Select Your Favourite
Category And Start Learning.

( 0 Review )

Machine Learning in Practice

9,999.00

( 0 Review )

Course Level

Intermediate

Total Hour

40h

Video Tutorials

12

Course content

40h

Week 1: Introduction to Machine Learning and Data Preparation

Draft LesOverview of machine learning and its applicationsson
Types of machine learning algorithms (supervised, unsupervised, etc.)
Data preparation techniques, including cleaning, normalization, and feature engineering

Week 2: Machine Learning Models and Model Selection

Week 3: Evaluating Machine Learning Models and Implementation Strategies

Week 4: Advanced Machine Learning Techniques and Real-World Applications

About Course

Machine Learning in Practice: Real-World Applications and Implementation Strategies” is a course that provides learners with hands-on experience in implementing machine learning solutions to solve real-world problems. The course covers topics such as data preparation, feature engineering, model selection, and evaluation, and teaches learners how to implement machine learning algorithms using popular frameworks and tools such as scikit-learn and TensorFlow.

The course is designed for professionals who want to gain practical skills in machine learning and apply them in their work, including data scientists, software engineers, and business analysts. By the end of the course, learners will have a solid understanding of how to apply machine-learning techniques to solve real-world problems and will be equipped with the skills necessary to implement machine-learning solutions in their own organizations.

Some of the specific topics covered in the course include:

  • Understanding the basics of machine learning, including supervised and unsupervised learning, classification, regression, and clustering.
  • Preparing data for machine learning, including data cleaning, normalization, and feature engineering.
  • Selecting and tuning machine learning models, including decision trees, random forests, logistic regression, and neural networks.
  • Evaluating machine learning models using common metrics such as accuracy, precision, and recall.
  • Implementing machine learning models using popular tools and frameworks such as scikit-learn and TensorFlow.
  • Understanding best practices for deploying and maintaining machine learning solutions in production.

Overall, “Machine Learning in Practice: Real-World Applications and Implementation Strategies” is a practical course that equips learners with the skills and knowledge necessary to implement machine learning solutions in real-world scenarios, and provides a solid foundation for further study and exploration in the field of machine learning.

Show More

What Will You Learn?

  • Introduction to Machine Learning and Data Preparation
  • Machine Learning Models and Model Selection
  • Evaluating Machine Learning Models and Implementation Strategies
  • Advanced Machine Learning Techniques and Real-World Applications

Instructor

JG
0 /5

20 Courses

AG
4.44 /5

78 Courses

Student Ratings & Reviews

No Review Yet
No Review Yet
9,999.00 20,999.00


Share
Share Course
Page Link
Share On Social Media

Want to receive push notifications for all major on-site activities?