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

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Mathematics for Mastering

Mathematics is the foundation of Machine Learning (ML) and Artificial Intelligence (AI). Understanding key mathematical concepts helps in:

  • Building accurate ML models
  • Optimizing algorithms
  • Improving decision-making in AI applications
  • This guide covers essential math topics for ML and AI, along with useful resources.

Why is Math Important for ML & AI?

Math enables machines to learn from data, optimize models, and make predictions.

Alert this video could be the most impactful video you have ever seen in your machine Learning Journey in your artificial intelligence journey and your quantitative Finance Journey this video might change the way you are thinking about the machine learning until now because in this video I’m going to reveal the big why why you should learn the maths before you start the machine Learning Journey the basic maths very basic maths and I’m sure that I’ll clear all your wise that why you should learn it before you jump to the machine learning and Ai and Quant paths because many of us amazing people leave the journey Midway they just prefer to take a U-turn which is not good because the level of maths required is not that much deeper not that much complex if you’re not going into the machine learning research field otherwise any of us can handle the basic maths you just need a positive mindset a learning mindset that’s it and I’ll show you today that why we going to learn the statistics part of the maths first and this will cover most of the part and remaining part like the the probability and the linear algebra and the calculus we will learn along the way so before starting the video 

Let me share a personal experience of me which happened with me today only before starting this video what happened if you follow the cricket then you know that India has won the T20 World Cup after 17 years and it was really a big win for the India I didn’t watch the full match but I just watched the Roy sharma’s winning interview was my last game as well um honestly I have enjoyed since the time I started playing this format and the retirement announcement of the Virat kohi um this was going to be my last T20 World Cup playing for India it’s time for the Next Generation to take over vat kohi one of the greatest player of the Cricket World World announced the retirement today so while watching the retirement videos I stumbled upon a video of him talking about the most impactful time of his life which changed his life completely so I’ll just share that video link also in the description so please watch that video and let me tell you what happened with me while watching the video I watched the video four to five times and each time I watch the video I broke down emotionally because.

I can relate with that that what was the biggest why which led Virat kohle to become the greatest player of the world in the Cricket World so each one of us has to find out the why why we want to do this anything in your life if you want to go please first find the why if you are able to find the why then the how becomes very easy so please watch the video and let me know if you also felt the same emotion and share with all of us your experience your emotions in the comments if you want you can also DM me we can discuss and we can find out your why together because I felt chills in my body I felt so much of positive energy in myself while watching the video so that was a small incident please watch the video and let me know your experience in the comments okay so now let’s get back to the video and let me tell you the big wise of statistics and machine learning or data science okay now let’s get started with the 10 big wise and where we use the statistics part of the mathematics in data science machine learning and Quant Trading First Data understanding so statistics provides the tools to understand and interpret data distributions and relationships for example.

Analyzing stock price volatility so Traders use the standard deviation to find the price volatility of any stock which helps them to decide when to enter and when to exit the trades it can be also useful in detecting the seasonality in the commodity prices statistic tools like autocorrelation and forar transforms help identify the seasonal patterns in the Commodities like the oil or the natural gases we can also use the statistics to evaluate trading volume anomalies we can identify their days with unusually high or low trading volume the second big why and where we can use the statistics in Quant trading which is the model validation statistical test help validate the assumptions underlying trading models for example we can use the T tests or Anova to confirm if changes in an algorithm’s parameters lead to statistically significant Improvement in the trading performance like we can use the historical data to test a trading strategy would have been profitable under past conditions we can also apply the statistical test to compare the performance of two algorithms on the same data set to determine which is more effective we can also use the error.

Essential Mathematics for Mastering

New data applying methods like L1 and L2 regularization to prevent machine learning models from overfitting it can can be also used to reduce the model complexity by removing unnecessary predictors to improve the model robustness the ninth big why hypothesis testing hypothesis testing in statistics is crucial for making informed decisions based on data for example we can conduct market efficiency testing to identify the exploitable anomalies it can be also used to compare the strategies to identify the effic of different trading algorithms and it has its own significance in economic events like to analyze the impact of macroeconomic events on stock prices through hypothesis testing the last but not the least 10th Big Y Predictive Analytics for example.

We can use time series forecasting models like ARA and other statistical techniques to forecast future market trends based on historic IAL data it is widely used in sentiment analysis it analyze the sentiment data from news articles or social media platforms to predict the future Market movements and it is also used in behavioral pattern recognition for example we can use cluster analysis to identify the behavior pattern in the trader and that might indicate the upcoming Market movements and with that being said we have covered almost everything in Quant trading where we will be using the statistics and in future we will cover almost all the topics which we have mentioned in this session and also please if you have any doubt let me know in the comments we can discuss it in the future until then bye-bye take care have a nice day we’ll see you in the next video

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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