Course Report
Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.
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Course Features
Duration
3 hours
Delivery Method
Online
Available on
Lifetime Access
Accessibility
Desktop, Laptop
Language
English
Subtitles
English
Level
Intermediate
Teaching Type
Self Paced
Video Content
3 hours
Course Description
Highlights
Career Impact
Top 5 Percentile
Course Credibility
Top 20 Percentile
Pedagogy
Top 20 Percentile
Parameters
Course Credibility
Delivered through Guvi a renowned institution in the field, this course offers a comprehensive learning experience.
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Pedagogy
This comprehensive course equips you with all major Machine Learning skills applicable to your daily life. Personalized teaching ensures one-on-one doubt resolution with faculty, maximizing skill acquisition. These practical skills empower you to confidently apply your knowledge and thrive in various real-life situations. An exceptional course in Machine Learning, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.
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Career Impact
This course is exceptional, ranking among the top 5 percentile in Machine Learning for its significant career impact and excellent job assistance. Learners benefit from valuable career opportunities and support, enabling them to secure relevant positions and excel in the industry. The course's dual focus on career impact and job assistance enhances its value, making it an ideal choice for individuals seeking to advance their careers and succeed in the Machine Learning field.
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Course Overview
Job Assistance
Personlized Teaching
Hands-On Training,Industry Exposure
Case Studies, Captstone Projects
Skills You Will Gain
What You Will Learn
Installation of Anaconda & Jupyter Notebook IDE.
Learn how to load data into Scikit-learn
Run various ML algorithms for supervised/unsupervised learning.
How to build Classification & Regression Models.
Building the Decision Tree Lab.
How to spin up & tweak SVM for classification models.
Overfitting, Random Forest & Teamwork
Target Students
Students with experience in Python Programming aiming to build predictive models in Scikit-Learn Library.
Experienced professionals who are working with MATLAB/R/SAS, looking to transition their career in Machine Learning/Data Science.
Course Instructors
Team GUVI
Author