###### tags: `data_science` `machine_learning`
# How to Study Data Science

## ML 基礎
- [Coursera 機械学習](https://www.coursera.org/learn/machine-learning)
- [ゼロから作るDeep Learning① (NN & CNN)](https://www.amazon.co.jp/%E3%82%BC%E3%83%AD%E3%81%8B%E3%82%89%E4%BD%9C%E3%82%8BDeep-Learning-%E2%80%95Python%E3%81%A7%E5%AD%A6%E3%81%B6%E3%83%87%E3%82%A3%E3%83%BC%E3%83%97%E3%83%A9%E3%83%BC%E3%83%8B%E3%83%B3%E3%82%B0%E3%81%AE%E7%90%86%E8%AB%96%E3%81%A8%E5%AE%9F%E8%A3%85-%E6%96%8E%E8%97%A4-%E5%BA%B7%E6%AF%85/dp/4873117585)
- [ゼロから作るDeep Learning② (RNN)](https://www.amazon.co.jp/%E3%82%BC%E3%83%AD%E3%81%8B%E3%82%89%E4%BD%9C%E3%82%8BDeep-Learning-%E2%80%95Python%E3%81%A7%E5%AD%A6%E3%81%B6%E3%83%87%E3%82%A3%E3%83%BC%E3%83%97%E3%83%A9%E3%83%BC%E3%83%8B%E3%83%B3%E3%82%B0%E3%81%AE%E7%90%86%E8%AB%96%E3%81%A8%E5%AE%9F%E8%A3%85-%E6%96%8E%E8%97%A4-%E5%BA%B7%E6%AF%85/dp/4873117585)
- [論文解説 Attention Is All You Need (Transformer)](http://deeplearning.hatenablog.com/entry/transformer)
- [作って理解する Transformer / Attention](https://qiita.com/halhorn/items/c91497522be27bde17ce)
- Paper Reading:
- 参考
- 論文：[Deep Speaker: an End-to-End Neural Speaker Embedding System](https://arxiv.org/abs/1705.02304v1)
- 論文：[Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks](https://arxiv.org/abs/1808.10687)
- [夏のトップカンファレンス論文読み会 / Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://www.slideshare.net/ShunsukeOno/realtime-multiperson-2d-pose-estimation-using-part-affinity-fields-78988208)

## ML 応用
- [詳説ディープラーニング（生成モデル編）](https://note.com/yusugomori/n/n945f51cabc03?scrollpos=KqPWs)(生成モデル, [誤植](https://docs.google.com/document/d/1c1n3ztWVKPxaAiKSWLsmbvAU25vJCAl3skw_0eQLs3w/edit))
- [はじめてのGAN](https://elix-tech.github.io/ja/2017/02/06/gan.html)
- [Variational Autoencoder徹底解説](https://qiita.com/kenmatsu4/items/b029d697e9995d93aa24)
- [PythonとKerasによるディープラーニング (Keras)](https://www.amazon.co.jp/dp/B07D498RJK/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1)
- tensorflow2.0を使うとlayerの名前でエラー出るらしいので、実装の際はtensorflowのversion下げるかlayerにprefixつける

## Data Science in Cousera
### 注意
- 有料のやつは以下の手順で受講する
1. コースに飛ぶ
2. 各コースの項目をクリック
3. 「無料で登録」
4. **「聴講コース」を選ぶ**
- 無料トライアルを始めるだと課金になる

### Level.1
#### 数学基礎
- [ ] [Data Science Math Skills(無料)](https://www.coursera.org/learn/datasciencemathskills?)
- 13Hr
- 高校数学なのでやらなくていい
- [ ] [機械学習のための数学専門講座(有料)](https://www.coursera.org/specializations/mathematics-machine-learning)
- 16Hr

#### R & ベイズ統計, 機械学習
- [ ] [Statistics with R専門講座(有料)](https://www.coursera.org/specializations/statistics)
- 84Hr
- [ ] [Bayesian Statistics: From Concept to Data Analysis(無料)](https://www.coursera.org/learn/bayesian-statistics)
- 12Hr
- [ ] [Bayesian Statistics: Techniques and Models(無料)](https://www.coursera.org/learn/mcmc-bayesian-statistics)
- 30Hr

#### Python & 統計, 機械学習
- [ ] [Statistics with Python専門講座(有料)](https://www.coursera.org/specializations/statistics-with-python)
- 60Hr
- [ ] [IBM AIエンジニアリング プロフェッショナル認定(有料)](https://www.coursera.org/professional-certificates/ai-engineer#courses)
- 84Hr

### Level.2
#### 機械学習、ディープラーニング
- [ ] [機械学習(無料)](https://www.coursera.org/learn/machine-learning)
- 61Hr
- [ ] [ディープラーニング専門講座(有料)](https://www.coursera.org/specializations/deep-learning)
- 160hr
- [ ] [上級機械学習専門講座(有料)](https://www.coursera.org/specializations/aml)
- 240Hr

#### 確率過程、時系列
- [ ] [確率過程(無料)](https://www.coursera.org/learn/stochasticprocesses?)
- 23Hr
- [ ] [Practical Time Series Analysis(無料)](https://www.coursera.org/learn/practical-time-series-analysis)

### Level.3
#### 数学基礎
- [ ] [Mathematics for Data Science専門講座(有料)](https://www.coursera.org/specializations/mathematics-for-data-science)
- 96Hr
- [ ] [Introduction to Discrete Mathematics for Computer Science専門講座(有料)](https://www.coursera.org/specializations/discrete-mathematics?)
- 120Hr

#### ベイズ統計,グラフ理論
- [ ] [Bayesian Statistics: Mixture Models(無料)](https://www.coursera.org/learn/mixture-models)
- 22Hr
- [ ] [Probabilistic Graphical Models 専門講座(有料)](https://www.coursera.org/specializations/probabilistic-graphical-models?)
- 176Hr

#### 金融, 経済, 機械学習
- [ ] [Reinforcement Learning for Trading Strategies(有料)](https://www.coursera.org/learn/trading-strategies-reinforcement-learning?)
- 12Hr
- [ ] [Finance & Quantitative Modeling for Analysts専門講座(有料)](https://www.coursera.org/specializations/finance-quantitative-modeling-analysts)
- 32Hr

### speciality
#### 経済, 金融, 投資
- [ ] [Investment Management with Python and Machine Learning専門講座(有料)](https://www.coursera.org/specializations/investment-management-python-machine-learning)
- 80Hr
- [ ] [Fundamentals of Quantitative Modeling
(有料)](https://www.coursera.org/learn/wharton-quantitative-modeling)
- [ ] [Machine Learning and Reinforcement Learning in Finance専門講座(有料)](https://www.coursera.org/specializations/machine-learning-reinforcement-finance)
- 80Hr

#### 熱力学、量子力学
- [ ] [Statistical Thermodynamics専門講座(有料)](https://www.coursera.org/specializations/statistical-thermodynamics-engineering)
- 32Hr

#### IT
- [ ] [クラウドコンピューティング専門講座(有料)](https://www.coursera.org/specializations/cloud-computing)
- 128Hr
- [ ] [Getting Started with Google Kubernetes Engine(無料)](https://www.coursera.org/learn/google-kubernetes-engine)
- 11Hr

#### 医学, 生命科学
- [ ] [医学のためのAI専門講座(有料)](https://www.coursera.org/specializations/ai-for-medicine)
- 84Hr
- [ ] [公衆衛生のためのRによる統計分析専門講座(有料)](https://www.coursera.org/specializations/statistical-analysis-r-public-health)
- 48Hr
- [ ] [Big Data, Genes, and Medicine(無料)](https://www.coursera.org/learn/data-genes-medicine)
- 40Hr

#### 専門数学
- [ ] [Introduction to Galois Theory(無料)](https://www.coursera.org/learn/galois)
- 27Hr
- [ ] [ゲーム理論(無料)](https://www.coursera.org/learn/game-theory-1)
- 18Hr

## その他
### Kaggle
- [Kaggle](https://www.kaggle.com/)
- [Kaggleに登録したら次にやること ～ これだけやれば十分闘える！Titanicの先へ行く入門 10 Kernel ～](https://qiita.com/upura/items/3c10ff6fed4e7c3d70f0)
- [Kaggleスタートブック](https://www.amazon.co.jp/dp/B088R992TJ/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1)

### 100本ノックシリーズ
- [Python実践データ分析100本ノック](https://www.amazon.co.jp/dp/B07ZSGSN9S/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1)
- [Python実践機械学習システム100本ノック](https://www.amazon.co.jp/Python%E5%AE%9F%E8%B7%B5%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92%E3%82%B7%E3%82%B9%E3%83%86%E3%83%A0100%E6%9C%AC%E3%83%8E%E3%83%83%E3%82%AF-%E4%B8%8B%E5%B1%B1%E8%BC%9D%E6%98%8C-ebook/dp/B0928FD1P8/ref=pd_sbs_3/356-6138956-3972504?pd_rd_w=pCjFD&pf_rd_p=4e34a507-1281-42ae-953a-93a761caa89c&pf_rd_r=KCG9D534DKP696C1Z717&pd_rd_r=ce39c78f-5356-4e05-8f79-41bd75db5e2b&pd_rd_wg=m3wBe&pd_rd_i=B0928FD1P8&psc=1)
- [Python 実践AIモデル構築 100本ノック](https://www.amazon.co.jp/Python-%E5%AE%9F%E8%B7%B5AI%E3%83%A2%E3%83%87%E3%83%AB%E6%A7%8B%E7%AF%89-100%E6%9C%AC%E3%83%8E%E3%83%83%E3%82%AF-%E4%B8%8B%E5%B1%B1%E8%BC%9D%E6%98%8C-ebook/dp/B09G2K5VJ9/ref=pd_sbs_3/356-6138956-3972504?pd_rd_w=kTWTz&pf_rd_p=4e34a507-1281-42ae-953a-93a761caa89c&pf_rd_r=P74DVE2FS1YZQ95E8G4Q&pd_rd_r=5849053e-d4d9-49ec-8dfb-48fb1bad28b4&pd_rd_wg=4pp7V&pd_rd_i=B09G2K5VJ9&psc=1)