7-Day Machine Learning Bootcamp: From Data to Deployment

 7-Day Machine Learning Bootcamp: From Data to Deployment

Course Objective

By the end of this course, participants will understand the entire lifecycle of machine learning models, from data preprocessing to deployment. They will work with supervised and unsupervised learning algorithms, explore recommender systems, and practice model evaluation and deployment strategies.

Day 1: Introduction and Data Preprocessing

Objective: Understand the basics of machine learning, and learn how to prepare data for training models.

  • Morning:

    • Introduction to Machine Learning
      • Overview of machine learning types: supervised, unsupervised, reinforcement
      • Applications and real-world examples
    • Data Preprocessing Basics
      • Cleaning data: Handling missing values, outliers, and duplicates
      • Feature scaling: Normalization and standardization
      • Encoding categorical variables (One-hot encoding, label encoding)

  • Afternoon:

    • Hands-on Practice:
      • Load and preprocess a sample dataset using Python (Pandas, NumPy, Scikit-learn)
      • Exploratory Data Analysis (EDA): Visualizing data distributions and correlations

Day 2: Supervised Learning Basics

Objective: Learn foundational supervised learning algorithms and their applications.

  • Morning:

    • Decision Trees:
      • Concept, advantages, and limitations
      • Building decision trees using Scikit-learn
    • Logistic Regression:
      • Understanding the sigmoid function and binary classification
      • Implementing logistic regression models

  • Afternoon:

    • Hands-on Practice:
      • Train decision tree and logistic regression models on real-world datasets
      • Evaluate models using metrics like accuracy, precision, recall, and F1-score

Day 3: Advanced Supervised Learning

Objective: Dive deeper into KNN and Random Forest algorithms and hyperparameter tuning.

  • Morning:

    • K-Nearest Neighbors (KNN):
      • How KNN works, selecting the optimal value of K
      • Applications of KNN in classification and regression
    • Random Forest:
      • Bagging and ensemble methods
      • Feature importance and handling overfitting

  • Afternoon:

    • Hands-on Practice:
      • Compare performance of KNN and Random Forest on classification problems
      • Use GridSearchCV for hyperparameter tuning

Day 4: Unsupervised Learning

Objective: Understand clustering techniques and apply K-Means in practice.

  • Morning:

    • Introduction to Clustering
      • Difference between supervised and unsupervised learning
      • Applications of clustering (e.g., customer segmentation)
    • K-Means Clustering:
      • Concept of centroids, inertia, and elbow method
      • Limitations and assumptions

  • Afternoon:

    • Hands-on Practice:
      • Perform K-Means clustering on a real-world dataset
      • Visualize clusters and analyze the results

Day 5: Recommender Systems

Objective: Build basic recommender systems using association rules and collaborative filtering.

  • Morning:

    • Association Rules:
      • Apriori and FP-Growth algorithms
      • Understanding support, confidence, and lift
    • Collaborative Filtering:
      • User-based and item-based collaborative filtering
      • Matrix factorization concepts

  • Afternoon:

    • Hands-on Practice:
      • Build an association rule-based recommender system
      • Implement collaborative filtering for movie or product recommendations

Day 6: Model Evaluation and Optimization

Objective: Learn to evaluate, interpret, and optimize machine learning models.

  • Morning:

    • Evaluation Metrics:
      • Classification: ROC-AUC, confusion matrix, and cross-validation
      • Clustering: Silhouette score, Davies-Bouldin index
    • Overfitting and Underfitting:
      • Regularization techniques (L1, L2)
      • Cross-validation strategies

  • Afternoon:

    • Hands-on Practice:
      • Experiment with different evaluation metrics
      • Optimize models using regularization and validation techniques

Day 7: Model Deployment

Objective: Understand how to deploy a machine learning model for production.

  • Morning:

    • Introduction to Model Deployment:
      • Challenges and considerations in deployment
      • Deployment frameworks: Flask, FastAPI
    • Building an API for ML Models

  • Afternoon:

    • Hands-on Practice:
      • Deploy a pre-trained model as a REST API using Flask
      • Test the API using Postman or a similar tool

End of Course Project

Participants will work in teams to build an end-to-end machine learning project:

  • Select a problem (e.g., fraud detection, customer segmentation)
  • Perform data preprocessing and EDA
  • Train and evaluate a model using the techniques learned
  • Deploy the model using Flask or FastAPI

Deliverables: Final presentation and deployed model.


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input to AI model: design a course for this spread across 7 days .  The course covers the entire lifecycle of machine learning models, from data preprocessing to model deployment, utilizing algorithms such as decision trees, logistic regression, KNN, and Random Forest. Additionally, participants will explore unsupervised learning techniques like clustering and K-Means and build recommender systems using association rules and collaborative filtering.


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