Essential Mathematics for Mastering Machine Learning and AI 35/100 Days

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Mathematics for Mastering

Importance of Mathematics in Machine Learning and Artificial Intelligence

Mathematics required for machine learning and AI – understand in easy language

If you want to learn algo trading, crypto trading, or stock market algorithms, then you should have a basic understanding of machine learning and artificial intelligence (AI). A good knowledge of mathematics is very important to understand these techniques.

1. Linear Algebra
First of all, machine learning starts with linear algebra. It includes things like vectors and matrices, which help in understanding and processing data. For example, different calculations are done on data by converting it into a matrix.

2. Calculus
Then comes calculus, which is used to improve a model. Concepts like derivative and gradient descent gradually reduce the error of the model.

3. Probability and Statistics
Then comes understanding the information hidden in the data. Probability and statistics are necessary for this. These help us to know which data is more important and which one should be paid attention to.

4. Optimization
When we create a model, we use optimization techniques to improve it. Such as Gradient Descent, which reduces the error of the model.

5. Graph theory and information theory
These are used to understand networks and data relations. Such as the relationship between social media or stocks.

If you are interested in quantitative traders, crypto trading strategies or Freqtrade tutorial, then definitely learn these mathematical topics. These can make your algorithmic trading journey easier.

Watch this Day 35 video tutorial

Day 35: How to Learn Maths in ML/AI?

1. Which Python library is most commonly used for importing data from various sources like CSV, Excel, JSON, and SQL?

2. When importing data from a CSV file using Pandas, which method would you use?

3. To export a DataFrame to an Excel file in Pandas, which method is appropriate?

4. Which library in Python is most effective for web scraping to gather data?

5. When gathering data through an API, which Python library is commonly used to handle HTTP requests?

6. In a data analysis process, what is the first step typically performed after data collection?

7. To read data from a JSON file into a Pandas DataFrame, which method is used?

8. Which Python library provides functions for reading and writing SQL databases?

9. When exporting data to a CSV file in Pandas, which parameter ensures that the index is not written to the file?

10. Which method in Pandas allows you to fetch data from a SQL database and store it in a DataFrame?

11. In the context of web scraping, what is the primary purpose of using the BeautifulSoup library?

12. Which Python library is commonly used alongside BeautifulSoup for making HTTP requests?

13. To clean data in a DataFrame by removing missing values, which Pandas method is used?

14. Which data format is best suited for exporting hierarchical data structures?

15. For large-scale data analysis, which data storage format is most efficient for compression and speed?

16. When gathering financial data through an API, which endpoint parameter is essential for filtering data by date?

17. To merge two DataFrames in Pandas based on a common column, which method is used?

18. In data analysis, what is the primary purpose of data visualization?

19. When exporting data from a DataFrame to a SQL database, which Pandas method is used?

20. Which Python package is commonly used for accessing and exporting data to Microsoft Excel files?






 

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