Machine Learning
Course Desscription
This 12-week Machine Learning course is designed to provide a structured and hands-on approach to mastering foundational and intermediate concepts in machine learning. The program equips participants with the knowledge and skills required to handle real-world data, build predictive models, and evaluate their performance effectively.
Participants will learn essential techniques such as data preprocessing, feature engineering, and advanced algorithms while developing proficiency in Python and libraries like Pandas, NumPy, and scikit-learn. The curriculum incorporates practical implementations, use cases, and best practices, ensuring a thorough understanding of each concept.
Main Features
This comprehensive Machine Learning course covers key concepts like data preprocessing, regression, decision trees, and ensemble methods using Python. It emphasizes practical applications, model evaluation, and advanced topics like gradient descent and regularization. The program concludes with a real-world project to solidify skills.
Learning Outcomes
- Understand the core concepts of machine learning, including supervised and unsupervised learning.
- Gain proficiency in Python libraries such as Pandas, NumPy, and scikit-learn for data manipulation and model implementation.
- Develop skills to preprocess data, engineer features, and handle missing values and outliers effectively.
- Implement and evaluate machine learning models like linear regression, logistic regression, decision trees, and ensemble methods.
- Master advanced concepts such as gradient descent, regularization, and bias-variance tradeoff.
- Apply machine learning techniques to solve real-world problems through a hands-on final project.
The Machine Learning course provides a comprehensive introduction to machine learning concepts using Python libraries like Pandas, NumPy, and scikit-learn. Topics include data preprocessing, feature engineering, exploratory data analysis (EDA), and implementing algorithms such as regression, decision trees, and ensemble methods. Students will gain practical skills to build, evaluate, and optimize predictive models for real-world applications.
List Of Weeks
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Class 1
Quick revision of Python basics, Pandas, Numpy, and data manipulation.
Brief review of data visualization, EDA, and feature engineering techniques.Duration: 1 hour 30 min
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Class 2
Handling missing data, detecting outliers, and revisiting PCA.
Recap of projects from the first course and discussion of common issues.Duration: 1 hour 30 min
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Class 3
Introduction to machine learning concepts: supervised vs unsupervised learning.
Simple and multiple linear regression.
Implementation of linear regression using sklearn.Duration: 1 hour 30 min
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Class 4
Evaluating linear regression models: R-squared, Mean Squared Error (MSE), etc.
Assumptions of linear regression: multicollinearity, heteroscedasticity, etc.Duration: 1 hour 30 min
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Class 5
Introduction to gradient descent and its importance.
Derivation and intuition behind batch gradient descent.
Implementation of batch gradient descent in Python.Duration: 1 hour 30 min
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Class 6
Stochastic gradient descent (SGD) and mini-batch gradient descent.
Comparison of all types of gradient descent.
Learning rate selection and its impact.Duration: 1 hour 30 min
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Class 7
Understanding bias-variance tradeoff in machine learning models.
Overfitting vs underfitting.
Regularization: L1 (Lasso) and L2 (Ridge).Duration: 1 hour 30 min
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Class 8
Implementing Ridge and Lasso regression using sklearn.
Cross-validation and grid search for hyperparameter tuning.Duration: 1 hour 30 min
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Class 9
Introduction to logistic regression for classification tasks.
Implementing logistic regression in Python using sklearn.
Model interpretation: coefficients and odds ratios.Duration: 1 hour 30 min
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Class 10
Evaluation measures for classification: accuracy, precision, recall, F1 score, ROC-AUC.
Confusion matrix and interpretation.Duration: 1 hour 30 min
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Class 11
Introduction to decision trees.
Splitting criteria: Gini impurity, entropy.
Building and visualizing decision trees using sklearn.Duration: 1 hour 30 min
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Class 12
Overfitting in decision trees and how to prune.
Evaluation of decision trees: cross-validation, performance measures.Duration: 1 hour 30 min
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Class 13
Introduction to ensemble learning and its importance.
Voting classifiers (hard and soft voting).
Implementation of voting ensembles using sklearn.Duration: 1 hour 30 min
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Class 14
Case study: Combining decision trees, logistic regression, and SVMs using voting.
Duration: 1 hour 30 min
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Class 15
Introduction to bagging and its advantages (e.g., Random Forest).
Implementing Random Forest using sklearn.Duration: 1 hour 30 min
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Class 16
Introduction to boosting (AdaBoost, Gradient Boosting).
Implementing boosting algorithms using sklearn.
Comparing bagging and boosting.Duration: 1 hour 30 min
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Class 17
Introduction to clustering techniques: K-means, hierarchical clustering.
Implementing K-means using sklearn.
Evaluating clustering: silhouette score, elbow method.Duration: 1 hour 30 min
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Class 18
Introduction to stacking as an ensemble technique.
Implementing stacking with multiple classifiers.Duration: 1 hour 30 min
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Class 19
Introduction to K-Nearest Neighbors (KNN).
Implementing KNN for classification using sklearn.
Case study: Evaluating KNN performance.Duration: 1 hour 30 min
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Class 20
Introduction to Support Vector Machines (SVM).
Implementing SVM in Python with kernels (linear, RBF).
Introduction to Naive Bayes and its assumptions.Duration: 1 hour 30 min
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Class 21
In-depth discussion of use cases for the models covered (e.g., healthcare, finance).
Best practices for deploying machine learning models in production.
Introduction to model interpretability: SHAP values, LIME.Duration: 1 hour 30 min
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Class 22
Discussion of final project topics and expectations.
Guidelines for completing and presenting the project.Duration: 1 hour 30 min
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Class 23
Student project presentations.
Peer review and feedback on each project.Duration: 1 hour 30 min
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Class 24
Course wrap-up.
Q&A session and discussion of future learning paths (deep learning, advanced ML).Duration: 1 hour 30 min
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