Important skills that you must have

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Importance of mathematics for automatic learning

Expertise in mathematics is necessary to understand and apply algorithms in various applications. From the choice of good algorithm to the selection of the correct parameter, it uses mathematical concepts at each stage of an automatic learning process. The other reasons include the choice of effective training time, complexity and bias in the compromise of variance.

Now discuss the important skills you need to know to master mathematics for automatic learning.

1. Statistics and probability

Statistics and probability form the heart of data analysis. They are widely used in the field of automatic learning to analyze, view, interpret data and discover information. Statistics and probability have found a wide range of applications in several industries. He uses techniques in statistics and probabilities theory when collecting, pre -treatment and manipulation of data.

Here are some of the subjects you need to know:

• Descriptive statistics
• Hypothesis
• Regression
• Probability distributions
• Conditional probability
• Sampling and theorem of the central limit
• Bayes Theorem

Click on the following link to find out more about statistics and the probability of automatic learning: Statistics and probability of automatic learning

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2. Linear algebra

In the field of automatic learning, the concepts of linear algebra appear everywhere. This is another basic skill required to become an expert in automatic learning. To understand how each algorithm works, you need to know linear algebra. The concepts of linear algebra help implement automatic learning algorithms from zero. This includes work with vectors and matrix operations in a dimensional n space.

The following subjects are really important:

• Matritious vectors and properties
• Transposed and reverse matrix
• Determinants
• DOT product
• Own values ​​and clean vectors
• Matrix
• Analysis of the main components
• Orthogonality

Watch the following video to learn the above concepts of linear algebra: Linear algebra for automatic learning

3. Calculation

Knowledge of calculation is very important to understand the crucial automatic learning applications. You may need to revisit the math of the school. Automatic learning uses calculation concepts to formulate the functions used to train algorithms. Automatic learning models are formed with data sets with several functionality variables. Therefore, familiarizing yourself with multivariable calculation is important to build an appropriate model.

Here are key topics that will help you:

• Differential and integral calculation
• Limit, continuity and partial derivatives
• Step function, sigmoid, logit and reread
• Maximum and minimum of a function
• Product and chain rule

Here is a video link that will help you better understand the calculation: Calculation of automatic learning

In addition to mathematical skills above, you should also have good expertise with algorithms and optimization methods. Knowledge of the functioning of gradient descent algorithms and familiarity with the cost function and the likelihood function is also critical. The other areas that must concentrate are the data structures and discreet mathematics.

You will find below some of the subjects you need to know:

• Paintings
• Linked list
• Stack
• Queue
• Sorting algorithms
• Binary search
• Chopper
• Set and its properties
• Graphics

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Conclusion

After reading this article, you would have understood why mathematics play a key role in automatic learning. In addition, the skills you need to know, to master automatic learning algorithms and the creation of profitable models have also been clearly established. You have also learned the different subjects necessary for a better understanding of automatic learning.

To start your career in automatic learning, click on the following link: Post-diploma program in AI and automatic learning. If you have any questions, do not hesitate to publish them in the comments section below. Our team will come back to you as soon as possible.