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Applied Machine Learning with Python

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Applied Machine Learning with Python

Instructor: Edureka

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

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 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.

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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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This course offers an in-depth, practical introduction to machine learning using Python, covering core concepts across supervised, unsupervised, and semi-supervised methods.

Through hands-on exercises, you will master key algorithms such as decision trees and random forests for classification, regression models for prediction, and K-means clustering to uncover patterns in unlabeled data. You will also learn how to implement model boosting techniques to enhance accuracy and apply strategies for effectively leveraging unlabeled data to improve performance. This course is designed for learners with a foundation in Python and basic statistics, making it ideal for aspiring data scientists, machine learning practitioners, and Python developers looking to deepen their skills. By the end of this course, You will be able to: - Explain and implement decision trees and random forests as classification algorithms. - Define and differentiate various types of machine learning algorithms. - Analyze the working of regression for predictive tasks. - Apply K-means clustering to explore and discover patterns in unlabeled data. - Use unlabeled data to improve model training. - Manipulate boosting algorithms to achieve higher model accuracy. Equip yourself with practical tools and advanced techniques to bring predictive power to your projects. Enroll now and advance your AI journey!

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

30 videos•Total 135 minutes
  • Course Introduction•4 minutes
  • Machine Learning in Industry•4 minutes
  • How Companies use Machine Learning•5 minutes
  • Machine Learning Process•5 minutes
  • Steps in Machine Learning•4 minutes
  • Types of Machine Learning•6 minutes
  • Introduction to Linear Regression•4 minutes
  • Real Life Examples•3 minutes
  • Calculating OLS •7 minutes
  • Equation of OLS•3 minutes
  • Assumptions in Linear Regression•4 minutes
  • Demonstration: Setting Up the Model •5 minutes
  • Calculating R - Square and RMSE •5 minutes
  • Residual Plot and Q-Q Plot •2 minutes
  • Cook's Distance•5 minutes
  • Real - Life Examples of Logistic Regression•3 minutes
  • What is Logistic Regression•6 minutes
  • Cost Function•2 minutes
  • Assumptions in Logistic Regression•4 minutes
  • Demonstration of Logistic Regression: Transforming Data•6 minutes
  • Demonstration of Logistic Regression: Developing the Model•4 minutes
  • Confusion Matrix•4 minutes
  • Example for Calculating Confusion Matrix•6 minutes
  • Conditions for Over-Fitting and Under-Fitting•5 minutes
  • Overfitting and Underfitting•5 minutes
  • Performance Metrics - MSE, RMSE, MAE, MAPE •5 minutes
  • R - Square, RMSLE and Adjusted R - Square•4 minutes
  • Working of R - Square•5 minutes
  • Significance of R - Square•6 minutes
  • Summary for Inception of Machine Learning•3 minutes
6 readings•Total 70 minutes
  • Welcome to Applied Machine Learning with Python•10 minutes
  • How Companies are Crafting the Future•20 minutes
  • Machine Learning 101•10 minutes
  • Regression and its Assumptions•10 minutes
  • Role of Regularization•10 minutes
  • Evaluation of All Things Predictive•10 minutes
5 assignments•Total 42 minutes
  • Knowledge Check : Introduction to Machine Learning•30 minutes
  • Practice Quiz : AI and Augment of Machine Learning•3 minutes
  • Practice Quiz : Overview of Machine Learning•3 minutes
  • Practice Quiz : Regression•3 minutes
  • Practice Quiz : Evaluation Metrics•3 minutes
2 discussion prompts•Total 20 minutes
  • Introduce Yourself•10 minutes
  • Which of the following evaluation metrics is most suitable for Regression models?•10 minutes

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

34 videos•Total 156 minutes
  • Classification in Machine Learning•6 minutes
  • What is Decision Tree?•5 minutes
  • Decision Tree - Entropy and Information Gain•5 minutes
  • Step by Step Building of Decision Tree•5 minutes
  • Pruning in Decision Tree•3 minutes
  • Demonstration: Importing Data•6 minutes
  • Demonstration: Building Decision Tree and Random Forest •5 minutes
  • Demonstration: Importance of Features•2 minutes
  • Demonstration: Production Ready Random Forest•3 minutes
  • Demonstration: Hyperparameter Tuning•3 minutes
  • What is SVM?•4 minutes
  • Terminologies in SVM•6 minutes
  • Hinge Loss Function and Other Parameters•7 minutes
  • Demonstration of SVM - Exploring the Data•3 minutes
  • Demonstration of SVM - Setting up the SVM Classifier•6 minutes
  • What is Naive Bayes?•4 minutes
  • Working of Naive Bayes: Bayes Theorem•4 minutes
  • Example of Naive Bayes Algorithm•5 minutes
  • Demonstration of Naive Bayes Code•4 minutes
  • Working of KNN •3 minutes
  • Example of KNN Algorithm•4 minutes
  • Demonstration of KNN - Setting Up the Model•5 minutes
  • Demonstration of KNN - Transforming and Scaling Data•4 minutes
  • Demonstration of KNN - Creating Classifier•3 minutes
  • Dimensionality Reduction•7 minutes
  • Introduction to PCA•5 minutes
  • Applying PCA•6 minutes
  • Eigen Values and Eigen Vectors•5 minutes
  • Demonstration: Initializing PCA•3 minutes
  • Demonstration: Determining Optimal Number of Components through PCA•4 minutes
  • Demonstration: Implementing Optimal PCA•5 minutes
  • Working of LDA•5 minutes
  • Demonstration of LDA•6 minutes
  • Summary for Machine Learning Algorithms•3 minutes
3 readings•Total 60 minutes
  • Decision Trees and Random Forests•20 minutes
  • SVM, KNN and Naive Bayes: When to Use Which Algorithm?•20 minutes
  • Best Practices for Dimensionality Reduction: PCA vs. LDA•20 minutes
4 assignments•Total 48 minutes
  • Knowledge Check : Machine Learning Algorithms•30 minutes
  • Practice Quiz : Decision Tree and Random Forest•6 minutes
  • Practice Quiz : SVM, KNN and Naive Bayes Algorithm•6 minutes
  • Practice Quiz : Dimensionality Reduction•6 minutes
1 discussion prompt•Total 20 minutes
  • Which algorithm among SVM, KNN, and Naive Bayes do you find easier to use?•20 minutes

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

20 videos•Total 112 minutes
  • What are Association Rules?•7 minutes
  • Apriori Algorithm•6 minutes
  • Demonstrating Apriori Algorithm•7 minutes
  • What are Recommendation Engine?•6 minutes
  • CBF•7 minutes
  • Demonstration of Recommendation Engine: Preparing Data•6 minutes
  • Demonstration: Testing the Model•5 minutes
  • Elements for Reinforcement Learning•7 minutes
  • Demonstration of Boosting: Explaining the Dataset•6 minutes
  • Demonstration of Boosting: Cleaning and Transforming Dataset•6 minutes
  • Demonstration of Boosting: Factors Affecting Promotion•5 minutes
  • Demonstration of Boosting: Total Score and Service Affecting Promotion•5 minutes
  • Demonstration of Boosting: Age , Previous Year rating Influencing Promotion•4 minutes
  • Demonstration of Boosting: Department Influencing Promotion•5 minutes
  • Demonstration of Boosting: Education Affecting Promotion and Summarization•5 minutes
  • Demonstration of Boosting: Modeling the Data•5 minutes
  • Demonstration of Boosting: Building a Model•6 minutes
  • Working of K-Means Algorithm•6 minutes
  • Demonstration of K-Means Clustering•6 minutes
  • Summary for Association Rule Mining and Recommendation System•3 minutes
3 readings•Total 55 minutes
  • FP-Growth in Association Rule•10 minutes
  • How Recommendation Engines Personalize Your World•20 minutes
  • Training Models to Get Better with Experience•25 minutes
4 assignments•Total 48 minutes
  • Knowledge Check : Association Rules and Recommendation System•30 minutes
  • Practice Quiz : Association Rules•6 minutes
  • Practice Quiz : Recommendation Engines•6 minutes
  • Practice Quiz : Reinforcement Learning and Boosting•6 minutes

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

1 video•Total 3 minutes
  • Course Summary for Applied Machine Learning with Python•3 minutes
1 reading•Total 30 minutes
  • Final Project: Cab Booking Demand Analysis•30 minutes
1 assignment•Total 30 minutes
  • Knowledge Check : Applied Machine Learning•30 minutes
1 discussion prompt•Total 10 minutes
  • Describe Your Learning Journey•10 minutes

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Instructor

Edureka
203 Courses•185,285 learners

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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.

The course utilizes Google Colab as the primary platform for coding operations. Learners may also use integrated development environments (IDEs) like Jupyter Notebook, PyCharm, Spyder, or VS Code for more extensive coding projects if desired.

You should be comfortable with Python basics and have a foundation in statistics or linear algebra. Prior exposure to data analysis libraries is helpful but not required.

The course covers regression (linear, logistic), classification, decision trees, ensemble methods, and introduces advanced algorithms like SVMs.

Yes, it equips you with practical ML skills that are highly valued in data science, analytics, and AI career paths.

Machine learning opens opportunities in data science, AI engineering, business analytics, and research roles across industries.

Yes, you’ll receive a Coursera certificate to validate your skills and share with employers and on LinkedIn.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,