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What you'll get

  • Job Credibility
  • Certification Valid for Life
  • On-demand video*
  • E-Book
  • Self-Paced Learning
  • Certificate of Completion

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

Do you also want to learn about Python Machine Learning Projects? This is the machine learning course that you need to look for. The course contains several machine learning fundamentals and practical algorithms that you will discover in this course. This Python ML course with the project will help you to get professionally ready to work on a project. The duration of this python machine learning course is 6 months, and when you complete the course successfully. Then, you will get a government-approved certificate that you can add on your resume to get high-paying jobs in the company. Short modules are covering each topic of python and its machine learning concepts with real work examples.

Why should you join this python machine learning course with the project? 

There are several reasons to take the course, and one of the top reasons is it is one of the most demanding skills, and candidates get paid well.

  • The course helps you to get extensive information about machine learning concepts.
  • It also guides you about real work projects and teaches you to work on them using supervised and unsupervised ML algorithms.
  • In this course, you also learn about building chatbots with a detailed and practical approach.
  • You also learn about the IPO Market, creating custom newsfeed, and much more.


Course Content

Total: 48 lectures
  • Introduction
  • Machine Learning Applications - Part 1
  • Machine Learning Applications - Part 2
  • Why Machine Learning is Trending Now
  • Supervised Learning
  • Unsupervised Learning and Reinforcement Learning
  • Features
  • Features Practice with Python
  • Regression
  • Regression Practice with Python
  • Classification
  • Classification Practice with Python
  • Clustering
  • Clustering Practice with Python
  • Handling Image Data
  • Handling Video and Audio Data
  • Handling Text Data and One Hot Encoding
  • One Hot Encoding
  • Data Standardization
  • Machine Learning Model 1
  • Machine Learning Model 2
  • Machine Learning Model 3
  • Training Process, Error, Cost, and Loss
  • Optimization
  • Linear Regression from Scratch - Part 1
  • Linear Regression from Scratch - Part 2
  • Minimum-to-Mean Distance Classifier from Scratch - Part 1
  • Minimum-to-Mean Distance Classifier from Scratch - Part 2
  • K-Means Clustering from Scratch - Part 1
  • K-Means Clustering from Scratch - Part 2
  • Overfitting Introduction
  • An Overfitting Example on Python
  • Regularization
  • Generalization
  • Data Snooping and the Test Set
  • Cross-Validation
  • The Accuracy
  • The Confusion Matrix
  • The Curse of Dimensionality
  • The Principal Component Analysis (PCA)
  • Introduction to Deep Neural Networks (DNN)
  • Introduction to Convolutional Neural Networks (CNN)
  • Introduction to Recurrent Neural Networks (RNN)
  • Principal Component Analysis (PCA) with Python
  • Pipeline in Scikit-Learn for Machine Learning Project
  • Cross-Validation with Python
  • Face Recognition Project with Python
  • Mathematics Wrap-Up

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