CALL US
+62 8561110558
LOCATION
CoHive 101 Mega Kuningan, Jl. DR. Ide Anak Agung Gde Agung No.1, RT.5/2, Setiabudi, Jakarta Selatan 12950

Launching into Machine Learning

Launching into Machine Learning

 

This course is delivered in six modules. The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We then discuss how to set up a supervised learning problem, how to optimize a machine learning (ML) model, and how generalization and sampling can help assess the quality of ML models.

What you will learn:

  • Describe how to improve data quality.
  • Perform exploratory data analysis.
  • Build and train supervised learning models.
  • Optimize and evaluate models using loss functions and performance metrics.
  • Mitigate common problems that arise in machine learning.
  • Create repeatable and scalable training, evaluation, and test datasets.

Who this course is for?

This class is primarily intended for the following participants:
  • Aspiring machine learning data scientists and engineers
  • Machine learning scientists, data scientists, and data analysts
  • Data engineers

 

Prerequisite

To get the most out of this course, participants should have:
  • Some familiarity with basic machine learning concepts
  • Basic proficiency with a scripting language: Python preferred

 

Course Outline

  • Module 1: Introduction to Course
    • Summarize course scope
    • Get started with Google Cloud and Qwiklabs
  • Module 2: Improve Data Quality and Exploratory Data Analysis
    • Understand best practices for improving data quality
    • Perform exploratory data analysis
  • Module 3: Practical Machine Learning
    • Differentiate between the major categories of ML problems
    • Place major ML methods in the context of their historical development
    • Build and train supervised learning models
  • Module 4: Optimization
    • Quantify model performance using loss functions
    • Use loss functions as the basis for an algorithm called gradient descent
    • Optimize gradient descent to be as efficient as possible
    • Use performance metrics to make business decisions
  • Module 5: Generalization and Sampling
    • Assess whether your model is overfitting
    • Gauge when to stop model training
    • Create repeatable training, evaluation, and test datasets
    • Establish performance benchmarks
  • Module 6: Course Summary
    • Summarize Course

Jadwal Training

Tanggal Pukul Biaya (per pax; belum termasuk VAT 10%) Trainer Venue Daftar
TBA TBA Rp 14 juta Satria Yuda Utama Online TBA
TBA TBA Rp 14 juta Satria Yuda Utama Online TBA
TBA TBA Rp 14 juta Satria Yuda Utama Online TBA
TBA TBA Rp 14 juta Satria Yuda Utama Online TBA
TBA TBA Rp 14 juta Satria Yuda Utama Online TBA
 

Punya pertanyaan lebih lanjut tentang kursus ini?

Hubungi kami disini.

 

As a Google Cloud™* Managed Service Provider,  Cloud Ace has been providing one-stop services such as cloud implementation support, operational design, and post-implementation system maintenance to meet the needs of our customers.

Contacts

08561110558 
sales@id.cloud-ace.com
021-3950-6875

Pages

Copyright © 2021 Cloud Ace, Inc. All rights reserved.

has been added to the cart. View Cart