Day 62 of #100DayswithMachineLearning

Topic - Ridge Regression in ML ( Part 1 )

🧵 Ridge Regression (RR) is regularization technique used in statistical modeling & ML to handle the problem of multicollinearity (high correlation) among predictor variables

Are you completely new to SQL and do not know where to start?

Here is a simple concept roadmap for learning SQL as a complete beginner:

🧵 1. 𝐋𝐞𝐚𝐫𝐧 𝐭𝐡𝐞 𝐛𝐚𝐬𝐢𝐜𝐬:

- Primary Key vs Foreign Key
- Data Types
- Database diagrams
- Tables
- Records and Fields
- Naming standards for tables and fields

Day61 of #100DayswithMachineLearning

Topic - Bias Variance Trade-off in ML

🧵 🔹 If ML model is not accurate. it can make predictions error & these prediction errors are usually known as Bias & Variance

🔹 In ML these errors will alway be present as there is always slight difference between model predictions & actual predictions

Day 60 of #100DayswithMachineLearning

Topic - Polynomial Regression in ML

🧵 Polynomial regression is type of regression analysis where relationship between independent variable(s) and dependent variable is modeled as an nth-degree polynomial function.

It is an extension of simple linear regression which assumes linear relationship between the variable

🔸CONCAT_WS() in SQL { Very Helpful }

A Thread 🧵 CONCAT_WS() function in SQL is used to concatenate multiple strings into single string with specified separator between each string

"WS" stands for "with separator." This function is commonly used to construct strings contain multiple values such create comma-separated list

Day 59 of #100DayswithMachinelearning

Topic - Mini-Batch Gradient Descent

A Thread 🧵 Mini-batch gradient descent is a variation of the gradient descent optimization algorithm used in ML & DL

It is designed to address the limitations of two other variants: BGD and SGD

Data Analyst Project on Tableau

🧵 1) github.com/sachinkumar160…

Day 58 of #100DayswithMachineLearning

Topic - Stochastic Gradient Descent ( SGD )

A Thread 🧵 SGD is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It’s an inexact but powerful technique.

Day 57 of #100dayswithMachinelearning

Topic - Batch Gradient Descent (BGD)

A Thread 🧵 (BGD) is optimization algorithm commonly used in ML & optimization problems to minimize the cost function or maximize the objective function

It is type of GD algorithm that update model parameters by taking the average gradient of entire training dataset at each iteration

Day 47 of #100dayswithmachinelearning

Topic -- Principle Component Analysis
(PCA) Part 1 PCA statistics is science of analyzing all the dimension & reducing them as much as possible while preserving exact information

You can monitor multi-dimensional data (can visualize in 2D or 3D dimension) over any platform using the Principal Component Method of factor analysis.

Hello Folks 👨‍💻

If you are someone who is learning SQL, then this list can be helpful to you.

SQL - END-TO-END Learning Resources and Guide 👇 ( Must Read ) 1. SQL for Data Science

🔗lnkd.in/dw4aAC-q

2. Databases and SQL for Data Science with Python

🔗lnkd.in/d2psKJd9

Day 46 of #100dayswithmachinelearning

Topic -- Curse of Dimensionality

🧵 Refers to phenomenon where the performance of ML algorithms deteriorates as No. of dimension or feature of input data ⬆️

This is because the volume of space increases exponentially with No. of dimension which causes data to become sparse & distance btwn data point to increase

Day 45 of #100dayswithmachinelearning

Topic - Feature Construction & Feature Splitting

A Thread 🧵 Feature construction is a critical aspect of feature engineering, which involves the process of creating new features or transforming existing ones to improve the performance of machine learning models.

Netflix Data Analysis Project

🧵 @ProjectJupyter NoteBook Link --

github.com/sachinkumar160…

🏹SQL Interview Questions

🧵 🎯Are NULL values same as that of zero or a blank space❓

🔺A NULL value is not at all same as that of zero or a blank space.
🔺NULL value represents a value which is unavailable, unknown, assigned or not applicable whereas a zero is a number and blank space is a character.

Day 44 of #100dayswithmachinelearning

Topic -- Outlier Detection using Percentile Method

A Thread 🧵 Outliers are a very important and crucial aspect of Data Analysis.

It can be treated in different ways, such as trimming, capping, discretization, or by treating them as missing values.

Day 43 of #100dayswithmachinelearning

Topic - Outlier Detection and Removal using the IQR Method

A Thread 🧵 The IQR (Interquartile Range) method is a common approach for detecting and removing outliers from a dataset

IQR is the difference between 75th and 25th Quartile

we can remove the bad data from left or right skewed distribution as well for that statistics have introduced IQR

Day 42 of #100dayswithMachinelearning

Topic -- Outlier Detection & Removal using Z-score Method

A Thread 🧵 The Z-score method is statistical approach used for detecting & removing outlier in dataset. An outlier is observation that lies far away from other observation in dataset. Such observations can significantly affect statistical properties of dataset & lead to erroneous conclusion

" PowerBI Project For Data Analyst "

A Thread 🧵 1⃣ HR Analytics Dashboard

linkedin.com/posts/sachintu…

Day 33 of #100dayswithmachinelearning

Topic - Handling Mixed Variable in Feature Engineering 👨‍💻

A Thread 🧵 Handling missing Variable is very important as many machine learning algorithms do not support data with missing values. If you have missing values in the dataset, it can cause errors and poor performance with some machine learning algorithms.

30 Most Important SQL Interview Question { Must Read }

A Thread 🧵 ▶️( Q1-Q5 )

https://twitter.com/Sachintukumar/status/1643975772917633026?s=20