Program Objectives:

1. Machine Learning Engineer
2. Data Scientist
3. Data Analyst
4. Business Intelligence Analyst
5. Artificial Intelligence Developer
6. Data Engineer
7. Research Scientist
8. Predictive Modeler
9. Analytics Consultant
10. Machine Learning Researcher

What you will learn?

 

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Module 1: Introduction to Supervised Machine Learning

Section 1.1: Understanding Supervised Learning

• Subsection 1.1.1: What is Machine Learning?
  • Definition and categories (Supervised, Unsupervised, Reinforcement Learning)
• Subsection 1.1.2: Key Concepts in Supervised Learning
  • Training and testing
  • Features and labels

Section 1.2: Applications of Supervised Learning

• Subsection 1.2.1: Common Use Cases
  • Email spam detection
  • Predicting house prices
  • Customer churn analysis
• Subsection 1.2.2: Benefits and Limitations

Section 1.3: Setting Up the Python Environment

• Subsection 1.3.1: Installing Python and Jupyter Notebook
• Subsection 1.3.2: Key Libraries for Machine Learning
  • NumPy, Pandas, Matplotlib, Scikit-learn

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Module 2: Data Preprocessing and Exploration

Section 2.1: Preparing the Dataset

• Subsection 2.1.1: Loading Data with Pandas
• Subsection 2.1.2: Handling Missing Values
  • Imputation techniques (mean, median, mode)

Section 2.2: Exploring the Dataset

• Subsection 2.2.1: Descriptive Statistics
  • Mean, median, standard deviation
• Subsection 2.2.2: Data Visualization
  • Histograms, scatter plots, correlation heatmaps

Section 2.3: Feature Engineering

• Subsection 2.3.1: Encoding Categorical Data
  • One-hot encoding, label encoding
• Subsection 2.3.2: Feature Scaling
  • Standardization and normalization

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Module 3: Building Supervised Machine Learning Models

Section 3.1: Regression Models

• Subsection 3.1.1: Linear Regression
  • Concept and mathematical representation
  • Training and evaluating a linear regression model using Scikit-learn
• Subsection 3.1.2: Polynomial Regression
  • When and why to use polynomial regression
  • Implementing polynomial regression in Python

Section 3.2: Classification Models

• Subsection 3.2.1: Logistic Regression
  • Concept and sigmoid function
  • Building and evaluating a logistic regression model
• Subsection 3.2.2: k-Nearest Neighbors (k-NN)
  • How k-NN works (distance metrics, k-value selection)
  • Implementing k-NN for classification tasks

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Module 4: Advanced Techniques in Supervised Learning

Section 4.1: Decision Trees and Random Forests

• Subsection 4.1.1: Decision Trees
  • How decision trees work (splitting criteria, entropy, and Gini index)
  • Building and visualizing decision trees in Python
• Subsection 4.1.2: Random Forests
  • Ensemble learning and bagging
  • Training a random forest model using Scikit-learn

Section 4.2: Model Evaluation

• Subsection 4.2.1: Evaluation Metrics
  • Regression: Mean Squared Error (MSE), R-squared
  • Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC
• Subsection 4.2.2: Cross-Validation
  • Concept of k-Fold Cross-Validation
  • Implementing cross-validation for robust evaluation

Section 4.3: Hyperparameter Tuning

• Subsection 4.3.1: Grid Search
• Subsection 4.3.2: Random Search

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Module 5: Deployment and Real-World Applications

Section 5.1: Model Deployment Basics

• Subsection 5.1.1: Saving and Loading Models
  • Using joblib and pickle
• Subsection 5.1.2: Integrating Models into Applications
  • Simple API deployment using Flask

Section 5.2: Real-World Project

• Subsection 5.2.1: Problem Definition
  • Example: Predicting customer churn for a telecom company
• Subsection 5.2.2: Model Development
  • Data preprocessing, training, evaluation
• Subsection 5.2.3: Deployment and Presentation
  • Building a basic user interface to showcase predictions

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Capstone Project: End-to-End Implementation

• • Goal: Apply everything learned in the course to solve a real-world supervised learning problem.
  • Steps:
    • Data preprocessing
    • Model selection and evaluation
    • Hyperparameter tuning
    • Deployment of the final model
  • Outcome: A portfolio-worthy project demonstrating your expertise in supervised machine learning.