Machine Learning: Concepts and Applications

Go to Course: https://www.coursera.org/learn/machine-learning-applications

Introduction

## Course Review: Machine Learning: Concepts and Applications on Coursera ### Overview "Machine Learning: Concepts and Applications" is a comprehensive course designed for both beginners and those who wish to deepen their understanding of machine learning (ML) principles. Hosted on Coursera, this course combines theoretical insights with practical applications using Python and industry-standard libraries such as Pandas, Scikit-learn, and TensorFlow. As the complexities of machine learning expand, this course breaks down essential concepts into manageable modules, making it easier to grasp the subtleties of data modeling and analysis. ### Course Structure The course is organized into thoughtfully structured modules, each covering a crucial aspect of machine learning. 1. **Machine Learning and the Machine Learning Pipeline**: This introductory module lays the groundwork. Students learn about the data ingestion process using Pandas and are introduced to linear regression, establishing a strong foundation for subsequent topics. 2. **Least Squares and Maximum Likelihood Estimation**: Diving deeper into linear regression, this module focuses on evaluating models, selecting significant features, and understanding maximum likelihood estimation (MLE). 3. **Basis Functions and Regularization**: Here, you will explore polynomial expansions and the bias-variance tradeoff, essential for understanding model performance. Regularization methods are introduced to combat overfitting, making this module practical and insightful. 4. **Model Selection and Logistic Regression**: An important transition into classification techniques, students learn how to utilize cross-validation, pipelines, and GridSearch for model tuning. Logistic regression is discussed in detail, allowing learners to grasp its application in classification problems. 5. **More Classifiers: SVMs and Naive Bayes**: This module introduces Support Vector Machines (SVMs) and the Naive Bayes classifier. These classifiers are vital in solving real-world problems and are presented with a strong emphasis on interpretability and efficacy. 6. **Tree-Based Models, Ensemble Methods, and Evaluation**: Students delve into decision trees and ensemble methods like bagging and boosting, gaining a thorough understanding of model evaluation metrics. 7. **Clustering Methods**: Venturing into unsupervised learning, learners explore various clustering techniques such as k-means and hierarchical clustering, broadening their statistical toolkit. 8. **Dimensionality Reduction and Temporal Models**: The course continues to elevate learners’ understanding with Principal Component Analysis for dimensionality reduction and Hidden Markov Models for temporal data analysis. 9. **Deep Learning**: Often the buzzword in tech circles, this module captures the essence of deep learning with an overview of feed-forward and convolutional networks. Students learn how to implement and evaluate deep learning models using Python and Keras. ### Pros and Cons #### Pros - **Comprehensive Content**: The course covers a wide range of ML topics, ensuring that learners acquire a well-rounded understanding. - **Hands-On Experience**: Practical exercises using popular libraries empower students to apply what they've learned in real-world scenarios. - **Flexibility**: Being an online course, it allows students to learn at their own pace, making it accessible to a wider audience. #### Cons - **Steep Learning Curve**: Some foundational knowledge in statistics and Python programming is recommended, which might be challenging for complete beginners. - **Pacing**: The speed at which some modules progress may feel rushed for those who prefer a more incremental learning style. ### Recommendation I highly recommend "Machine Learning: Concepts and Applications" for anyone interested in entering the field of machine learning or enhancing their current skill set. Its combination of theory and hands-on practice prepares students to tackle real-world problems effectively. Whether you aim to pursue a career in data science, work on AI projects, or simply gain a deeper understanding of the technological landscape, this course serves as a valuable resource. If you are already familiar with basic programming principles and have a budding interest in data science, this course will significantly enhance your skills and confidence in machine learning. Enroll today on Coursera and embark on your journey into the exciting world of machine learning!

Syllabus

Machine Learning and the Machine Learning Pipeline

In this module you will be introduced to the machine-learning pipeline and learn about the initial work on your data that you need to do prior to modeling. You will learn about how to ingest data using Pandas, a standard Python library for data exploration and preparation. Next, we turn to the first approach to modeling that we explore in this class, linear regression with ordinary least squares.

In this module, you continue the work that we began in the last with linear regressions. You will learn more about how to evaluate such models and how to select the important features and exclude the ones that are not statistically significant. You will also learn about maximum likelihood estimation, a probabilistic approach to estimating your models.

This module introduces you to basis functions and polynomial expansions in particular, which will allow you to use the same linear regression techniques that we have been studying so far to model non-linear relationships. Then, we learn about the bias-variance tradeoff, a key relationship in machine learning. Methods like polynomial expansion may help you train models that capture the relationship in your training data quite well, but those same models may perform badly on new data. You learn about different regularization methods that can help balance this tradeoff and create models that avoid overfitting.

In this module, you first learn more about evaluating and tuning your models. We look at cross validation techniques that will help you get more accurate measurements of your model's performance, and then you see how to use them along with pipelines and GridSearch to tune your models. Finally, we look a the theory and practice of our first technique for classification, logistic regression.

You will learn about two more classification techniques in this module: first, Support Vector Machines (SVMs) and then Naive Bayes, a quick and highly interpretable approach that uses Bayes' theorem.

In this module, you will first learn about classification using decision trees. We will see how to create models that use individual decision trees, and then ensemble models, which use many trees, such as bagging, boosting, and random forests. Then, we learn more about how to evaluate the performance of classifiers.

To this point, we have been focusing on supervised learning and training models that estimate a target variable that you have specified. In this module, we take our first look at unsupervised learning, a domain of machine learning that uses techniques to find patterns and relationships in data without you ever defining a target. In particular, we look at a variety of clustering techniques, beginning with k-means and hierarchical clustering, and then distribution and density-based clustering.

You will look at two new techniques in this module. The first is Principal Component Analysis, a powerful dimensionality reduction technique that you can use to project high-dimensional features into lower-dimensional spaces. This can be used for a range of purposes, including feature selection, preventing overfitting, visualizing in two- or three-dimensional spaces higher dimensional data, and more. Then, you will study hidden Markov models, a technique that you can use to model sequences of states, where each state depends on the one that came before.

This module introduces you to one of the most hyped topics in machine learning, deep learning with feed-forward neural networks and convolutional neural networks. You will learn about how these techniques work and where they might be very effective--or very ineffective. We explore how to design, implement, and evaluate such models using Python and Keras.