DESCRIPTION

Features & Benefits

• 8 sessions, each session of 2 hours spread over 4 weeks
• 16 hours of LIVE Instruction spread over 4 weeks
• Training material with lab exercises provided
• Each session is recorded and recordings are provided to students over Microsoft Cloud

Course dates:

February 24 - Marchl 18, 2018

Weekly Schedule

• Saturday and Sunday, every week
• 8:00 AM - 10:00 AM (US Pacific Standard Time) each day

Please confirm your local time

Video Conference Link

Will be sent upon registration and payment

Training Provider:

Omni212

Omni212 IT Training

https://www.omni212.com/services/training/

INDAS.ID - Course Overview

This Data Science course will help you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes using R. You'll learn the concepts of Statistics, Time Series, Text Mining and an introduction to Deep Learning. You'll solve real life case studies on Media, Healthcare, Social Media, Aviation, HR.

About this course

In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Machine Learning platform, or with R, and Python on Azure stack.

What you will learn in this course?

• Explore the data science process
• Probability and statistics in data science
• Data exploration and visualization
• Data ingestion, cleansing, and transformation
• Introduction to machine learning
• The hands-on elements of this course leverage a combination of R, Python, and Machine Learning

What are the prerequisites?

• Python programming knowledge
• Basic machine learning knowledge (especially supervised learning)
• Basic statistics knowledge (mean, variance, standard deviation, etc.)
• Linear algebra (vectors, matrices, etc.)
• Calculus (differentiation, integration, partial derivatives, etc.)

Course Outline

• Data Preprocessing
• Linear And Logistic Regression Models.
• Decision Trees and Random Forest.
• Naive Bayes and Support Vector Machine.
• K-means and Hierarchical Clustering.
• Natural Language Processing.
• Artificial Neural Networks.
• Convolutional Neural Network.

Refund Policy

1. There are no refunds.
2. If for any reason the course has not been taken, class is cancelled or rescheduled, the payment can be applied towards any future course by Omni212.