Intermediate Data Manipulation and Machine Learning

Packt via Coursera

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Overview

In this comprehensive course, you will explore artificial intelligence (AI) and its core concepts, forming a solid foundation for machine learning. You will delve into regression analysis, applying univariate, polynomial, and multivariate regression techniques to real-world problems through interactive labs. Next, you will learn model preparation and evaluation, focusing on underfitting, overfitting, data splitting, and resampling methods, alongside regularization techniques to enhance model performance. The course covers classification methods, including confusion matrices, ROC curves, decision trees, random forests, logistic regression,...

Syllabus

Machine Learning: Introduction

In this module, we will lay the groundwork for understanding AI and machine learning. We will start by exploring the core concepts of AI, delve into the fundamentals of machine learning, and gain insights into how models are built and trained to solve real-world problems.

Machine Learning: Regression

In this module, we will dive deep into regression analysis, starting with an overview of different regression types. We will then explore univariate and multivariate regression, including hands-on labs and exercises, to solidify our understanding of these essential techniques.

Machine Learning: Model Preparation and Evaluation

In this module, we will focus on preparing and evaluating machine learning models. We will explore critical concepts like underfitting and overfitting, learn to split data for model assessment, and practice resampling techniques to ensure robust model performance.

Machine Learning: Regularization

In this module, we will delve into the fundamentals of regularization. We will explore how techniques like L1 and L2 regularization work and practice applying them in hands-on lab sessions to enhance the reliability and performance of our models.

Machine Learning: Classification Basics

In this module, we will cover the basics of classification. We will start with confusion matrices and ROC curves, then engage in interactive and lab sessions to gain hands-on experience in evaluating and optimizing classification models.

Machine Learning: Classification with Decision Trees

In this module, we will explore decision trees for classification. We will learn how they work, engage in lab sessions to build and implement decision tree models, and apply our knowledge to solve practical classification problems.

Machine Learning: Classification with Random Forests

In this module, we will delve into Random Forests. We will understand the principles of ensemble learning, engage in coding labs to build and optimize Random Forest models, and explore how these techniques improve classification performance.

Machine Learning: Classification with Logistic Regression

In this module, we will explore logistic regression for classification. We will learn how logistic regression models work, engage in coding labs to build and interpret these models, and apply our knowledge to solve practical classification tasks.

Machine Learning: Classification with Support Vector Machines

In this module, we will delve into Support Vector Machines (SVM). We will learn how SVMs work, engage in coding labs to build and optimize SVM models, and apply our knowledge to solve challenging classification tasks.

Machine Learning: Classification with Ensemble Models

In this module, we will explore ensemble models. We will understand how these techniques work, discover how they enhance classification performance, and evaluate their impact on model accuracy and robustness.

Machine Learning: Association Rules

In this module, we will delve into association rules. We will explore the fundamentals of this technique, apply the Apriori algorithm in hands-on labs, and practice extracting meaningful associations and patterns from real-world datasets.

Machine Learning: Clustering

In this module, we will explore clustering techniques. We will start with an overview, then dive into specific methods like k-means, hierarchical clustering, and DBSCAN. Through hands-on labs and exercises, we will gain practical experience in grouping data and uncovering patterns.

Machine Learning: Dimensionality Reduction

In this module, we will delve into dimensionality reduction. We will explore techniques like PCA and t-SNE, engage in practical lab sessions, and apply these methods to simplify and interpret complex data structures.

Machine Learning: Reinforcement Learning

In this module, we will explore reinforcement learning. We will understand the mechanisms of RL algorithms, apply the UCB algorithm in interactive and lab sessions, and gain practical skills in optimizing RL agents for better decision-making in uncertain environments.