├── .github
    └── FUNDING.yml
├── Google机器学习速成课程.md
├── Google机器学习速成课程
    ├── 18世纪文学例子.png
    ├── 18世纪文学例子1.png
    ├── 18世纪文学例子2.png
    ├── 18世纪文学例子3.png
    ├── EmbeddingExample3-1.svg
    ├── FloatingPointFeatures.svg
    ├── GradientDescentDiagram.svg
    ├── HighLambda.svg
    ├── InputRepresentationWithValues.png
    ├── LearningRateJustRight.svg
    ├── LearningRateTooLarge.svg
    ├── LearningRateTooSmall.svg
    ├── LossSideBySide.png
    ├── LowLambda.svg
    ├── MlSystem.svg
    ├── OneHotEncoding.svg
    ├── OneVsAll.svg
    ├── RawDataToFeatureVector.svg
    ├── RegularizationTwoLossFunctions.svg
    ├── ScalingBinningPart1.svg
    ├── ScalingBinningPart2.svg
    ├── ScalingClipping.svg
    ├── ScalingLogNormalization.svg
    ├── ScalingNoticingOutliers.svg
    ├── SoftmaxLayer.svg
    ├── Thumbs.db
    ├── WorkflowWithValidationSet.svg
    ├── dnn-to-geometric-view.svg
    ├── linear-relationships.svg
    ├── 习题_AUC 和预测结果的尺度.png
    ├── 习题_AUC和预测结果的尺度.png
    ├── 习题_L2正则化.png
    ├── 习题_准确率.png
    ├── 习题_反馈环.png
    ├── 习题_召回率.png
    ├── 习题_在线推理.png
    ├── 习题_在线训练.png
    ├── 习题_均方误差.png
    ├── 习题_批量大小.png
    ├── 习题_特征和标签.png
    ├── 习题_特征组合.png
    ├── 习题_监督式学习.png
    ├── 习题_离线推理.png
    ├── 习题_离线训练.png
    ├── 习题_精确率.png
    ├── 习题_精确率和召回率.png
    ├── 习题_验证集.png
    ├── 分类_ROC曲线.png
    ├── 分类_ROC曲线下面积.png
    ├── 分类_准确率.png
    ├── 分类_召回率.png
    ├── 分类_狼来了混淆矩阵.png
    ├── 分类_精确率.png
    ├── 分类_精确率和召回率的关系1.png
    ├── 分类_精确率和召回率的关系2.png
    ├── 分类_精确率和召回率的关系3.png
    ├── 分类_预测偏差.png
    ├── 分类_预测偏差曲线.png
    ├── 多类别分类神经网络_Softmax.png
    ├── 对非线性规律进行编码.png
    ├── 嵌入_协同过滤的目的_1.png
    ├── 嵌入_协同过滤的目的_2.png
    ├── 梯度下降法_1.png
    ├── 梯度下降法_偏导数.png
    ├── 梯度下降法_梯度.png
    ├── 梯度下降法_梯度举例.png
    ├── 正则化_L2正则化.png
    ├── 正则化_L2正则化和相关特征.png
    ├── 癌症预测例子.png
    ├── 目录.png
    ├── 神经网络剖析_1.png
    ├── 神经网络剖析_2.png
    ├── 神经网络剖析_3.png
    ├── 神经网络剖析_4.png
    ├── 神经网络剖析_5.png
    ├── 线性回归_2.png
    ├── 线性回归_3.png
    ├── 训练与损失_1.png
    ├── 逻辑回归_S形函数.png
    ├── 逻辑回归_对数几率.png
    └── 逻辑回归_推断计算例子.png
├── Google机器学习速成课程Code
    ├── feature_crosses.ipynb
    ├── feature_sets.ipynb
    ├── first_steps_with_tensor_flow.ipynb
    ├── improving_neural_net_performance.ipynb
    ├── intro_to_neural_nets.ipynb
    ├── intro_to_pandas.ipynb
    ├── intro_to_sparse_data_and_embeddings.ipynb
    ├── logistic_regression.ipynb
    ├── multi_class_classification_of_handwritten_digits.ipynb
    ├── synthetic_features_and_outliers.ipynb
    └── validation.ipynb
├── PDF
    ├── Google机器学习速成课程.pdf
    ├── 机器学习中的常识性问题_望江人工智库.pdf
    ├── 机器学习术语表GoogleDevelopers.pdf
    ├── 机器学习知识点彩图版.pdf
    └── 机器学习规则GoogleDevelopers.pdf
├── README.md
└── 图片
    ├── Thumbs.db
    ├── 二分类评价指标表格.png
    ├── 机器学习彩图版-偏差和方差的权衡.png
    └── 混淆矩阵和12率公式.png


/.github/FUNDING.yml:
--------------------------------------------------------------------------------
 1 | # These are supported funding model platforms
 2 | 
 3 | github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
 4 | patreon: # Replace with a single Patreon username
 5 | open_collective: # Replace with a single Open Collective username
 6 | ko_fi: # Replace with a single Ko-fi username
 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 9 | liberapay: # Replace with a single Liberapay username
10 | issuehunt: # Replace with a single IssueHunt username
11 | otechie: # Replace with a single Otechie username
12 | custom: ['https://yuanxiaosc.github.io/images/wechatpay.jpg', 'https://yuanxiaosc.github.io/images/alipay.jpg']
13 | 


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28 |      x="864.19751"
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31 |        id="tspan2444">计算参数更新</tspan></text>
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35 |      x="1193.7686"
36 |      y="384.57098"
37 |      id="text1114"><tspan
38 |        id="tspan2454">计算损失</tspan></text>
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50 |      y="307.62653"
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53 |        x="129.68826"
54 |        id="tspan2446">特征</tspan></text>
55 |   <text
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58 |      x="130.87036"
59 |      y="451.44135"
60 |      id="text1132"><tspan
61 |        id="tspan2448">标签</tspan></text>
62 |   <text
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64 |      style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:21.33333397px;line-height:1.25;font-family:sans-serif;-inkscape-font-specification:'sans-serif, Normal';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-feature-settings:normal;text-align:start;letter-spacing:0px;word-spacing:0px;writing-mode:lr-tb;text-anchor:start;fill:#000000;fill-opacity:1;stroke:none"
65 |      x="885.43213"
66 |      y="264.22839"
67 |      id="text1136"><tspan
68 |        id="tspan2452">推理：执行预测</tspan></text>
69 | </svg>
70 | 


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280 |        id="tspan266">我们将高效地</tspan></text>
281 | <text
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284 |      style="font-variant:normal;font-weight:normal;font-size:18.66662788px;font-family:Arial;-inkscape-font-specification:ArialMT;writing-mode:lr-tb;fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none;stroke-width:0.99999797"
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287 |        x="353.65625"
288 |        y="118.53917"
289 |        id="tspan270">到达最低点。</tspan></text>
290 | </svg>


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/Google机器学习速成课程/LossSideBySide.png:
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https://raw.githubusercontent.com/yuanxiaosc/Machine-Learning-Book/118f8e6d4b83958f41c9c86c23014f2655a01ac2/Google机器学习速成课程/LossSideBySide.png


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/Google机器学习速成课程/LowLambda.svg:
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11 | <desc>模型权重的平均值为 0 且权重分布方式介于均匀分布和正态分布之间的直方图。</desc>
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247 |        xml:space="preserve"><tspan
248 |          y="211.48473"
249 |          x="203.78751"
250 |          id="tspan1139"
251 |          sodipodi:role="line">特征提取</tspan></text>
252 |   </g>
253 |   <path
254 |      id="path111"
255 |      d="m 336.70059,180.0148 h 91.41166 v 30.29922 h -91.41166 z"
256 |      inkscape:connector-curvature="0"
257 |      style="fill-rule:evenodd;stroke:#000000;stroke-width:2;stroke-linecap:butt;stroke-linejoin:round" />
258 |   <text
259 |      xml:space="preserve"
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264 |        sodipodi:role="line"
265 |        id="tspan1141"
266 |        x="347.56226"
267 |        y="199.49641">机器学习代码</tspan></text>
268 | </svg>
269 | 


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/Google机器学习速成课程/OneHotEncoding.svg:
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135 |      style="line-height:0%"
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140 |        class="ohe-st4 ohe-st5"
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142 |        style="font-size:21px;font-family:RobotoMono-Regular">street_name</tspan><tspan
143 |        style="font-family:sans-serif; font-size:21px;"
144 |        id="tspan32"> 特征 = </tspan><tspan
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147 |        class="ohe-st4 ohe-st5"
148 |        id="tspan59"
149 |        style="font-size:21px;font-family:RobotoMono-Regular">  [0, 0, ..., 0, <tspan
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151 |    id="tspan887">1, 0, ...,</tspan> 0]</tspan></text>
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173 |      style="font-size:17.29874039px;line-height:0%;font-family:Roboto-Regular;stroke-width:0.96104109"
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175 |      class="ohe-st6 ohe-st7"
176 |      transform="scale(0.96104112,1.0405382)"
177 |      x="966.05957"
178 |      y="349.67886">V: 唯一街道名称的数量</text>
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186 |      transform="scale(0.96104112,1.0405382)"
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191 |        x="695.3717"
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193 |        class="ohe-st6 ohe-st7"
194 |        id="tspan83"
195 |        style="font-weight:bold;font-size:17.29874039px;font-family:Roboto-Regular">独热编码</tspan></text>
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199 |        style="fill:#c4eaf9;fill-opacity:1;stroke:#000000;stroke-width:1;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1"
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205 |        ry="15.828061" /><text
206 |        xml:space="preserve"
207 |        style="font-style:normal;font-weight:normal;font-size:40px;line-height:0%;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
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209 |        y="50.752747"
210 |        id="text856"><tspan
211 |          id="tspan854"
212 |          x="73.268265"
213 |          y="50.752747">原始数据</tspan></text>
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222 |      xml:space="preserve"
223 |      style="font-style:normal;font-weight:normal;font-size:40px;line-height:0%;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
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228 |        x="935.68372"
229 |        y="187.04904">特征</tspan></text>
230 | <text
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232 |      id="text39"
233 |      x="494.4249"
234 |      y="254.83545"
235 |      style="font-size:18.66666603px;line-height:0%">可以使用独热编码处理字符串特征</text>
236 | <text
237 |      style="font-size:11.53249359px;line-height:0%"
238 |      y="487.24805"
239 |      x="695.3717"
240 |      id="text876"
241 |      transform="scale(0.96104112,1.0405382)"><tspan
242 |        style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:17.29874039px;font-family:Roboto-Regular;-inkscape-font-specification:Roboto-Regular"
243 |        id="tspan874"
244 |        class="ohe-st6 ohe-st7"
245 |        y="487.24805"
246 |        x="695.3717">其中 1 表示“<tspan style="font-family: monospace">Shorebird Way</tspan>”，</tspan></text>
247 | <text
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249 |      id="text880"
250 |      x="695.3717"
251 |      y="506.46893"
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253 |        x="695.3717"
254 |        y="506.46893"
255 |        class="ohe-st6 ohe-st7"
256 |        id="tspan878"
257 |        style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:17.29874039px;font-family:Roboto-Regular;-inkscape-font-specification:Roboto-Regular">0 表示所有其他街道。</tspan></text>
258 | </svg>
259 | 


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/Google机器学习速成课程/RawDataToFeatureVector.svg:
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184 |        style="font-size:18.66666603px">量的形式呈现</tspan></text>
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187 |      y="185.27202"
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201 |        style="font-size:18.66666603px"
202 |        y="213.66422"
203 |        x="721.24634"
204 |        id="tspan861">从原始数据创建特征的过程</tspan></text>
205 | <text
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234 | <text transform="matrix(1 0 0 1 417.9736 129.8428)" class="zhsbpii-st3 zhsbpii-st8">LatitudeBin7 = 37 &lt; 纬度 &lt;= 38</text>
235 | </svg>
236 | 


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209 |        d="m532.78436 347.131l309.55585 0l0 54.015747l-309.55585 0z"
210 |        fill-opacity="0.0"
211 |        fill="#000000" />
212 |     <path
213 |        style="fill:#f4b400;fill-rule:nonzero;stroke-width:0.88329595"
214 |        id="path75"
215 |        d="m 675.12458,356.41863 h 92.96711 v 38.4 h -92.96711 z" />
216 |     <path
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219 |        d="m439.76642 397.42783l89.22833 0"
220 |        fill-opacity="0.0"
221 |        fill="#000000" />
222 |     <path
223 |        id="path81"
224 |        fill-rule="evenodd"
225 |        d="m439.76642 397.42783l78.94708 0"
226 |        stroke-linecap="butt"
227 |        stroke-linejoin="round"
228 |        stroke-width="3.0"
229 |        stroke="#424242" />
230 |     <path
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232 |        fill-rule="evenodd"
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238 |     <text
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240 |        y="82.315033"
241 |        x="103.70899"
242 |        style="font-style:normal;font-weight:normal;font-size:26.66666603px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none"
243 |        xml:space="preserve"><tspan
244 |          style="font-size:29.33333397px"
245 |          y="82.315033"
246 |          x="103.70899"
247 |          id="tspan3784">使用训练集训练模型</tspan></text>
248 |     <text
249 |        id="text3790"
250 |        y="82.329353"
251 |        x="554.16223"
252 |        style="font-style:normal;font-weight:normal;font-size:26.66666603px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none"
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254 |          style="font-size:29.33333397px"
255 |          y="82.329353"
256 |          x="554.16223"
257 |          id="tspan3788">使用验证集评估模型</tspan></text>
258 |     <text
259 |        id="text3794"
260 |        y="210.01683"
261 |        x="456.70139"
262 |        style="font-style:normal;font-weight:normal;font-size:26.66666603px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none"
263 |        xml:space="preserve"><tspan
264 |          style="font-size:32px;text-align:center;text-anchor:middle"
265 |          id="tspan3796"
266 |          y="210.01683"
267 |          x="456.70139">根据在验证集上获</tspan><tspan
268 |          id="tspan58"
269 |          style="font-size:32px;text-align:center;text-anchor:middle"
270 |          y="246.6835"
271 |          x="456.70139">得的效果调整模型</tspan></text>
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273 |        id="text3800"
274 |        y="229.92058"
275 |        x="426.35919"
276 |        style="font-style:normal;font-weight:normal;font-size:26.66666603px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none"
277 |        xml:space="preserve"><tspan
278 |          style="font-weight:bold;font-size:29.33333397px"
279 |          y="253.51433"
280 |          x="426.35919"
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282 |     <text
283 |        id="text3804"
284 |        y="387.05545"
285 |        x="231.43651"
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287 |        xml:space="preserve"><tspan
288 |          style="font-size:29.33333397px;text-align:center;text-anchor:middle"
289 |          id="tspan3806"
290 |          y="387.05545"
291 |          x="231.43651">选择在验证集上获</tspan><tspan
292 |          id="tspan62"
293 |          style="font-size:29.33333397px;text-align:center;text-anchor:middle"
294 |          y="423.72211"
295 |          x="231.43651">得最佳效果的模型</tspan></text>
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298 |        y="387.05545"
299 |        x="692.16028"
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301 |        xml:space="preserve"><tspan
302 |          style="font-weight:bold;font-size:29.33333397px;text-align:center;text-anchor:middle"
303 |          id="tspan3816"
304 |          y="387.05545"
305 |          x="692.16028">使用测试集</tspan><tspan
306 |          id="tspan68"
307 |          style="font-weight:bold;font-size:29.33333397px;text-align:center;text-anchor:middle"
308 |          y="423.72211"
309 |          x="692.16028">确认模型的效果</tspan></text>
310 |   </g>
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/README.md:
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 1 | # Machine-Learning-Book
 2 | [Machine-Learning-Book（机器学习宝典）](https://github.com/yuanxiaosc/Machine-Learning-Book)涵盖了从机器学习从入门到精通所需的所有必备知识。
 3 | 
 4 | 1. 其中《[机器学习知识点彩图版.pdf](https://raw.githubusercontent.com/yuanxiaosc/Machine-Learning-Book/master/PDF/机器学习知识点彩图版.pdf)》以生动形象的图片描述机器学习中的知识点。
 5 | 2. 其中《[Google机器学习速成课程.pdf](https://raw.githubusercontent.com/yuanxiaosc/Machine-Learning-Book/master/PDF/Google机器学习速成课程.pdf)》以加利福尼亚房价预测为线索，讲解了机器学习概念、特征工程以及机器学习在现实世界的应用。该课程有对应知识点的习题和解答，你可以随时检测自己的学习效果。
 6 | 3. 其中《[机器学习中的常识性问题 (最新网页版)](https://yuanxiaosc.github.io/2019/08/16/机器学习中的常识性问题/)》 ， 该文系统性总结了机器学习基础知识。比如你了解机器学习中常见的二分类问题评价指标：混淆矩阵、准确率、精确率、召回率、敏感性、特异性、AUC、ROC以及它们之间的关系吗？（答案见文末）
 7 | 
 8 | ![](图片/机器学习彩图版-偏差和方差的权衡.png)
 9 | <center>机器学习彩图版-偏差和方差的权衡</center>
10 | 
11 | ## 机器学习宝典内容汇总
12 | 百度网盘打包下载本资源：
13 | + 链接：https://pan.baidu.com/s/1OLscfquhYKOuN7X-QVqQNA
14 | + 提取码：6g4l
15 | 
16 | |标签|名称|说明|
17 | |-|-|-|
18 | |养兴趣|[机器学习知识点彩图版.pdf](https://raw.githubusercontent.com/yuanxiaosc/Machine-Learning-Book/master/PDF/机器学习知识点彩图版.pdf)|以生动形象的图片描述机器学习中的知识点。|
19 | |练招式|完整版[Google机器学习速成课程.md](Google机器学习速成课程.md) or [Google机器学习速成课程.pdf](PDF/Google机器学习速成课程.pdf) and [谷歌机器学习速成课程-配套TensorFlow代码](Google机器学习速成课程Code)|本文讲解了机器学习概念、特征工程以及机器学习在现实世界的应用。解决了：加利福利亚房价预测问题（回归问题）+分类问题+手写字体识别问题|
20 | |口诀|[机器学习术语表（PDF）](https://raw.githubusercontent.com/yuanxiaosc/Machine-Learning-Book/master/PDF/机器学习术语表GoogleDevelopers.pdf) or [机器学习术语表（网页版）]( https://developers.google.com/machine-learning/glossary/ ) |本术语表中列出了一般的机器学习术语和 TensorFlow 专用术语的定义。|
21 | |心得|[机器学习规则（PDF）](https://raw.githubusercontent.com/yuanxiaosc/Machine-Learning-Book/master/PDF/机器学习规则GoogleDevelopers.pdf) or [机器学习规则（网页版）]( https://developers.google.com/machine-learning/guides/rules-of-ml/ ) |本文档旨在帮助已掌握机器学习基础知识的人员从 Google 机器学习的最佳实践（经验）中受益。|
22 | |练内功|[机器学习中的常识性问题（PDF）](PDF/机器学习中的常识性问题_望江人工智库.pdf) or [机器学习中的常识性问题 (最新网页版)](https://yuanxiaosc.github.io/2019/08/16/机器学习中的常识性问题/)| 系统性深入学习机器学习。机器学习中的常识性问题定义：作为一名合格的机器学习从业人员必须理解的机器学习领域的常识性问题。|
23 | 
24 | ## 开始学习 练招式
25 | 
26 | 点击开始学习完整版 [Google机器学习速成课程.md](Google机器学习速成课程.md)，也可以下载完整版[Google机器学习速成课程.pdf](https://raw.githubusercontent.com/yuanxiaosc/Machine-Learning-Book/master/PDF/Google机器学习速成课程.pdf)。
27 | 
28 | 
29 | ## 学习进阶 练内功
30 | 机器学习中的常识性问题定义：作为一名合格的机器学习从业人员必须理解的机器学习领域的常识性问题。
31 | 
32 | 点击开始学习 [机器学习中的常识性问题](https://yuanxiaosc.github.io/2019/08/16/机器学习中的常识性问题/)
33 | 
34 | 机器学习中常见的二分类问题评价指标：混淆矩阵、准确率、精确率、召回率、敏感性、特异性、AUC、ROC以及它们之间的关系吗？
35 | 
36 | 答案：
37 | 
38 | ![](图片/混淆矩阵和12率公式.png)
39 | ![](图片/二分类评价指标表格.png)
40 | 


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