15 Industry level machine learning projects 2022

15 Industry level machine learning projects 2022

15 Industry level machine learning projects 2022

Work on interesting industry level projects which can help you in interviews as well as adding strength in your resume.

Language: english

Note: 5.0/5 (2 notes) 919 students  New course 

Instructor(s): Neural class

Last update: 2022-04-19

What you’ll learn

  • Learn about data science projects and its various libraries using python
  • Learners will be working on real life projects.
  • These projects can add great value in user’s resume and college project.
  • Learn about how to deploy a machine learning model.
  • Learn about supervised and unsupervised learning

 

Requirements

  • Basic python syntax

 

15 Industry level machine learning projects 202215 Industry level machine learning projects 2022

Description

ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT  INDUSTRY LEVEL MACHINE LEARNING PROJECTS

  • Do you want to harness the power of machine learning?

  • Are you looking to gain an edge by adding cool projects in your resume?

  • Do you want to learn how to deploy a machine learning model?

Gaining proficiency in machine learning can help you harness the power of the freely available data and information on the world wide web and turn it into actionable insights

This course is based on15 real life machine learning projects– You will work on 15 interesting projects which are used in machine learning industry.

My course provides a foundation to carry out real life machine learning projects. By taking this course, you are taking an important step forward in your data science journey to become an expert in harnessing the power of real projects.


Inside the course, you’ll learn how to:

  • Gain complete machine learning tool sets to tackle most real world problems

  • Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.

  • Combine multiple models with by bagging, boosting or stacking

  • Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data

  • Develop in Jupyter (IPython) notebook, Spyder and various IDE

  • Communicate visually and effectively with Matplotlib and Seaborn

  • Engineer new features to improve algorithm predictions

  • Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data

  • Use SVM for handwriting recognition, and classification problems in general

  • Use decision trees to predict staff attrition

  • Apply the association rule to retail shopping datasets

  • And much much more!

No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area.

Make This Investment in Yourself

If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!

Take this course and become a machine learning engineer!

In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!

ENROLL NOW 🙂

 

15 Industry level machine learning projects 2022

Excel Skills for Business [Coursera]

Who this course is for

  • Those who wants to learn data science
  • Those who wants to get exposure with industry based projects
  • Those who wants to build amazing projects and make their resume shine during interviews.
  • Those who wants an edge over other applicants in interview.

 

Course content

  • Project 1
    • .
  • Project 2
    • .
  • Project 3
    • .
  • Project 4
    • .
  • Project 5
    • .
  • Project 6
    • .
  • Project 7
    • .
  • Project 8
    • .
  • Project 9
    • .
  • Project 10
    • .
  • Project 11
    • .
  • Project 12
    • .
  • Project 13
    • .
  • Project 14
    • .
  • Project 15
    • .

 


 

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