Machine Learning Fundamentals

About Course

This foundational course introduces the core concepts, algorithms, and practical applications of machine learning.
Participants will explore the theoretical underpinnings of key ML paradigms, including supervised and
unsupervised learning, and gain hands-on experience in implementing, evaluating, and optimizing common
machine learning techniques. Utilizing Python and popular libraries such as Scikit-learn, Pandas, and NumPy, the
curriculum expertly balances conceptual understanding with practical skills. This prepares students to effectively
tackle diverse real-world problems, including predictive modeling, classification, and data clustering

Grasp the core principles and paradigms of machine learning, including model training, validation, overfitting, and the bias-variance tradeoff.
Implement and assess fundamental supervised learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines for both classification (e.g., spam detection, image
recognition) and regression tasks (e.g., housing price prediction, stock forecasting).
Apply unsupervised learning techniques like K-Means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA) for dimensionality reduction, with practical applications in customer segmentation and anomaly
detection.
Master the complete machine learning workflow, from initial data collection and cleaning to robust feature engineering, strategic model selection, training, rigorous evaluation, and basic deployment considerations.
Develop the ability to select appropriate algorithms based on specific problem characteristics, dataset size, data type, and interpretability requirements.
Identify and effectively address common challenges in machine learning applications, including handling missing values, managing imbalanced datasets, performing hyperparameter tuning, and ensuring strong model
generalization.

Course Content

Introduction to Machine Learning & Ecosystem
Explore core machine learning concepts such as features, labels, models, training, and prediction. Differentiate between supervised, unsupervised, and reinforcement learning paradigms, with illustrative examples like recommendation systems and medical diagnostics. Gain an overview of the machine learning pipeline, its broad applications across industries, and inherent limitations. This module also introduces the Python ML ecosystem, including Jupyter notebooks and foundational libraries like NumPy and Pandas.

Data Preparation and Feature Engineering
Learn essential data cleaning techniques for handling missing values (imputation, removal) and noisy data. Master various preprocessing steps, including encoding categorical features (one-hot encoding, label encoding) and applying normalization/standardization. Dive into feature selection methods (e.g., RFE, Chi-squared) and feature extraction techniques (e.g., PCA) to optimize model performance and interpretability.

Supervised Learning: Classification Algorithms
Study foundational classification algorithms: Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN). Understand their underlying mathematical principles, practical implementations in Scikit-learn, and how to apply them to real-world classification problems like sentiment analysis or fraudulent transaction detection. This module emphasizes common evaluation metrics such as accuracy, precision, recall, F1-score, and ROC curves, alongside strategies for multi-class classification.

Supervised Learning: Regression Algorithms
Delve into linear regression, polynomial regression, and advanced regularization techniques like Ridge and Lasso Regression to prevent overfitting. Explore Decision Tree Regression and Ensemble Methods for regression tasks. Learn to apply these models to predict continuous outcomes, such as sales figures or temperature, and master evaluation metrics specific to regression, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.

Unsupervised Learning and Dimensionality Reduction
Discover popular clustering algorithms such as K-means, Hierarchical Clustering, and DBSCAN, and their application in identifying hidden patterns in data (e.g., customer segmentation). Explore dimensionality reduction techniques like Principal Component Analysis (PCA) and t-SNE for visualizing high-dimensional data and mitigating the curse of dimensionality. This module also covers basic concepts of association rule mining and common evaluation methods for unsupervised models.

Model Evaluation, Selection, and Improvement
Implement robust cross-validation strategies (k-fold, stratified k-fold) for reliable model assessment. Understand the critical bias-variance tradeoff and learn practical methods for hyperparameter tuning using techniques like Grid Search and Random Search. Apply ensemble techniques (Bagging, Boosting) to enhance model performance. Learn to identify and address common issues like overfitting and underfitting using diagnostic tools, learning curves, and validation curves.

Capstone Project
Students will design and implement a comprehensive machine learning solution for a chosen real-world problem, utilizing provided datasets or their own. This hands-on project will encompass every stage of the ML workflow: detailed data exploration (EDA), rigorous data preparation, thoughtful feature engineering, strategic model selection, thorough training, and meticulous evaluation. Students will compare the performance of multiple algorithms, optimize their chosen model's parameters, and document their findings in a detailed technical report outlining their methodology, results, and insights. A final presentation of the project will involve showcasing a working model and explaining key decisions.

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Gain access to essential Python libraries, including NumPy for numerical operations, Pandas for data manipulation, and Scikit-learn for a wide array of
machine learning algorithms.
Utilize powerful visualization tools like Matplotlib and Seaborn for
insightful data analysis and model interpretation.
The course provides curated datasets covering various problem types (e.g., UCI Machine Learning Repository datasets, Kaggle competition data) and interactive Jupyter notebooks that serve as practical,
Hands-on learning labs, enabling direct experimentation with code examples and exercises.

Machine Learning Fundamentals

About Course

What Will You Learn?

Course Content

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Machine Learning Fundamentals

Machine Learning Fundamentals

About Course

What Will You Learn?

Course Content

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