📝Day 35 of #Deeplearning

✅Topic - Backpropagation in CNN

▫️ #BackPropagation is an algorithm to train neural networks. It is the method of fine-tuning weights of a neural network based on error rate obtained in previous epoch (i.e., iteration)

A Complete 🧵 ✅Backpropagation is an algorithm for supervised learning of artificial neural networks using #gradientdescent

Given an artificial neural network and an error function, method calculates gradient of error function with respect to neural network's weights using chain rule

✅Day 32 of #Deeplearning

📝Topic - Padding & Strides

▫️ #Padding is simply a process of adding layers of zeros to our input images

▫️ #Stride describes step size of kernel when you slide a filter over an input image

A Complete 🧵 ▫️ Padding is simply a process of adding layers of zeros to our input images.

The purpose of padding is to preserve original size of an image when applying a #convolutional filter & enable filter to perform full convolutions on edge pixel

✅Day 29 of #Deeplearning

▫️ Topic - Keras Tuner & Hyperparameter Tuning

📝#KerasTuner is a powerful library that allows you to automate hyperparameter tuning process & search for best model configuration

A Complete 🧵 A ML model has two types of parameters:

Trainable parameters - learned by algorithm during training. For instance weights of a neural network are trainable parameters

#Hyperparameters - set before launching learning process. learning rate in a dense layer are hyperparameter

✅Day6⃣ of #Deeplearning🧬

▫️ Topic - Multilayer Perceptrons

📝#Multilayerperceptron is type of feedforward neural network consisting of fully connected neurons with nonlinear kind of activation function

It is widely used to distinguish data that is not linearly separable ✅Some of its key concepts

Input layer
Hidden layer
Output layer
Weights
Bias Neurons

✅Day1⃣ of #DeepLearning

▫️Topic - Machine Learning (ML) vs Deep Learning (DL)

📝Deep learning is a sub-category of #Machinelearning focused on structuring a learning process for computers where they can recognize patterns & make decisions, much like humans do

A Complete 🧵 DL is essentially a type of sophisticated, multi-layered filter

input raw, unorganized data at top, & it traverses through various layers of neural network, getting refined & analyzed at each level. Eventually, what emerges at bottom is a coherent, structured piece of info a

✅Day 95 of #MachineLearning

▫️ Topic - XGBoost Algorithm in Machine Learning🎰

📝#XGBoost efficient handling of missing values is one of its core advantages, allowing it to handle real-world #data with missing values without considerable pre-processing

A Complete Thread🧵 - Optimization & Improvement

process by which ML #algorithm is tuned to improve its performance.This includes adjusting parameters such as learning rate, tree depth, & regularization strength to achieve best model for a given data set

✅SQL Projects for Data Professional 🎰

Complete Thread 🧵 1)

github.com/sachinkumar160…

✅Day 94 of #MachineLearning

▫️ Topic - Naive Bayes algorithm in ML🔧

📝#NaïveBayes classification is a straightforward & powerful algorithm for classification task🧝

A Complete Thread🧵 ✅Naïve Bayes Classifier uses Bayes’ theorem to predict membership probabilities for each class such as probability that given record or data point belong to particular class

class with highest probability is considered as most likely class, known as Maximum A Posteriori (MAP)

✅Day-13 of #Python Series🐍

Topic - #Sets Method in Python {Detail Thread}

A Thread🧵 ▫️ Sets are unordered collections of unique elements, meaning that each element can only appear once in set

▫️ Curly braces {} are used to denote set in Python

✅Day-12 of #Python Series🐍

📍Topic - Lambda Functions in Python

▫️ A #lambda function is small function containing a single expression written in single line

▫️ It can also act as anonymous functions where they don’t require function name or identifier

A Thread 🧵 ✅Lambda functions consist of 3 parts:

Lambda Keyword
Bound variable/lambda argument
Body or lambda expression

▫️Normal function are created using def keyword. They can have any no of argument any no of expressions, & lines of code.
generally used for large blocks of code

✅Day-11 of #Python Series🐍

📍Topic - Difference Between map, applymap & apply in #pandas

A Thread 🧵 1) apply() -

The pandas #apply() function operates on both dataframes & series.

We can use it on either columns of dataframes (axis=1) or on rows of dataframes (axis=0)

Day7⃣ of Python Series 🐍

✅Topic - Regex Functions in #Python

Regular expressions commonly referred to as #Regex are dynamic tools used for manipulation & pattern matching of textual Data

🧵👇 Regular expressions (RE) in Python are represented as strings & combine normal characters & special symbols called metacharacters

These #metacharacters have special meanings & are used to define patterns to be matched

Day4⃣ of Python Series 🐍

Topic -- Modules vs Packages in #Python📦

✅#Module is a Single file containing Python code, while a #Package is a directory that contains multiple modules along with a special __init__.py file.

🧵👇 1⃣Module -

is single file containing Python code that can be imported & used in other Python code

It can define variables, functions, classes, & other Python constructs that can be used by other code

#Modules are great way to organize code & make it reusable across programs

Day1⃣ of #Statistics Series⚡️

Today Topic - Confounding Variable

✅#Confoundingvariable in Statistics is a variable that is related to both independent variable ( variable you're studying) & dependent variable ( outcome you're measuring)

🧵 A Confounding variable can influence the outcome of an experiment in many ways, such as:

Invalid correlations.
Increasing variance.
Introducing a bias.

1⃣1⃣ Essential #Distributions That Data Professional Use 95% of Time [ Imp Thread ]

✅Statistical models assume an underlying #data generation process📊

Source - Avi Chawla

🧵 > C: Continuous
> D: Discrete

1) #NormalDistribution (C)-

- Most common in #datascience
- Characterized by a symmetric bell-shaped curve
- Eg: Height of individuals

SQL Injection💉📊 [Must Read]

✅It is essential to study #SQLinjection attacks nowadays because they continue to threaten security of #webapplications & sensitive data they store🚀

🧵 ✅Devastating Consequences of #SQL
Injection Attacks

Data Theft
Data Corruption
Server Compromise
Denial of Service (DoS)
Compliance Violation

Normalization in #SQL [Must Read]🚀

✅It is #database design technique that reduce data redundancy & eliminate undesirable characteristics like Insertion, Update & Deletion Anomalies

✅#Normalization rule divides larger table into smaller table & links them using relationship ✅The purpose of #Normalization in SQL is to eliminate redundant (repetitive) #data & ensure data is stored logically

✅SQL #Key is used to identify duplicate information, & it also helps establish a relationship between multiple tables in the #Database

Complete #PowerBI Topics with Sub Topics📙📊🚀

🧵 1️⃣ Introduction to #PowerBI :

  - Understanding Power BI ecosystem.

  - Differentiating between Power BI Desktop, #PowerBIService and Power BI Mobile.

  - Navigating #PowerBIinterface and exploring its various components

Data Analyst Projects Ideas 💡📊

🧵 1⃣ Exercise Analysis Project👨‍💻

github.com/sachinkumar160…

#DataAnalyst Project on T20 World Cup 2022 using #Python📊🥳🏏

It involves collecting & processing #data related to tournament, performing various analyses, & creating visualizations to gain insights

🧵 Importing #Python libraries

this code is setting up environ for creating interactive visualizations using #Plotly & configuring default template to use white background with other style setting

Once this configuration is set, you can proceed to create and customize your Plotly

Differential Calculus in #DataScience🔬🧮📊🚀

It plays Significant role in #Data Domain in various ways

#Differentialcalculus provides data scientists with essential tools to model, analyze & optimize complex data & algorithms

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