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コース概要
Introduction
This section provides a general introduction of when to use 'machine learning', what should be considered and what it all means including the pros and cons. Datatypes (structured/unstructured/static/streamed), data validity/volume, data driven vs user driven analytics, statistical models vs. machine learning models/ challenges of unsupervised learning, bias-variance trade off, iteration/evaluation, cross-validation approaches, supervised/unsupervised/reinforcement.
MAJOR TOPICS
1. Understanding naive Bayes
- Basic concepts of Bayesian methods
- Probability
- Joint probability
- Conditional probability with Bayes' theorem
- The naive Bayes algorithm
- The naive Bayes classification
- The Laplace estimator
- Using numeric features with naive Bayes
2. Understanding decision trees
- Divide and conquer
- The C5.0 decision tree algorithm
- Choosing the best split
- Pruning the decision tree
3. Understanding neural networks
- From biological to artificial neurons
- Activation functions
- Network topology
- The number of layers
- The direction of information travel
- The number of nodes in each layer
- Training neural networks with backpropagation
- Deep Learning
4. Understanding Support Vector Machines
- Classification with hyperplanes
- Finding the maximum margin
- The case of linearly separable data
- The case of non-linearly separable data
- Using kernels for non-linear spaces
5. Understanding clustering
- Clustering as a machine learning task
- The k-means algorithm for clustering
- Using distance to assign and update clusters
- Choosing the appropriate number of clusters
6. Measuring performance for classification
- Working with classification prediction data
- A closer look at confusion matrices
- Using confusion matrices to measure performance
- Beyond accuracy – other measures of performance
- The kappa statistic
- Sensitivity and specificity
- Precision and recall
- The F-measure
- Visualizing performance tradeoffs
- ROC curves
- Estimating future performance
- The holdout method
- Cross-validation
- Bootstrap sampling
7. Tuning stock models for better performance
- Using caret for automated parameter tuning
- Creating a simple tuned model
- Customizing the tuning process
- Improving model performance with meta-learning
- Understanding ensembles
- Bagging
- Boosting
- Random forests
- Training random forests
- Evaluating random forest performance
MINOR TOPICS
8. Understanding classification using the nearest neighbors
- The kNN algorithm
- Calculating distance
- Choosing an appropriate k
- Preparing data for use with kNN
- Why is the kNN algorithm lazy?
9. Understanding classification rules
- Separate and conquer
- The One Rule algorithm
- The RIPPER algorithm
- Rules from decision trees
10. Understanding regression
- Simple linear regression
- Ordinary least squares estimation
- Correlations
- Multiple linear regression
11. Understanding regression trees and model trees
- Adding regression to trees
12. Understanding association rules
- The Apriori algorithm for association rule learning
- Measuring rule interest – support and confidence
- Building a set of rules with the Apriori principle
Extras
- Spark/PySpark/MLlib and Multi-armed bandits
21 時間
お客様の声 (5)
短くシンプルにしてください。概念に基づいた直感的および視覚的なモデルを作成します (デシジョン ツリー グラフ、線形方程式、手動で y_pred を計算してモデルがどのように機能するかを証明します)。
Nicolae - DB Global Technology
コース - Machine Learning
Machine Translated
ML を理解するという目標を達成するのに役立ちました。このトピックがいかに広大であるかは、3 日間のトレーニング後に明らかになるため、このトピックについて適切に紹介してくれた Pablo に多大な敬意を表します。また、レイテンシが非常に優れた、あなたが提供した仮想マシンのアイデアもとても気に入りました。これにより、すべての研究者が自分のペースで実験を行うことができました。
Silviu - DB Global Technology
コース - Machine Learning
Machine Translated
The way practical part, seeing the theory materializing into something practical is great.
Lisa Fekade - Vodacom
コース - Machine Learning
It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.
Attila Nagy - L M ERICSSON LIMITED
コース - Machine Learning
I liked the lab exercises.
