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Machine Learning Essentials with Python (TTML5506-P)

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Course schedule:
psinghal
Last Update October 1, 2024
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About This Course

Learn how Artificial Intelligence is being applied in modern business.

This foundation-level hands-on course explores core skills and concepts in machine learning practices. You’ll learn machine learning concepts and algorithms from scratch. This includes the foundations, applicability and limitations, and an exploration of implementation and use.

Learning Objectives

Popular machine learning algorithms, their applicability and limitations
Practical application of these methods in a machine learning environment
Practical algorithm use cases and limitations

Inclusions

  • Instructor-led training
  • Training Seminar Student Handbook
  • Collaboration with classmates (not currently available for self-paced course)
  • Real-world learning activities and scenarios
  • Exam scheduling support*
  • Enjoy job placement assistance for the first 12 months after course completion.
  • This course is eligible for CCS Learning Academy’s Learn and Earn Program: get a tuition fee refund of up to 50% if you are placed in a job through CCS Global Tech’s Placement Division*
  • Government and Private pricing available.*

Pre-requisites

  • Basic Python skills
  • Good foundational mathematics in linear algebra and probability
  • Basic Linux skills
  • Familiarity with command line options such as ls, cd, cp, and su

Target Audience

  • Experienced Developers, Data Analysts, and others interested in learning about machine learning algorithms and core concepts leveraging Python.
  • This course is also offered in R or Scala – please inquire for details.

Curriculum

43 Lessons24h

1. Machine Learning (ML) Overview

Machine Learning landscape
Machine Learning applications
Understanding ML algorithms and models (supervised and unsupervised)

2. Machine Learning Environment

3. Machine Learning Concepts

4. Feature Engineering (FE)

5. Logistic Regression

6. Classification: SVM (Supervised Vector Machines)

7. Classification: Decision Trees and Random Forests

8. Classification: Naive Bayes

9. Clustering (K-Means)

10. Principal Component Analysis (PCA)

11. Recommendation (collaborative filtering)

12. Time Permitting: Capstone Project

Your Instructors

psinghal

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$1,995.00

Level
Intermediate
Duration 24 hours
Lectures
43 lectures

Inclusions

  • Instructor-led training
  • Training Seminar Student Handbook
  • Collaboration with classmates (not currently available for self-paced course)
  • Real-world learning activities and scenarios
  • Exam scheduling support*
  • Enjoy job placement assistance for the first 12 months after course completion.
  • This course is eligible for CCS Learning Academy’s Learn and Earn Program: get a tuition fee refund of up to 50% if you are placed in a job through CCS Global Tech’s Placement Division*
  • Government and Private pricing available.*
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