Applied Machine Learning 🚀

This repository documents my structured journey of learning Applied Machine Learning through theory, hands-on coding, feature engineering, and end-to-end projects.

The goal is to build strong ML foundations and apply them to real datasets using industry-standard tools and workflows.

📌 What This Repository Contains

• 📘 Concept notes (clear explanations of ML fundamentals)
• 🧪 Jupyter notebooks with implementations
• 📊 Exploratory Data Analysis (EDA)
• 🛠 Feature engineering techniques
• 🔄 ML pipelines and preprocessing workflows
• 📈 End-to-end mini projects

🗂 Progress Table

#	Topic	Description	Link
01	Introduction to ML	Overview, history, real-world examples, ML terminology	01-introduction-to-ml
02	AI vs ML vs DL	Conceptual differences with examples and diagrams	02-ai-vs-ml-vs-dl
03	Types of ML	Supervised, Unsupervised, Reinforcement Learning with use-cases	03-types-of-ml
04	Batch Learning	Training in offline settings, advantages & limitations	04-batch-learning
05	Online Learning	Incremental learning and streaming data concepts	05-online-learning
06	Instance vs Model Based Learning	Lazy vs eager learning comparison	06-instance-vs-model-based
07	Challenges in ML	Data quality, overfitting, bias, scalability issues	07-challenges-in-ml
08	ML Applications	Real-world use cases across industries	08-ml-applications
09	ML Development Lifecycle	Problem definition → data → modeling → deployment	09-ml-development-lifecycle
10	Data Based Job Roles	Analyst → Engineer → Scientist → ML Ops	10-various-data-based-job-roles
11	Tensor in ML	Scalars → vectors → matrices → tensors	11-tensors-in-ml
13	End-to-End Placement Project	Logistic Regression project with full ML workflow	13-placement-project-logistic-regression
15	Working with CSV Files	Data loading, preprocessing using pandas	15-working-with-CSV-files
16	Working with JSON & SQL	Data extraction from structured sources	16-working-with-json-Sql
17	API to DataFrame	Fetching API data and converting to pandas DataFrame	17-api-to-dataframe
19	Understanding Your Data	Data inspection, missing values, data types	19-understanding-your-data
20	Univariate Analysis	Statistical & visual analysis of single variables	20-univariate-analysis
21	Multivariate Analysis	Relationship analysis between multiple features	21-multivariate-analysis
22	Pandas Profiling	Automated EDA report generation	22-pandas-profilling
23	Feature Extraction	Creating new features from raw data	23-feature-extraction
24	Standardization	Scaling features using StandardScaler	24-standardization
25	Normalization	MinMax scaling & feature range transformation	25-normalization
26	Ordinal Encoding	Encoding ordinal categorical variables	26-ordinal-encoding
27	One Hot Encoding	Encoding nominal categorical features	27-one-hot-encoding
28	Column Transformer	Applying different transformations to different columns	28-column-transformer
29	ML Pipelines	Building preprocessing + model pipelines using sklearn	29-ml-pipelines
30	Function Transformer	Exploring Function Transformer, applying custom transformations, and analyzing results on Titanic data	30-function-transformer
31	Power Transformer	Exploring Box-Cox and Yeo-Johnson transformations and analyzing their impact on skewed features in Titanic data	31-power-transform
32	Binning and Binarization Transformer	Applying discretization (binning) and binarization techniques	32-binning-and-binarization
33	Handling Mixed Variables	Engineering structured features from mixed variables	33-handling-mixed-variables
34	Handling Date and Time	Practical date-time feature engineering techniques	34-handling-date-and-time
35	Complete Case Analysis	Handling missing values using complete case analysis	35-complete-case-analysis
...	...	Ongoing daily learning	...

🛠 Tools & Technologies

• Python
• NumPy
• Pandas
• Matplotlib / Seaborn
• Scikit-learn
• Jupyter Notebook

▶ How to Use

1. Clone the repository
2. Navigate to a topic folder
3. Read the README.md for theory
4. Open the notebook to see practical implementation

🎯 Objectives

• Strengthen machine learning foundations
• Implement concepts through real datasets
• Practice feature engineering & preprocessing
• Build complete ML pipelines
• Document consistent daily progress

📜 License

This project is licensed under the MIT License.