Starting a 100 Days Code Challenge for Learning Data Science from Scratch is my goal on Learning Data Science in Machine Learning by:

• Learning Fundamentals of Python
• Python Libraries for Data Science
• Data Manipulation and Preprocessing
• Machine Learning Basics
• Advanced Machine Learning Techniques
• Deep Learning and Neural Networks
• Model Evaluation and Deployment
• Data Science Project and Wrap-Up

• Project 1: Bank Management System: Python, OOPS, and MySQL Database

• 25 days Completion: Successful Completion of 25 Days in 100 Days of Data Science Code

• Control flow statements like if-else conditions and loops.

Github Repository: Source Code

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• Concept of modules.
• How to import and use built-in modules as well as create your own.

GitHub Repository: Source Code

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• Python's built-in data structures such as lists, tuples, dictionaries, and sets.
• Also, learn about indexing, slicing, and manipulating these data structures.

GitHub Repository: Source Code

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• Read from and write to files in Python.
• Learn about exception handling and how to handle errors using try-except blocks.

GitHub Repository: Source Code

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• Class Declaration
• Object Instantiation
• Constructor and Destructor
• Built-in Class Attributes and Functions
• Instance, Class and Static Variables and Functions.

GitHub Repository: Source Code

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• Data Abstraction
• Encapsulation
• Inheritance
• Polymorphism.

GitHub Repository: Source Code

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• Higher Order Functions
• List Comprehensions
• Regular Expressions (RegEx)

GitHub Repository: Source Code

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• Setting Up MySQL Connection
• Executing SQL Queries.

GitHub Repository: Source Code

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• Database Setup
• Python Environment Setup
• Database Connectivity
• Create Basic Classes
• Customer Management.

GitHub Repository: Source Code

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• Account Management(Create Account, List Account Details)
• Basic Error Handling(Apply Validations on Input values)
• Testing and Debugging(Checking Input value validations).

GitHub Repository: Source Code

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• Transfer Operation
• Final Testing and Documentation
• Clean Up and Deployment.

GitHub Repository: Source Code

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• Introduction to NumPy
• Installing NumPy
• Creating NumPy arrays
• Array indexing and slicing
• Array reshaping and resizing
• Stacking and splitting arrays.

GitHub Repository: Source Code

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• Element-wise Operations
• Aggregation Functions
• Linear Algebra with NumPy.

GitHub Repository: Source Code

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• Descriptive statistics
• Random number generation
• Sorting and searching arrays

GitHub Repository: Source Code

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• Introduction to Pandas
• Install Pandas
• Types of Data Structures : Series, DataFrames
• Importing and Exporting DataFrames
• DataFrame Functions
• Accessing DataFrames : Indexing, Slicing, loc[], iloc[].

GitHub Repository: Source Code

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• Advanced Indexing and Selection - (Label-based indexing, boolean indexing, and advanced slicing)
• Combining DataFrames - (Concatenation, merging, and joining techniques)
• Data Manipulation
• Advanced Data Manipulation - (reshaping data, pivoting, and melting)
• Data Aggregation and Grouping - (groupby() and other aggregation Functions)

GitHub Repository: Source Code

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• Basic Data Cleaning and Pre-Processing:
  • Removing Duplicates
  • Fixing Wrong Data
  • Cleaning Data of Wrong Format
  • Cleaning Empty Cells
  • dropna(), fillna()
  • drop_duplicates()
• Data Transformation - ( apply() and map() )
• Working with Text Data - Functions of str attribute

GitHub Repository: Source Code

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• Feature Engineering:
  • Data Normalization
  • Data Scaling
  • Data Standardization
• Time Series Analysis and Resampling:
  • Working with datetime data
  • Date offsets
  • Resampling time series data
  • Datetime index

GitHub Repository: Source Code

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• Matplotlib:
  • Installation of Matplotlib library
  • Import Matplotlib library
• Matplotlib Pyplot:
  • Plotting x and y points
  • Plotting without line
  • Matplotlib Markers (Types, Color, Size)
  • Matplotlib Line (LineStyle, Line colors, line width)
  • Single Plot with multiple lines
  • Matplotlib Labels and Title (Create Label, Create Title, Set font properties to Title and Label, Title Position)
  • Adding Grid Lines (Line Properties of grid)
• Matplotlib Bars:
  • Vertical Bars
  • Horizontal Bars
  • Bar colors
  • Bar width
  • Bar height

GitHub Repository: Source Code

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• Subplots:
  • subplot() function
  • Title for each subplot
  • Super title of Plot
• Matplotlib Scatter Plot:
  • Create Scatter Plots
  • Compare Plots
  • Color each dots
  • ColorMap for dots
  • Combine Color, Size and Alpha values
• Matplotlib Histograms:
  • Create Histogram
• Matplotlib Pie Charts:
  • Create Pie Chart
  • Labels
  • startAngle
  • Explode
  • Shadow
  • Colors
  • Legend
  • Header

GitHub Repository: Source Code

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• Seaborn:
  • Installation of Seaborn
  • Import Seaborn library
• Different types of plots:
  • Relational Plots
  • Categorical Plots
  • Distribution Plots
  • Regression Plots
• Categorical Plots:
  • Bar Plot
  • Count Plot
  • Box Plot
  • Violinplot
  • Stripplot
  • Swarmplot
  • Factorplot
• Distribution Plots:
  • Histogram
  • Distplot
  • Jointplot
  • Pairplot
  • Rugplot
  • KDE Plot

GitHub Repository: Source Code

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• Customizing Seaborn Plots:

  • Changing Figure Asthetics
  • Removal of Spines
  • Changing the Figure size
  • Scaling the plots
  • Setting the Style Temporarily
  • Color Palette - (Diverging, Sequential, Default color palette)

• Multiple Plots with Seaborn:

  • Using Matplotlib - (add_axes(), subplot(), subplot2grid() functions)
  • Using Seaborn - (FacetGrid() method, PairGrid() method)

• Relational Plot Types:

  • relplot()
  • Scatter Plot
  • Line Plot

• Regression Plot Types:

  • lmplot
  • RegPlot

• Matrix Plots:

  • HeatMap
  • Clustermap

GitHub Repository: Source Code

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• Introduction
  • Features
  • Applications
• Identifiers:
  • Keywords
  • Variables and Constants
• Operators in python
• Data types in python
  • String data type and operations
  • List data type and operations
  • Tuple data type and operations
  • Set data type and operations
  • Dictionary data type and operations
• Control Statements in python:
  • Decision making
  • looping statements
  • looping control statements

GitHub Repository: Source Code

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• Introduction
  • Installation
  • Import
• Create arrays in python
• Array creation using NumPy Functions
  • zeros
  • ones
  • arange
  • linspace
  • eye
  • identity
  • fromiter
• Accessing array elements
  • Indexing and Slicing
• Random number Generation
  • rand()
  • random()
  • ranf()
  • randint()
  • randn()

GitHub Repository: Source Code

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• Introduction - Install, Import
• Data Structures:
  • Series
  • DataFrames
• DataFrames
  • Importing and Exporting
  • Functions - columns, describe(), info(), head(), tail(), isna()
  • Accessing DataFrames - loc[], iloc[],
• Basic Data Cleaning:
  • Empty Cells
  • Wrong Format Data
  • Fixing Wrong Data
  • Removing Duplicates
• Apply filters
  • apply()
  • map() - Using Dictionary, Series, Function for mapping

GitHub Repository: Source Code

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• Artificial Intelligence:
• Machine Learning:
  • Difference between Artificial Intelligence and Machine Learning
  • Applications of Machine Learning
  • Limitations of Machine Learning
  • Types of Machine Learning
    • Supervised Learning
    • Unsepervised Learning
    • Reinforcement Learning
  • Comparisons between all types

GitHub Repository: Source Code

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• 1. Data Preprocessing:
  • Data Cleaning
  • Feature Selection/Extraction
  • Normalization/Scaling
  • Encoding Categorical Variables
  • Splitting Data
• 2. Model Training:
  • Selecting a Model
  • Initializing Parameters
  • Training Loop
  • Gradient Descent (for Optimization)
  • Hyperparameter Tuning
• 3. Model Evaluation:
  • Metrics
  • Cross-Validation
  • Confusion Matrix
  • ROC and AUC
  • Overfitting and Underfitting

GitHub Repository: Source Code

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• Cross-Validation
• Evaluation Metrics:
  • Accuracy
  • Precision
  • Recall
  • F1-Score
  • Area Under Curve (AUC) and Receiver Operating Characteristic (ROC)
• Confusion Matrix
• Overfitting and Underfitting Detection:
  • Overfitting
  • Underfitting

GitHub Repository: Source Code

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• Underfitting:
  • Choosing a more complex model
  • Adding more features
  • Fine-tuning hyperparameters
• Overfitting:
  • Collect more data
  • Feature selection
  • Cross-validation
  • Regularization techniques
  • Early stopping

GitHub Repository: Source Code

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• Linear Regression Introduction
• Simple Linear Regression:
  • Assumptions of Simple LR
  • Equation of Simple LR
  • Applications of Linear Regression
  • Working of Linear Regression
  • Finding goodness of fit
  • Examples of Linear Regression
  • Implementation of Simple Linear Regression
  • Real-world Application: Salary Prediction

GitHub Repository: Source Code

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• Multiple Linear Regression (MLR):
  • Key points of MLR
  • Equation of MLR
  • Assumptions of MLR
  • Implementation of MLR using Python
  • Real-world Application: Student Performance Analysis

GitHub Repository: Source Code

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• Classification

• Types of Learners:

  • Lazy Learners: Firstly, store dataset and wait until receive test dataset.
  • Eager Learner: Develop classification model based on training dataset, before receiving testing dataset.

• Types of Classification Algorithms:

  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Neural Networks

• Terminologies in Classification:

  • Features and Labels
  • Training and Testing Data
  • Confusion Matrix
  • Precision, Recall, F1-Score
  • ROC and AUC Curve

• Types of Classification:

  • Binary Classification: Two classes (e.g., Yes/No)
  • Multiclass Classification: Multiple distinct classes (e.g., Cat/Dog/Horse)

• Models' Evaluation Techniques for Classification: Used for finding goodness of model's fit:

  • Accuracy
  • Precision and Recall
  • F1-Score
  • ROC Curve and AUC
  • Confusion Matrix

GitHub Repository: Source Code

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• Logistic Regression:
  • Logistic Function (Sigmoid Function)
  • Assumptions of Logistic Regression
  • Types of Logistic Regression:
    • Binary / Binomial
    • Multinomial
    • Ordinal
  • Terminologies involved in Logistic Regression
  • Implementation of Logistic Regression
• Difference between Linear Regression and Logistic Regression

GitHub Repository: Source Code

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• Decision Tree:
  • Components of a Decision Tree
    • Root Node
    • Internal Nodes
    • Leaf Nodes
  • Attribute Selection Measures(ASM):
    • Entropy
    • Information Gain
    • Gini Index
  • How Decision Trees Work
  • Advantages of Decision Trees

GitHub Repository: Source Code

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• Decision Tree Implementation Setup:
  • Data Pre-processing
  • Model Training
  • Predicting the Results
  • Model Evaluation Techniques
• Examples for Decision Tree Implementation:
  • IRIS Flower Classification
  • Red Wine Quality Prediction

GitHub Repository: Source Code

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• Ensemble Methods:
  • Bagging
  • Boosting
  • Stacking
  • Advantages of Ensemble Methods

GitHub Repository: Source Code

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• Gradient Boosting in Machine Learning:
  • What is Gradient Boosting
  • Key Components of Gradient Boosting
  • How Gradient Boosting Works
  • Benefits of Gradient Boosting

GitHub Repository: Source Code

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• AdaBoost and XGBoost:
  • AdaBoost (Adaptive Boosting)
  • XGBoost (Extreme Gradient Boosting)
  • Advantages of AdaBoost and XGBoost
  • Applications

GitHub Repository: Source Code

LinkedIn post: Daily Update

• Random Forests:
  • What are Random Forests
  • Key Components of Random Forests
  • How Random Forests Work
  • Benefits of Random Forests
  • Real-world Applications of Random Forests

GitHub Repository: Source Code

LinkedIn post: Daily Update

• Random Forest Implementation:
  • Step-by-Step Approach
  • IRIS Flower Prediction
  • Red Wine Quality Prediction
• Hyperparameter Tuning:
  • Unlocking Model Potential
  • GridSearchCV
  • RandomizedSearchCV

GitHub Repository: Source Code

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• Decision Tree in Action
• Enchantment of Random Forests
• Social Media Ads prediction

GitHub Repository: Source Code

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• Introduction to SVM
• Terminologies used in SVM
• Advantages of SVM
• Limitations of SVM

GitHub Repository: Source Code

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• SVM Implementation:
  • Linear SVM (Social Media Ads) : Kaggle Notebook
  • Non-Linear SVM (IRIS Flower Prediction) : Kaggle Notebook

GitHub Repository: Source Code

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• SVM Regression Implementation:
  • Salary Prediction : Kaggle Notebook
  • Boston Housing Price Prediction : Kaggle Notebook

GitHub Repository: Source Code

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• KNN Introduction
• Distance Metrics:
  • Euclidean Distance
  • Manhatten Distance
  • Minkowski Distance
• How KNN works
• How to choose value of 'K'

GitHub Repository: Source Code

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• KNN Classification:
  • IRIS Flower Prediction : Kaggle Notebook
  • Mushroom Clasification : Kaggle Notebook

• KNN Regression:
  • Employee Salary Prediciton : Kaggle Notebook
  • Student Performance Prediction : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

• KNN Regression:
  • House Price Prediction : Kaggle Notebook
• KNN Classification:
  • BMI Classification : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update