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Machine Learning Code Crash Course

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Rating4.2
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Machine Learning Code Crash Course, available at Free, has an average rating of 4.2, with 8 lectures, based on 12 reviews, and has 1569 subscribers.

You will learn about Machine Learning Algorithms Coding Supervised Learning & Unsupervised Learning Data Visualization and Data Preprocessing This course is ideal for individuals who are Beginners in Machine Learning Coding It is particularly useful for Beginners in Machine Learning Coding.

Enroll now: Machine Learning Code Crash Course

Summary

Title: Machine Learning Code Crash Course

Price: Free

Average Rating: 4.2

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

  • Machine Learning Algorithms
  • Coding
  • Supervised Learning & Unsupervised Learning
  • Data Visualization and Data Preprocessing

Who Should Attend

  • Beginners in Machine Learning Coding

Target Audiences

  • Beginners in Machine Learning Coding

Unlock the full potential of machine learning with our comprehensive Advanced Machine Learning Coding in Python course. Designed for both beginners and experienced developers, this course will take you on a deep dive into the world of machine learning and equip you with the skills and knowledge needed to excel in this rapidly evolving field.

In this hands-on course, you’ll embark on a journey that starts with the fundamentals of machine learning and gradually progresses to advanced techniques and real-world applications. You will gain proficiency in Python, the primary programming language of choice for machine learning, and learn how to harness powerful libraries such as sci-kit-learn to build and train complex models.

I will guide you through a structured curriculum that covers topics like data preprocessing, feature engineering, model selection, hyperparameter tuning, and deploying machine learning models. You’ll work on practical exercises, projects, and case studies, applying your newfound skills to solve real-world problems.

By the end of this course, you’ll be well-prepared to tackle the most challenging machine-learning tasks, from image and text classification to regression and reinforcement learning. Whether you’re aiming to advance your career, enhance your data analysis skills, or develop innovative machine-learning applications, this course provides the foundation you need. Join us and become a proficient machine-learning practitioner in Python!

Course Curriculum

Chapter 1: Supervised Learning

Lecture 1: Linear Regression

Lecture 2: Logistic Regression

Lecture 3: Ridge and Lasso Regression

Lecture 4: Decision Tree Classifier

Lecture 5: Decision Tree Regressor

Lecture 6: Random Forest Classifier

Lecture 7: XGBOOST Classifier

Chapter 2: Unsupervised Learning

Lecture 1: Kmeans, DBSCAN and PCA Clustering Algorithms

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 0 votes
  • 3 stars: 3 votes
  • 4 stars: 3 votes
  • 5 stars: 6 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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