ارزشمند با فرمول های ریاضی و تکنیک های عملی و الگوریتم هایی آشنا شده و آن ها را بخوبی یاد می گیرید که بوسیله آن ها می توان Data Mining را بر روی مجموعه داده های بسیار بزرگ پیاده کنید.

عنوان اصلی : Mining Massive Datasets

این مجموعه آموزش ویدیویی محصول موسسه آموزشی Coursera است که بر روی 1 حلقه DVD به همراه اسلایدهای مدرس و به مدت زمان 20 ساعت و 5 دقیقه در اختیار علاقه مندان قرار می گیرد.

در ادامه با برخی از سرفصل های درسی این مجموعه آموزش آشنا می شویم :

Week 1 Materials - 01 Distributed File Systems 15-50
Week 1 Materials - 02 The MapReduce Computational Model 22-04
Week 1 Materials - 03 Scheduling and Data Flow 12-43
Week 1 Materials - 04 Combiners and Partition Functions 12-17 Advanced
Week 1 Materials - 05 Link Analysis and PageRank 9-39
Week 1 Materials - 06 PageRank- The Flow Formulation 9-16
Week 1 Materials - 07 PageRank- The Matrix Formulation 8-02
Week 1 Materials - 08 PageRank- Power Iteration 10-34
Week 1 Materials - 09 PageRank- The Google Formulation 12-08
Week 1 Materials - 10 Why Teleports Solve the Problem 12-26
Week 1 Materials - 11 How we Really Compute PageRank 13-49
Week 2 Materials - 01 Finding Similar Sets 13-37
Week 2 Materials - 02 Minhashing 25-18
Week 2 Materials - 03 Locality-Sensitive Hashing 19-24
Week 2 Materials - 04 Applications of LSH 11-40
Week 2 Materials - 05 Fingerprint Matching 7-07
Week 2 Materials - 06 Finding Duplicate News Articles 6-08
Week 2 Materials - 07 Distance Measures 22-39
Week 2 Materials - 08 Nearest Neighbor Learning 11-39
Week 2 Materials - 09 Frequent Itemsets 29-50
Week 2 Materials - 10 A-Priori Algorithm 13-07
Week 2 Materials - 11 Improvements to A-Priori 17-26  Advanced
Week 2 Materials - 12 All or Most Frequent Itemsets in 2 Passes 14-40 Advanced
Week 3 Materials - 01 Community Detection in Graphs- Motivation 5-44
Week 3 Materials - 02 The Affiliation Graph Model 10-04
Week 3 Materials - 03 From AGM to BIGCLAM 8-48
Week 3 Materials - 04 Solving the BIGCLAM 9-19
Week 3 Materials - 05 Detecting Communities as Clusters 8-39 Advanced
Week 3 Materials - 06 What Makes a Good Cluster 8-48 Advanced
Week 3 Materials - 07 The Graph Laplacian Matrix 6-51 Advanced
Week 3 Materials - 08 Examples of Eigendecompositions of Graphs 6-16 Advanced
Week 3 Materials - 09 Defining the Graph Laplacian 3-27 Advanced
Week 3 Materials - 10 Spectral Graph Partitioning- Finding a Partition 13-25 Advanced
Week 3 Materials - 11 Spectral Clustering- Three Steps 7-17 Advanced
Week 3 Materials - 12 Analysis of Large Graphs- Trawling 9-02 Advanced
Week 3 Materials - 13 Mining Data Streams 12-01
Week 3 Materials - 14 Counting 1s 29-00 Advanced
Week 3 Materials - 15 Bloom Filters 18-00
Week 3 Materials - 16 Sampling a Stream 11-30
Week 3 Materials - 17 Counting Distinct Elements 25-59 Advanced
Week 4 Materials - 01 Overview of Recommender Systems 16-51
Week 4 Materials - 02 Content-Based Recommendations 21-00
Week 4 Materials - 03 Collaborative Filtering 20-52
Week 4 Materials - 04 Implementing Collaborative Filtering 13-46 Advanced
Week 4 Materials - 05 Evaluating Recommender Systems 6-09
Week 4 Materials - 06 Dimensionality Reduction- Introduction 12-01
Week 4 Materials - 07 Singular-Value Decomposition 13-39
Week 4 Materials - 08 Dimensionality Reduction with SVD 9-04
Week 4 Materials - 09 SVD Gives the Best Low-Rank Approximation 8-28 Advanced
Week 4 Materials - 10 SVD Example and Conclusion 11-58
Week 4 Materials - 11 CUR Decomposition 6-27 Advanced
Week 4 Materials - 12 The CUR Algorithm 6-15 Advanced
Week 4 Materials - 13 Discussion of the CUR Method 7-09
Week 4 Materials - 14 Latent-Factor Models 16-11
Week 4 Materials - 15 Latent-Factor Recommender System 14-16
Week 4 Materials - 16 Finding the Latent Factors 13-20
Week 4 Materials - 17 Extension to Include Global Effects 9-42 Advanced
Week 5 Materials - 01 Overview of Clustering 8-46
Week 5 Materials - 02 Hierarchical Clustering 14-07
Week 5 Materials - 03 The k-Means Algorithm 12-49
Week 5 Materials - 04 The BFR Algorithm 25-01
Week 5 Materials - 05 The CURE Algorithm 15-13 Advanced
Week 5 Materials - 06 Computational Advertising- Bipartite Graph Matching 24-47
Week 5 Materials - 07 The AdWords Problem 19-21
Week 5 Materials - 08 The Balance Algorithm 15-16
Week 5 Materials - 09 Generalized Balance 14-35 Advanced
Week 6 Materials - 01 Support Vector Machines- Introduction 7-30
Week 6 Materials - 02 Support Vector Machines- Mathematical Formulation 12-15
Week 6 Materials - 03 What is the Margin 8-22
Week 6 Materials - 04 Soft-Margin SVMs 9-46
Week 6 Materials - 05 How to Compute the Margin 14-36 Advanced
Week 6 Materials - 06 Support Vector Machines- Example 7-07
Week 6 Materials - 07 Decision Trees 8-33
Week 6 Materials - 08 How to Construct a Tree 13-21
Week 6 Materials - 09 Information Gain 9-50
Week 6 Materials - 10 Building Decision Trees Using MapReduce 8-14 Advanced
Week 6 Materials - 11 Decision Trees- Conclusion 7-25
Week 6 Materials - 12 MapReduce Algorithms Part I 10-51 Advanced
Week 6 Materials - 13 MapReduce Algorithms Part II 9-46 Advanced
Week 6 Materials - 14 Theory of MapReduce Algorithms 19-39 Advanced
Week 6 Materials - 15 Matrix Multiplication in MapReduce 24-48 Advanced
Week 7 Materials - 01 LSH Families 21-13
Week 7 Materials - 02 More About LSH Families 12-57
Week 7 Materials - 03 Sets and Strings With a High Degree of Similarity 11-29 Advanced
Week 7 Materials - 04 Prefix of a String 7-43 Advanced
Week 7 Materials - 05 Positions Within Prefixes 14-04 Advanced
Week 7 Materials - 06 Exploiting Length 14-39 Advanced
Week 7 Materials - 07 Computing PageRank on Big Graphs 10-18 Advanced
Week 7 Materials - 08 Topic-Specific PageRank 10-06
Week 7 Materials - 09 Application to Measuring Proximity in Graphs 6-25
Week 7 Materials - 10 Hubs and Authorities 15-16 Advanced
Week 7 Materials - 11 Web Spam- Introduction 6-50
Week 7 Materials - 12 Spam Farms 8-00
Week 7 Materials - 13 TrustRank 10-05

مشخصات این مجموعه :
زبان آموزش ها انگلیسی روان و ساده
دارای آموزشهای ویدیویی و دسته بندی شده
ارائه شده بر روی 1 حلقه DVD به همراه اسلایدهای مدرس
مدت زمان آموزش 20 ساعت و 5 دقیقه !
محصول موسسه آموزشی Coursera

خصوصیات محصول
زبان انگلیسی ساده و روان
زیرنویس انگلیسی
حجم به مگابایت 1970
زمان به دقیقه 1205
تعداد دیسک 1

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