Packt
Intermediate Data Manipulation and Machine Learning
Packt

Intermediate Data Manipulation and Machine Learning

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Identify and describe core concepts of AI and machine learning

  • Explain and illustrate various regression analysis techniques to solve real-world problems

  • Utilize methods to build and evaluate robust machine learning models

  • Assess clustering and dimensionality reduction methods for data analysis

Details to know

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Recently updated!

September 2024

Assessments

6 assignments

Taught in English

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This course is part of the R Ultimate 2023 - R for Data Science and Machine Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 14 modules in this course

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.

What's included

3 videos2 readings

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.

What's included

12 videos

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.

What's included

6 videos1 assignment

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.

What's included

2 videos

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.

What's included

7 videos

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.

What's included

4 videos1 assignment

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.

What's included

5 videos

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.

What's included

5 videos

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.

What's included

5 videos1 assignment

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.

What's included

1 video

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.

What's included

7 videos

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.

What's included

10 videos1 assignment

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.

What's included

12 videos

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.

What's included

6 videos1 reading2 assignments

Instructor

Packt - Course Instructors
Packt
375 Courses24,936 learners

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Packt

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