🚀 AI / ML Engineering Journey

Welcome to my Artificial Intelligence & Machine Learning Engineering Journey.

This repository documents my learning progress, experiments, assignments, and projects while mastering AI, Machine Learning, Deep Learning, and Generative AI.

The goal of this repository is to build industry-level AI engineering skills and create real-world projects.

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👨‍💻 About Me

Atharv Prabhakar Navatre

Computer Engineering Student passionate about:

• Artificial Intelligence • Machine Learning • Generative AI • Full Stack Development

I am building this repository as a public portfolio of my AI engineering journey.

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🎯 Repository Goals

• Master AI and Machine Learning fundamentals • Implement algorithms from scratch • Build industry-level AI projects • Learn the complete AI engineering stack • Document learning publicly

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🧭 AI/ML Learning Roadmap

Python & Data

• Python programming • Variables & operators • Conditional statements • Loops & flow control • Functions & lambda functions • Lists, tuples, dictionaries, sets • File handling & JSON • Object Oriented Programming

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Data Processing & Visualization

Libraries used:

• NumPy • Pandas • Matplotlib • Seaborn

Topics:

• Data cleaning • Data preprocessing • Exploratory Data Analysis (EDA) • Data visualization

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Mathematics for AI

• Statistics • Probability • Linear Algebra • Calculus • Central Limit Theorem

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Machine Learning

Supervised Learning

• Linear Regression • Logistic Regression • Naive Bayes • K-Nearest Neighbors • Decision Trees

Unsupervised Learning

• K-Means Clustering • DBSCAN • PCA

Reinforcement Learning

Learning through rewards and feedback.

Concepts:

• Precision • Recall • F1 Score • Bias-Variance Tradeoff

Tools:

• Scikit-learn • Kaggle

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Deep Learning

• Neural Networks • Forward & Backward Propagation • Perceptron

Architectures:

• Feed Forward Neural Network (FNN) • Recurrent Neural Network (RNN) • Long Short Term Memory (LSTM) • Convolutional Neural Network (CNN) • Transformers

Frameworks:

• PyTorch • TensorFlow • Keras

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Generative AI

• Large Language Models (LLMs) • GANs • RAG (Retrieval Augmented Generation) • Agentic AI

Tools:

• Cursor AI • GitHub Copilot • Claude • OpenAI APIs

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AI Engineering Stack

• Flask • HTML / CSS / JavaScript • SQL • Git & GitHub • Docker • Kubernetes

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📁 Repository Structure

ai-ml-engineering-journey

01_python_fundamentals
02_data_analysis
03_machine_learning
04_deep_learning
05_generative_ai
06_ai_engineering_stack

projects
datasets
notebooks
docs

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⚙️ Development Workflow

Learn Concept
      ↓
Implement in Python
      ↓
Experiment with Dataset
      ↓
Train ML/DL Model
      ↓
Evaluate Model
      ↓
Build Project
      ↓
Deploy AI Application

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🏗️ AI Project Architecture

Dataset
   ↓
Data Preprocessing
   ↓
Feature Engineering
   ↓
Machine Learning Model
   ↓
Model Evaluation
   ↓
API (Flask / FastAPI)
   ↓
Frontend Interface
   ↓
Deployment (Docker / Cloud)

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🧠 Projects

Projects in this repository include:

• Sentiment Analysis • Customer Segmentation (Clustering) • Medical Prediction Model • Finance Data Analysis • Generative AI Assistant

More projects will be added as the journey progresses.

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🛠️ Tech Stack

Languages

Python

Libraries

NumPy Pandas Matplotlib Seaborn Scikit-learn PyTorch TensorFlow Keras

Tools

Git GitHub Docker Kubernetes Flask

AI Tools

OpenAI API Cursor AI GitHub Copilot Claude

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📊 Learning Progress

This repository will continuously evolve with:

• Lecture implementations • Assignments • Experiments • Mini projects • Major AI applications

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🌐 Connect With Me

GitHub https://github.com/Atharv-navatre

Twitter / X https://x.com/AtharvNava39873

LinkedIn https://www.linkedin.com/in/atharv-navatre-1201662b1/

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📜 License

This project is licensed under the MIT License.

You are free to use, modify, and distribute the code with proper attribution.

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⭐ If you like this repository, consider starring it and following my AI learning journey.