IBM - Machine Learning With Python
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About this course
The Machine Learning with Python course offers a comprehensive introduction to the world of machine learning through one of the most popular and accessible programming languages - Python. Designed for learners who already have a foundation in Python for data science, this course bridges the gap between coding skills and practical machine learning applications.
The course begins by explaining the core concepts of Supervised vs. Unsupervised Learning and how these differ from traditional statistical modeling. Learners will gain a solid understanding of how machines can be trained to make predictions and find patterns, exploring both the theoretical and practical aspects.
Throughout the modules, students will work with popular algorithms such as Classification, Regression, Clustering, and Dimensionality Reduction. They will also gain hands-on experience with common models and tools like Train/Test Split, Root Mean Squared Error (RMSE), Random Forests, and evaluation metrics.
Key learning topics include:
Supervised Learning using algorithms such as K-Nearest Neighbors, Decision Trees, and Random Forests, along with an analysis of their advantages and limitations.
Unsupervised Learning techniques like K-Means Clustering, Hierarchical Clustering, and Density-Based Clustering, including methods to measure distances between clusters.
Dimensionality Reduction through feature extraction and selection, and Collaborative Filtering for recommendation systems, including its real-world challenges.
With real-life examples woven throughout, the course shows how machine learning is shaping industries and society in unexpected ways.
By the end of this course, participants will be able to:
Understand the key differences between supervised and unsupervised learning and when to apply each approach.
Implement and evaluate machine learning algorithms using Python.
Build predictive models using classification and regression techniques, and assess their performance to avoid overfitting and underfitting.
Apply clustering algorithms to uncover patterns and groupings in data without predefined labels.
Perform dimensionality reduction to optimize model performance and interpretability.
Develop recommendation systems using collaborative filtering techniques.
Critically analyze machine learning models, understanding their limitations and potential societal impact.
This course equips learners with the essential tools, algorithms, and analytical thinking required to start building intelligent systems, preparing them for further study or professional applications in the rapidly growing field of machine learning.
Prerequisites
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This module introduces foundational concepts of Machine Learning (ML) by contrasting it with traditional statistical modeling. It covers the key types of ML—Supervised, Unsupervised, and Reinforcement Learning—highlighting
their approaches to learning from data. The module explains how Supervised Learning uses labeled datasets for tasks like classification and regression, while Unsupervised Learning works with unlabeled data to uncover hidden patterns
through techniques like clustering and dimensionality reduction. It also provides practical examples such as spam detection, customer segmentation, and stock prediction, helping learners distinguish between different ML tasks and their appropriate algorithms.
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Module 2 introduces the concept of Supervised Learning, where algorithms are trained using labeled data to make predictions on unseen inputs. The module explores key supervised techniques like K-Nearest Neighbors (K-NN), Decision Trees, and Random Forests, which are widely used for classification and regression tasks. Learners understand how each algorithm functions, their real-world applications, and their strengths and limitations. Key concepts such as pruning (to avoid overfitting in decision trees) and ensemble learning (in random forests) are also introduced to show how supervised models can be optimized for better accuracy and reliability.
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Module 3 of the IBM Machine Learning with Python course focuses on regression algorithms and model evaluation in supervised learning. Unlike classification, which predicts categorical outcomes, regression predicts continuous values such as prices, sales, or temperatures. The module introduces key regression algorithms like Linear Regression, Polynomial Regression, Decision Tree Regression, Random Forest Regression, and K-Nearest Neighbors Regression. It also covers the importance of model evaluation using methods like train-test split and cross-validation, and explains critical concepts like overfitting and underfitting. The module concludes by describing standard regression evaluation metrics such as MAE, MSE, RMSE, R2, and MAPE, helping learners understand how to choose and evaluate models for real-world applications.
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Module 4 introduces Unsupervised Learning, a powerful branch of machine learning that deals with unlabeled data. Unlike supervised learning, where models learn from labeled input-output pairs, unsupervised learning algorithms try to discover hidden patterns or groupings in data without any explicit labels. This module focuses on techniques like Clustering (K-Means, Hierarchical, DBSCAN), Association Rule Learning, and Dimensionality Reduction, and explains how they are used in real- world applications such as customer segmentation, anomaly detection, recommendation systems, and market basket analysis. It also discusses concepts like linkage methods and distance measurements in hierarchical clustering.
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Module 5 focuses on two critical machine learning techniques: Dimensionality Reduction and Collaborative Filtering. Dimensionality Reduction simplifies datasets by reducing the number of input variables while preserving essential information. It improves model performance, reduces overfitting, and makes data visualization easier. This is achieved through Feature Selection and Feature Extraction using techniques like PCA and LDA. The second part introduces Collaborative Filtering, a popular recommendation system technique that suggests items based on user or item similarity. It is used by platforms like Amazon and Netflix and comes with challenges like scalability and sparsity. Together, these topics enhance the learner’s understanding of managing large datasets and creating personalized experiences.
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