Machine-Learning-Learning-Journey

This repository documents my hands-on learning journey in Data Science, Machine Learning, Data Analysis, and Data Visualization using Python.

About

I created this repository to track my progress while learning Machine Learning and its supporting concepts through practical implementation. Rather than focusing only on theory, I aim to understand how machine learning workflows are applied to real-world datasets through data analysis, visualization, preprocessing, model training, and evaluation.

Topics Covered

Mathematics for Machine Learning

• Probability Fundamentals

Data Visualization

• Matplotlib Basics
• Bar Charts
• Histograms
• Pie Charts
• Scatter Plots
• Subplots
• Seaborn Visualizations

Machine Learning

• Iris Dataset Classification
• Model Training
• Model Evaluation and Accuracy Measurement
• Exploratory Data Analysis (EDA)
• House Price Prediction Dataset Analysis

Repository Structure

Machine-Learning-Learning-Journey
│
├── Mathematics-for-ML
├── Data-Visualization
├── Machine-Learning
│   ├── Iris-Classification
│   └── House-Price-Prediction
└── README.md

Technologies Used

• Python
• NumPy (Upcoming)
• Pandas (Upcoming)
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook

Current Progress

• Probability
• Data Visualization
• Exploratory Data Analysis
• Iris Classification
• Model Evaluation
• NumPy Fundamentals
• Pandas Fundamentals
• Feature Engineering
• Feature Scaling
• Machine Learning Pipelines
• Deep Learning & Neural Networks

Future Goals

• Complete core Machine Learning concepts
• Learn Deep Learning and Neural Networks
• Build end-to-end Machine Learning projects
• Participate in AI/ML competitions and hackathons
• Contribute to open-source AI projects

Author

Manisha Kumari

GitHub: https://github.com/Manisha7530

LinkedIn: linkedin.com/in/manisha-kumari-3a060b296