• 8 Sections
  • 46 Lessons
  • 8 Hours
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  • Module 1: Introduction to Machine Learning (45 min)
    6
    • 1.1
      What is Machine Learning?
    • 1.2
      Applications of ML in Real-World Scenarios
    • 1.3
      Types of ML: Supervised, Unsupervised, Reinforcement Learning
    • 1.4
      ML vs AI vs Deep Learning
    • 1.5
      Overview of ML Workflow (Data Collection → Preprocessing → Modeling → Evaluation → Deployment)
    • 1.6
      Popular ML Libraries: Scikit-Learn, TensorFlow, PyTorch
  • Module 2: Setting Up the Environment & Essential Libraries (30 min)
    4
    • 2.1
      Installing Python, Jupyter Notebook, and Anaconda
    • 2.2
      Overview of Libraries: NumPy, Pandas, Matplotlib, Seaborn
    • 2.3
      Hands-on: Loading and Visualizing a Dataset
    • 2.4
      Introduction to Google Colab for Cloud-Based ML
  • Module 3: Data Preprocessing & Feature Engineering (1 Hour)
    6
    • 3.1
      Understanding Data: Structured vs Unstructured
    • 3.2
      Handling Missing Values, Duplicates, and Outliers
    • 3.3
      Feature Scaling & Normalization (MinMaxScaler, StandardScaler)
    • 3.4
      Encoding Categorical Variables (Label Encoding, One-Hot Encoding, Ordinal Encoding)
    • 3.5
      Feature Selection: Removing Redundant & Irrelevant Features
    • 3.6
      Hands-on: Preprocessing a Real Dataset
  • Module 4: Supervised Learning - Regression (1 Hour 15 min)
    6
    • 4.1
      What is Regression?
    • 4.2
      Linear Regression: Concept & Implementation
    • 4.3
      Polynomial Regression: When to Use It?
    • 4.4
      Decision Tree & Random Forest Regression
    • 4.5
      Performance Metrics: RMSE, MAE, R² Score
    • 4.6
      Hands-on: Predicting House Prices using Regression
  • Module 5: Supervised Learning - Classification (1 Hour 15 min)
    6
    • 5.1
      What is Classification?
    • 5.2
      Logistic Regression: Understanding the Sigmoid Function
    • 5.3
      Decision Trees & Random Forest: Pros & Cons
    • 5.4
      Support Vector Machines (SVM): Concept & Implementation
    • 5.5
      Performance Metrics: Accuracy, Precision, Recall, F1 Score, Confusion Matrix, ROC Curve
    • 5.6
      Hands-on: Spam Email Detection using Classification
  • Module 6: Unsupervised Learning (1 Hour 15 min)
    6
    • 6.1
      Introduction to Clustering
    • 6.2
      K-Means Clustering: Algorithm & Practical Implementation
    • 6.3
      Hierarchical Clustering: Concept & Implementation
    • 6.4
      Anomaly Detection: Identifying Outliers in Data
    • 6.5
      Dimensionality Reduction: PCA (Principal Component Analysis)
    • 6.6
      Hands-on: Customer Segmentation Using Clustering
  • Module 7: Model Evaluation & Hyperparameter Tuning (1 Hour)
    6
    • 7.1
      Train-Test Split & Cross-Validation
    • 7.2
      Bias-Variance Tradeoff
    • 7.3
      Overfitting vs Underfitting
    • 7.4
      Hyperparameter Tuning: ○ Grid Search ○ Random Search ○ Automated Hyperparameter Tuning
    • 7.5
      Saving & Loading ML Models for Future Use
    • 7.6
      Hands-on: Tuning a Classification Model for Better Performance
  • Module 8: End-to-End ML Project (1 Hour)
    6
    • 8.1
      Choosing a Dataset for the Final Project (E.g., House Price Prediction, Customer Churn Analysis)
    • 8.2
      Data Cleaning, Feature Engineering, Model Selection
    • 8.3
      Building & Evaluating the ML Model
    • 8.4
      Introduction to Model Deployment (Flask, Streamlit, FastAPI)
    • 8.5
      Best Practices in Machine Learning Projects
    • 8.6
      Final Thoughts & Next Steps in ML Learning

Machine Learning Mastery: A Fast-Track 8-Hour Practical Bootcamp

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