1. Introduction to Machine Learning
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What is Machine Learning?
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Difference between AI, ML, DL, and Data Science
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Types of Machine Learning: Supervised, Unsupervised, Reinforcement
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Applications of Machine Learning
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Setting up ML Environment (Anaconda, Jupyter Notebook)
2. Python Libraries for Machine Learning
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Working with NumPy and Pandas
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Data Visualization with Matplotlib and Seaborn
3. Data Preprocessing & Exploratory Data Analysis (EDA)
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Understanding Dataset Structure
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Handling Missing Data
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Encoding Categorical Variables
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Feature Scaling: Standardization, Normalization
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Feature Engineering Basics
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Descriptive Statistics
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Visualizing Data Distributions
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Correlation Matrix & Heatmaps
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Outlier Detection
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Extracting Insights from Data Patterns
4. Supervised Learning Algorithms
Regression Algorithms:
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Linear Regression
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Multiple Linear Regression
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Polynomial Regression
Classification Algorithms:
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Logistic Regression
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K-Nearest Neighbors (KNN)
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Support Vector Machines (SVM)
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Decision Trees
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Random Forest
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Naive Bayes
Model Evaluation Techniques:
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Confusion Matrix
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Accuracy, Precision, Recall, F1 Score
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ROC-AUC Curve
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Mean Squared Error (MSE), RMSE, MAE
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K-Fold Cross Validation
5. Unsupervised Learning Algorithms
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Introduction to Unsupervised Learning
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Clustering Algorithms:
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K-Means Clustering
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Hierarchical Clustering
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DBSCAN
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Dimensionality Reduction: PCA (Principal Component Analysis)
6. Feature Selection & Engineering
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Feature Importance
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Removing Multicollinearity
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Recursive Feature Elimination (RFE)
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Using Domain Knowledge for Feature Selection
7. Model Deployment
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Saving Models using Pickle or Joblib
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Creating a Simple Web App using Streamlit or Flask
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Introduction to Model Deployment on Cloud (basic overview)
8. Advanced Topics (Optional / Analyst Focus)
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Ensemble Learning: Bagging, Boosting (AdaBoost, XGBoost)
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Hyperparameter Tuning: GridSearchCV, RandomizedSearchCV
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Introduction to Time Series Forecasting
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Basics of Natural Language Processing (NLP)
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Ethics and Bias in Machine Learning
9. Real-World Project Workflow
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Defining the Problem
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Data Collection & Cleaning
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EDA & Preprocessing
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Model Building & Evaluation
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Interpretation & Communication of Results
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Report or Dashboard Creation
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Deployment (Optional)
Course Outcomes
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Understand and apply ML concepts including supervised, unsupervised learning, and real-world applications.
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Preprocess and analyze data using Python libraries (NumPy, Pandas, Matplotlib, Seaborn) and perform feature engineering.
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Develop and evaluate models for regression, classification, clustering, and dimensionality reduction using proper metrics.
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Implement advanced ML techniques such as ensemble methods, hyperparameter tuning, and time series forecasting.
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Deploy models using Streamlit, Flask, or cloud platforms and create end-to-end ML solutions.
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Build job-ready projects with practical insights, ensuring skills for industry roles in Machine Learning and Data Science.
