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[公开课程][数据科学的Scala编程]

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2018-10-03 00:10:05
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https://cognitiveclass.ai/learn/scala/ Scala Programming for Data Science 英文字幕
学习
公开课
编程
数据科学
scala
ryuichisaga 发消息
视频选集
（23/75）
1.1 Introduction to Scala
03:48
1.2 Getting Started with Scala
05:03
1.3 Creating a Scala Project
06:50
1.4 The Scala REPL
05:50
1.5 Scala Documentation
06:10
2.1 Classes
05:21
2.2 Immutable and Mutable Fields
05:13
2.3 Methods
05:13
2.4 Default and Named Arguments
03:41
2.5 Objects
04:35
3.1 Companion Objects
03:45
3.2 Case Classes and Case Objects
04:56
3.3 Apply and Unapply
04:44
3.4 Synthetic Methods
05:17
3.5 Immutability and Thread Safety
05:34
4.1 Collections Overview
05:17
4.2 Sequences and Sets
08:10
4.3 Options
03:31
4.4 Tuples and Maps
06:06
4.5 Higher Order Functions
08:22
5.1 For Expressions
06:02
5.2 Pattern Matching
04:50
5.3 Handling Options
03:56
5.4 Handling Failures.mp4
05:07
5.5 Handling Futures
05:44
1.1 What is Spark
06:13
1.2 Our First Spark Application
04:55
1.3 The Spark Execution Model
04:14
1.4 Spark Web Console
03:26
1.5 Running Spark in a Standalone Cluster
03:13
2.1 Tuning RDDs
05:56
2.2 Spark Web Console-A Deep Dive
04:30
2.3 Broadcast Variables and Accumulators
03:52
2.4 More Transformations the Inverted Index Algorithm
07:00
2.5 Refining the Inverted Index
06:12
3.1 DataFrames for Large Scale Data Science
04:14
3.2 Exploring the DataFrame API
05:55
3.3 DataFrame Data Sources I
03:41
3.4 DataFrame Data Sources II
03:36
3.5 Speeding Up with DataFrames
04:22
4.1 DataFrame Joins
04:48
4.2 Other DataFrame Transformations
06:49
4.3 Using Hive with Spark
07:27
4.4 Spark Streaming
08:08
4.5 DataFrames and Spark Streaming Hive
06:12
5.1 Introduction to Spark MLlib
03:37
5.2 Overview of Spark ML Algorithms
09:12
5.3 Introduction to GraphX
05:35
5.4 Sentiment Analysis of Twitter Stream I
06:18
5.5 Sentiment Analysis of Twitter Stream II
04:11
1.1 Vectors and Labeled Points
07:07
1.2 Local and Distributed Matrices
08:49
1.3 Summary Statistics
05:31
1.4 Sampling
06:50
1.5 Hypothesis Testing
09:10
2.1 Statistics,Random Data and Sampling on DataFrames
12:17
2.2 Handling Missing Data and Imputing Values
11:08
2.3 Transformers and Estimators
08:41
2.4 Data Normalization
05:01
2.5 Identifying Outliers
07:33
3.1 Feature Vectors
03:29
3.2 Categorial Features
06:54
3.3 Using Explode, User Defined Functions and Pivot
04:19
3.4 PCA for Feature Engineering
09:13
3.5 RFormula
04:29
4.1 Decision Trees
08:46
4.2 Random Forests
07:16
4.3 Gradient Boosting Trees
07:11
4.4 Linear Methods
06:16
4.5 Evaluation
11:05
5.1 Introduction
04:28
5.2 Grants Creating Features
04:53
5.3 Grants Building a Pipeline
04:17
5.4 Grants Tuning the Model
03:48
5.5 Grants Wrapping Up
04:13

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