Machine Learning Engineer Roadmap

Stage 1: Programming Foundations (Month 1–2)

Goal:

Build a strong base in programming and logic — mainly in Python.

Learn:

• Python Basics: variables, loops, conditionals, functions, OOP (Object-Oriented Programming)
  
• Data Structures & Algorithms:
  
  • Lists, Tuples, Dictionaries
  • Sorting, Searching
  • Stack, Queue, Tree, Graphs (basic understanding)
• Math for ML (Intro):
  
  • Linear Algebra (vectors, matrices)
  • Probability & Statistics (mean, variance, distributions)
  • Calculus basics (derivatives, gradients)

 Tools:

• Python (Anaconda, Jupyter Notebook, VS Code)
• Libraries: numpy, pandas, matplotlib

 Mini Projects:

• Temperature Converter
• Basic Calculator
• Data visualization (using matplotlib)

 Resources:

• YouTube: Telusko, CodeWithHarry, freeCodeCamp
• W3Schools / SoloLearn (you already use them 👏)

Stage 2: Data Handling & Preprocessing (Month 3)

 Goal:

Learn to clean, prepare, and explore data — a key ML step.

Learn:

• Data Loading & Cleaning (pandas)
• Handling Missing Values, Outliers
• Feature Engineering
• Data Normalization & Scaling
• Exploratory Data Analysis (EDA)

Tools:

• pandas, numpy, matplotlib, seaborn, scikit-learn

 Projects:

• Analyze a dataset (Kaggle or UCI): e.g., Titanic, Iris
• Create visual insights dashboards
  

 Resources:

• Kaggle “Intro to Data Science”
• YouTube: Krish Naik / Codebasics

Stage 3: Core Machine Learning (Month 4–6)

 Goal:

Understand ML algorithms and how to train, test, and evaluate models.

 Learn:

• Supervised Learning:
  
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
    
• Unsupervised Learning:
  
  • K-Means Clustering
  • Hierarchical Clustering
  • PCA (Dimensionality Reduction)
    
• Model Evaluation:
  
  • Accuracy, Precision, Recall, F1-score
  • Confusion Matrix, ROC-AUC
    

Tools:

• scikit-learn
• Jupyter Notebook

Projects:

• House Price Prediction (Regression)
• Spam Detection (Classification)
• Customer Segmentation (Clustering)

Resources:

• “Hands-On Machine Learning with Scikit-Learn & TensorFlow” by Aurélien Géron
• Kaggle ML Tutorials

Stage 4: Deep Learning (Month 7–9)

Goal:

Understand and build neural networks for images, text, and more.

Learn:

• Neural Network Basics
  • Perceptron, Activation Functions, Loss Functions
  • Gradient Descent, Backpropagation
    
• Deep Learning Frameworks
  • TensorFlow, Keras, PyTorch
    
• CNNs (Convolutional Neural Networks) – for images
• RNNs / LSTMs – for text/time series
• Transfer Learning

Tools:

• TensorFlow, Keras, PyTorch
• Google Colab (free GPU)

Projects:

• Digit Recognition (MNIST)
• Image Classifier (Cats vs Dogs)
• Sentiment Analysis (IMDB Dataset)

Resources:

• Coursera: DeepLearning.AI (Andrew Ng)
• YouTube: Codebasics, freeCodeCamp

Stage 5: Data Engineering & Deployment (Month 10)

Goal:

Learn how to take your models from notebooks to production.

Learn:

• Model Deployment:
  • Flask / FastAPI for ML APIs
  • Streamlit for web dashboards
    
• Model Versioning & Tracking:
  • MLflow, DVC (optional)
    
• Cloud Platforms:
  • AWS Sagemaker / Google Cloud AI / Azure ML
    
• Containerization:
  
  • Docker basics
    

Projects:

• Deploy ML model as API using Flask
• Streamlit app for predictions (e.g., price predictor)

Stage 6: Advanced Topics (Month 11–12)

Learn:

• Natural Language Processing (NLP): Tokenization, Transformers, BERT
• Computer Vision (CV): Object Detection (YOLO, OpenCV)
• Reinforcement Learning: Q-Learning, Policy Gradients
• MLOps Basics: CI/CD for ML, model monitoring

 Projects:

• Chatbot using NLP
• Object Detection app
• ML model pipeline automation

Stage 7: Build Portfolio & Job Prep (Ongoing)

Goal:

Showcase your skills and get ML Engineer opportunities.

Build:

• Portfolio Website or GitHub Profile
• 5–8 Real Projects (small but clear)
• LinkedIn Optimization
• Contribute to open-source ML repos
  

Example Portfolio Projects:

1. Customer Churn Prediction
2. Image Classification Web App
3. Movie Recommendation System
4. Sentiment Analysis Tool
5. Medical Diagnosis ML model
   

Job Prep:

• Learn System Design for ML
• Practice ML Interview Questions
• Study Maths behind algorithms

Recommended Project Flow (Hands-on Path)

Stage	Project	Concept Covered
1	Calculator / Data Visualizer	Python Basics
2	Data Cleaning + EDA	Data Preprocessing
3	Spam Detector	Classification
4	Image Classifier	Deep Learning
5	ML API with Flask	Deployment
6	Chatbot or Recommender	NLP / Advanced
7	Portfolio Website	Showcasing Work

Daily Study Plan (You can customize)

Day Type	Hours	Focus
Mon–Sat	2 hrs	Learning + Practice
Sunday	4 hrs	Project work + Revision

Final Goal:

By following this roadmap for 12 months, you’ll be ready to apply for roles like:

• Machine Learning Engineer
• Data Scientist
• AI Developer
• Deep Learning Engineer