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Machine Learning with Python

10,000.00

( 0 Review )

Course Level

Intermediate

Total Hour

180h

Video Tutorials

17

Course content

180h

Month 1

Introduction to Machine Learning: overview, types of ML, data preparation
Python basics for data science: variables, data types, data structures, control structures, functions
Data manipulation and analysis with NumPy and Pandas
Data visualization with Matplotlib and Seaborn
Linear Regression: basics, assumptions, implementation in Python
Logistic Regression: basics, assumptions, implementation in Python

Month 2

Month 3

About Course

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression, and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project, you will demonstrate your skills by building, evaluating, and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job-ready skills to add to your resume and a certificate in machine learning to prove your competency.

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What Will You Learn?

  • Describe the various types of Machine Learning algorithms and when to use them
  • Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression
  • Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees
  • Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics

Material Includes

  • Live Classes
  • 100% Online Course
  • Hands-on Project
  • Shareable Certificate

Instructor

JG
0 /5

21 Courses

AG
4.44 /5

78 Courses

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Material Includes

  • Live Classes
  • 100% Online Course
  • Hands-on Project
  • Shareable Certificate

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