机器学习 必备技能_机器学习-技能

机器学习 必备技能

机器学习-技能 (Machine Learning - Skills)

Machine Learning has a very large width and requires skills across several domains. The skills that you need to acquire for becoming an expert in Machine Learning are listed below −

机器学习的范围非常广，需要跨多个领域的技能。 下面列出了成为机器学习专家所需的技能-

• Statistics
  统计
• Probability Theories
  概率论
• Calculus
  结石
• Optimization techniques
  优化技术
• Visualization
  可视化

各种机器学习技能的必要性 (Necessity of Various Skills of Machine Learning)

To give you a brief idea of what skills you need to acquire, let us discuss some examples −

为了让您简要了解您需要掌握哪些技能，让我们讨论一些示例-

数学符号 (Mathematical Notation)

Most of the machine learning algorithms are heavily based on mathematics. The level of mathematics that you need to know is probably just a beginner level. What is important is that you should be able to read the notation that mathematicians use in their equations. For example - if you are able to read the notation and comprehend what it means, you are ready for learning machine learning. If not, you may need to brush up your mathematics knowledge.

大多数机器学习算法都很大程度上基于数学。 您需要了解的数学水平可能仅仅是初学者。 重要的是您应该能够阅读数学家在方程式中使用的表示法。 例如，如果您能够阅读该符号并理解其含义，那么您就可以学习机器学习了。 如果没有，您可能需要复习数学知识。

$$f_{AN}(net-\theta)=\begin{cases}\gamma & if\:net-\theta \geq \epsilon\\net-\theta & if - \epsilon< net-\theta

$$ f_ {AN}(net- \ theta)= \ begin {cases} \ gamma＆if \：net- \ theta \ geq \ epsilon \\ net- \ theta＆if-\ epsilon

$$\displaystyle\\\max\limits_{\alpha}\begin{bmatrix}\displaystyle\sum\limits_{i=1}^m \alpha-\frac{1}{2}\displaystyle\sum\limits_{i,j=1}^m label^\left(\begin{array}{c}i\\ \end{array}\right)\cdot\:label^\left(\begin{array}{c}j\\ \end{array}\right)\cdot\:a_{i}\cdot\:a_{j}\langle x^\left(\begin{array}{c}i\\ \end{array}\right),x^\left(\begin{array}{c}j\\ \end{array}\right)\rangle \end{bmatrix}$$

$$ \ displaystyle \\\ max \ limits _ {\ alpha} \ begin {bmatrix} \ displaystyle \ sum \ limits_ {i = 1} ^ m \ alpha- \ frac {1} {2} \ displaystyle \ sum \ limits_ { i，j = 1} ^ m label ^ \ left(\ begin {array} {c} i \\ \ end {array} \ right)\ cdot \：label ^ \ left(\ begin {array} {c} j \\ \ end {array} \ right)\ cdot \：a_ {i} \ cdot \：a_ {j} \ langle x ^ \ left(\ begin {array} {c} i \\ \ end {array} \ right)，x ^ \ left(\ begin {array} {c} j \\ \ end {array} \ right)\ rangle \ end {bmatrix} $$

$$f_{AN}(net-\theta)=\left(\frac{e^{\lambda(net-\theta)}-e^{-\lambda(net-\theta)}}{e^{\lambda(net-\theta)}+e^{-\lambda(net-\theta)}}\right)\;$$

$$ f_ {AN}(net- \ theta)= \ left(\ frac {e ^ {\ lambda(net- \ theta)}-e ^ {-\ lambda(net- \ theta)}} {e ^ { \ lambda(net- \ theta)} + e ^ {-\ lambda(net- \ theta)}} \ right)\; $$

概率论 (Probability Theory)

Here is an example to test your current knowledge of probability theory: Classifying with conditional probabilities.

这是一个示例，用于测试您当前对概率论的了解：使用条件概率进行分类。

$$p(c_{i}|x,y)\;=\frac{p(x,y|c_{i})\;p(c_{i})\;}{p(x,y)\;}$$

$$ p(c_ {i} | x，y)\; = \ frac {p(x，y | c_ {i})\; p(c_ {i})\;} {p(x，y)\ ;} $$

With these definitions, we can define the Bayesian classification rule −

有了这些定义，我们可以定义贝叶斯分类规则-

• If P(c1|x, y) > P(c2|x, y) , the class is c1 .
  如果P(c1 | x，y)> P(c2 | x，y)，则类别为c1。
• If P(c1|x, y) < P(c2|x, y) , the class is c2 .
  如果P(c1 | x，y)

优化问题 (Optimization Problem)

Here is an optimization function

这是一个优化功能

$$\displaystyle\\\max\limits_{\alpha}\begin{bmatrix}\displaystyle\sum\limits_{i=1}^m \alpha-\frac{1}{2}\displaystyle\sum\limits_{i,j=1}^m label^\left(\begin{array}{c}i\\ \end{array}\right)\cdot\:label^\left(\begin{array}{c}j\\ \end{array}\right)\cdot\:a_{i}\cdot\:a_{j}\langle x^\left(\begin{array}{c}i\\ \end{array}\right),x^\left(\begin{array}{c}j\\ \end{array}\right)\rangle \end{bmatrix}$$

$$ \ displaystyle \\\ max \ limits _ {\ alpha} \ begin {bmatrix} \ displaystyle \ sum \ limits_ {i = 1} ^ m \ alpha- \ frac {1} {2} \ displaystyle \ sum \ limits_ { i，j = 1} ^ m label ^ \ left(\ begin {array} {c} i \\ \ end {array} \ right)\ cdot \：label ^ \ left(\ begin {array} {c} j \\ \ end {array} \ right)\ cdot \：a_ {i} \ cdot \：a_ {j} \ langle x ^ \ left(\ begin {array} {c} i \\ \ end {array} \ right)，x ^ \ left(\ begin {array} {c} j \\ \ end {array} \ right)\ rangle \ end {bmatrix} $$

Subject to the following constraints −

受到以下约束-

$$\alpha\geq0,and\:\displaystyle\sum\limits_{i-1}^m \alpha_{i}\cdot\:label^\left(\begin{array}{c}i\\ \end{array}\right)=0$$

$$ \ alpha \ geq0和\：\ displaystyle \ sum \ limits_ {i-1} ^ m \ alpha_ {i} \ cdot \：label ^ \ left(\ begin {array} {c} i \\ \ end {array} \ right)= 0 $$

If you can read and understand the above, you are all set.

如果您能阅读和理解以上内容，那么您已经准备就绪。

可视化 (Visualization)

In many cases, you will need to understand the various types of visualization plots to understand your data distribution and interpret the results of the algorithm’s output.

在许多情况下，您将需要了解各种类型的可视化图，以了解数据分布并解释算法输出的结果。

Besides the above theoretical aspects of machine learning, you need good programming skills to code those algorithms.

除了上述机器学习的理论方面之外，您还需要良好的编程技能才能对这些算法进行编码。

So what does it take to implement ML? Let us look into this in the next chapter.

那么实现ML需要做什么呢？ 让我们在下一章对此进行研究。

翻译自: https://www.tutorialspoint.com/machine_learning/machine_learning_skills.htm

机器学习 必备技能