This course provides an in-depth, hands-on introduction to machine learning using Python. You'll explore core concepts and methods, diving into supervised, unsupervised, and semi-supervised learning. Through practical exercises and examples, you'll master key algorithms including decision trees and random forests for classification, regression for predictive modeling, and K-means clustering for uncovering hidden patterns in unlabeled data. Additionally, you’ll gain insights into using model-boosting techniques to enhance model accuracy and apply strategies for leveraging unlabeled data effectively.
Applied Machine Learning with Python
Applied Machine Learning with Python
This course is part of Mastering AI: Neural Nets, Vision System, Speech Recognition Specialization
Instructor: Edureka
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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
What you'll learn
Explore machine learning algorithms, including supervised, unsupervised, and semi-supervised methods.
Apply decision trees, random forests, and K-means clustering for classification and clustering.
Develop machine learning models to gain insights and make predictions from real-world data.
Enhance model accuracy by applying model-boosting techniques and evaluating their effectiveness.
Details to know
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Recently updated!
February 2025
Assessments
14 assignments
Taught in English
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This course is part of the Mastering AI: Neural Nets, Vision System, Speech Recognition Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
In this module, learners will explore various types of machine learning and algorithms, such as Regression, along with different evaluation metrics that evaluate machine learning models at different stages of development.
What's included
30 videos6 readings5 assignments2 discussion prompts
This module will cover various supervised machine learning algorithms used to model data and provide desired results and conclusions, which will help individuals or organizations make informed decisions backed by data analysis.
What's included
34 videos3 readings4 assignments1 discussion prompt
This module covers association rule mining to uncover meaningful associations. Additionally, learners will explore how to build recommendation engines, which play a key role in personalizing user experiences, boosting user engagement, and driving sales across various industries.
What's included
20 videos3 readings4 assignments
This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on Python programming concepts, Regression Modeling, Supervised machine learning algorithms and Association rule mining.
What's included
1 video1 reading1 assignment1 discussion prompt
Recommended if you're interested in Machine Learning
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Frequently asked questions
This course, Applied Machine Learning with Python, focuses on teaching practical machine learning techniques using Python. It covers various algorithms, including decision trees, random forests, regression, and clustering, and guides learners in applying these methods to solve real-world problems.
The course emphasizes hands-on experience in building models, analyzing data, and improving model performance through techniques like boosting. By the end, learners will have the skills to implement machine learning algorithms, evaluate their effectiveness, and uncover valuable insights from data.
The Applied Machine Learning with Python course is ideal for aspiring data scientists, software developers, and professionals looking to enhance their skills in machine learning. It provides hands-on experience in building and deploying machine learning models using Python, making it perfect for those seeking to apply data-driven solutions in real-world scenarios.
The duration of this course is approximately 4 weeks, depending on the learner's pace, with an estimated commitment of 2-3 hours per week for lectures, hands-on projects, and assessments.