Machine Learning - Data Science Course

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Course Structure

This course is divided into the following modules:

1. Introduction to Data Science and Machine Learning

   • What is Data Science?
   • Overview of Machine Learning
   • Applications of Machine Learning in various industries

2. Introduction to Pandas

   • Installing and setting up Pandas
   • Pandas DataFrames and Series
   • Data manipulation (filtering, sorting, grouping)
   • Handling missing data

3. Exploratory Data Analysis (EDA)

   • Descriptive statistics with Pandas
   • Data visualization techniques
   • Correlation and covariance
   • Feature engineering

4. Supervised Learning

   • Introduction to supervised learning
   • Linear regression, Logistic regression
   • Decision trees, Random forests
   • Model evaluation (accuracy, precision, recall, F1 score)

5. Unsupervised Learning

   • Introduction to unsupervised learning
   • Clustering techniques (K-Means, Hierarchical)
   • Dimensionality reduction (PCA)
   • Anomaly detection

6. Data Preprocessing

   • Data normalization and standardization
   • Handling categorical data
   • Data splitting (train/test)
   • Cross-validation

7. Model Deployment and Applications

   • Introduction to model deployment
   • Using Flask/Django for simple model deployment
   • Applications in real-world scenarios

Resources

• Textbook/Reference: [Book Title, Author]
• Online Documentation:
  • Pandas Documentation
  • Scikit-Learn Documentation
  • Matplotlib Documentation
• Datasets: Various datasets used in this course can be found in the datasets/ directory.

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