∂efined.
@regressionrizz
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decoding algorithmic cognition through mathematical abstraction.
Joined August 2025
then you can comfortably make some simple project by blending different concepts together.
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working with numpy arrays and performing basic operations feels straightforward until you dive into linear algebra problems, which can get tricky but are pretty interesting though these kinds of ques can build confidence and once you get the hang of it+
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I don't understand why people don't focus on what they mention in thier bios like why don't you learn that but you're trying to learn linux which is totally opposite of your syllabus? only because you want to look cool? seriously.
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— machine learning progress log: ⤷ numpy broadcasting practice ⤷ some numpy tricks and operations
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3. DQL in brief and it's commands ⤷ filtering with where clause ⤷ logical and comparison operators ⤷ sorting
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2. DML in brief and it's commands ⤷ insert, update, delete ⤷ transaction control basic
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SQL (Structured Query Language): 1. DDL in brief and it's commands - schema design ⤷ normalization ⤷ entity relationship ⤷ data integrity
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I'll also make some projects using python and it's libraries. I just need to complete the remaining topics asap.
• python completed • numpy completed • did basics of matplotlib but will do more again • only few topics left in sql • pandas, plotly, streamlit left • only few topics in linear algebra and also did revision and practice.
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• python completed • numpy completed • did basics of matplotlib but will do more again • only few topics left in sql • pandas, plotly, streamlit left • only few topics in linear algebra and also did revision and practice.
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SQL (Structured Query Language): ⤷ SQL vs NoSQL, standards and dialect ⤷ Database Fundamentals - data types, keys and constraints ⤷ DDL, DML, DQL and aggregate Functions ⤷ currently doing exercises on sqlbolt from scratch.
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— machine learning progress log: ⤷ practicing and performing operations on data ⤷ basics of matplotlib ⤷ linear algebra operations
— machine learning progress log: ⤷ Indexing and slicing in numpy ⤷ sorting, filtering and filtering with mask ⤷ where clause and conditionals ⤷ adding and removing data ⤷ arrays compatibility
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— machine learning progress log: ⤷ Indexing and slicing in numpy ⤷ sorting, filtering and filtering with mask ⤷ where clause and conditionals ⤷ adding and removing data ⤷ arrays compatibility
— machine learning progress log: ⤷ numpy basics and array types ⤷ difference bw list and arrays ⤷ creating arrays from list ⤷ creating arrays from scratch ⤷ arrays reshaping
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— machine learning progress log: ⤷ numpy basics and array types ⤷ difference bw list and arrays ⤷ creating arrays from list ⤷ creating arrays from scratch ⤷ arrays reshaping
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out of context but made some dashboards and also learnt about sql server, sql server management studio and how to use APIs in python, file handling.
Had some work so I was not posting but I'll start from tomorrow again and will do numpy, pandas and also only few topics left in linear algebra so I will cover them.
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Had some work so I was not posting but I'll start from tomorrow again and will do numpy, pandas and also only few topics left in linear algebra so I will cover them.
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— machine learning progress log: ⤷ completed chapter 1 and half chapter 2 theory of strang's book and did some examples and will do exercise questions tom. ⤷ revision of linear algebra from matrices to vector spaces ⤷ python revision and started a short course.
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currently doing problems on nxn system and next is matrix multiplication.
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— mathematics for machine learning: • Linear Algebra ⤷ mml book 47/417 ⤷ did some examples and currently doing exercise questions.
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