Training with Iverson classes

Training is not a commodity – all training centres are not the same. Iverson Associates Sdn Bhd is the most established, the most reputable, and the top professional IT training provider in Malaysia. With a large pool of experienced and certified trainers, state-of-the-art facilities, and well-designed courseware, Iverson offers superior training, a more impactful learning experience and highly effective results.

At Iverson, our focus is on providing high-quality IT training to corporate customers, meeting their learning needs and helping them to achieve their training objectives. Iverson has the flexibility to provide training solutions whether for a single individual or the largest corporation in a well-paced or accelerated training programme.

Our courses continue to evolve along with the fast-changing technological advances. Our instructor-led training services are available on a public and a private (in-company) basis. Some of our courses are also available as online, on demand, and hybrid training.

This course focuses on the practical aspects of Machine Learning, Deep Learning and Artificial Intelligence. The objective is to make use of TensorFlow for various types of neural networks. The participants will build and train deep learning models.

Additional Info

  • Certification Course only
  • Course Code ML&AI
  • Price RM6800
  • Exam Price Exclude
  • Duration 5 Days
  • Principals Microsoft
  • Schedule

    13-17 Aug 2018 (Penang)

    3-7 Sep 2018 (Penang)

    1-5 Oct 2018

  • Audience

    The intended audience for this course:
    - Data Engineers
    - Data Scientists
    - Machine Learning Engineers
    - Integration Engineers
    - Architects
     

  • Prerequisities

    Participants should preferably have some hands-on experience on Python

  • Module 1 Title Understanding the Big Picture
  • Module 1 Content

    • Artificial Intelligence (AI) Overview

    • What is Machine Learning (ML)?

    • AI vs ML vs Data Science

    • Relationship between Deep Learning (DL) and Machine Learning

    • Practical Use cases

    • Concepts and Terms

    • Tools/Platforms for ML, DL and AI

    • Machine Learning Project End to End Pipeline

    • Scalable ML/AI: Big Data and Cloud fits into the Ecosystem

  • Module 2 Title GCP Introduction
  • Module 2 Content

    • GCP Introduction.

    • Why Google Cloud Platform (GCP)?

    • How Innovations at Google driving Data Engineering and Science globally?

    • Key Google Products related to Data and Machine Learning

    • Come on same page w.r.t. terms and concepts

  • Module 3 Title Getting holistic view: Architectures and Pipelines
  • Module 3 Content

    • Introduction to Serverless Architecture

    • Current Challenges with On-Premise Architectures

    • How Google enables higher productivity?

    • How Key Google Products fit in Enterprise Architecture?

    • How to design modern Data Analytics Pipeline on GCP?

  • Module 4 Title Machine Learning APIs
  • Module 4 Content

    • Introduction to Machine Learning APIs

    • Key ML Use Cases

    • Vision API

    • Natural Language API

    • Translate API

    • Speech API

  • Module 5 Title Environment for Experiments
  • Module 5 Content

    • Installing Anaconda

    • Setting up Jupyter Notebook

    • Experiencing Notebooks

    • Key Python Syntax Recap

    • Hands-on Exercises

  • Module 6 Title Key Statistics
  • Module 6 Content

    • Summary Statistics

    • Exploratory Data Analysis

    • Numerical Computation using Python

    • Hands-on Exercises

  • Module 7 Title Data Visualization
  • Module 7 Content

    • Overview

    • Using MatPlot Lib

    • Working with Seaborn

    • Key types of plots

    • Exploratory Analysis using Seaborn

    • Hands-on Exercises

  • Module 8 Title Acquiring & Preparing Data
  • Module 8 Content

    • Content Acquisition Overview

    • Working with Beautiful Soup

    • Acquiring data using Rest Based APIs

    • Data Cleaning & Wrangling using Pandas

    • Missing Values and Outlier

    • Cleansing Twitter Data

    • Performing Twitter Sentiment Analysis

    • Hands-on Exercises

  • Module 9 Title Feature Engineering
  • Module 9 Content

    • What is Feature Engineering?

    • Why Feature Engineering?

    • How to apply Feature Engineering?

    • Discussions on various scenarios

    • Hands-on Exercises

  • Module 10 Title Machine Learning using Scikit Learn
  • Module 10 Content

    • Types of Machine Learning

    • Key Algorithms in Machine Learning

    • Practical Applications of Machine Learning

    • Various frameworks/Libraries popular for ML

    • Concepts and Terms

    • Why Scikit Learn?

    • Code Walkthrough

    • Hands-on Exercises

  • Module 11 Title Supervised Machine Learning
  • Module 11 Content

    • Key Classification Algorithms

    • Naïve Bayes Classifier

    • Confusion Matrix

    • Accuracy

    • Key Regression Algorithms

    • Linear, Logistic Regressions

    • Gradient Descent

    • Loss function

    • Bias vs Variance Tradeoff

    • Evaluating Models

    • Hands-on Exercises

  • Module 12 Title Un-Supervised Machine Learning
  • Module 12 Content

    • Why Un-supervised learning is important?

    • Where it can be applied?

    • Principal Component Analysis

    • Performing Clustering of data

    • Hands-on Exercises

  • Module 13 Title Spark Introduction
  • Module 13 Content

    • Overview

    • Understand What is Spark?

    • Why Spark?

    • Languages used in Spark Programming

    • Logical Architecture

    • Physical Architecture

    • Pros & Cons of using Python, Java & Scala

    • Key Terms & Concepts

    • Ways to create RDDs

    • Operations on RDD

    • Pair RDD

    • Key Spark Components

    • Working with various types of data

    • Hands-on exercises

  • Module 14 Title Spark SQL
  • Module 14 Content

    • Introduction

    • Using the SQL API – sqlContext.sql

    • Dataframe API

    • Working with various Datasources

    • Inferred Schemas

    • Querying DataFrames Using Column Expressions

    • Data formats

    • Hands-on Exercises

  • Module 15 Title Spark Machine Learning
  • Module 15 Content

    • Quick introduction to Machine Learning

    • Introduction to ML and MLLib Spark

    • Data types - Vectors, Matrices, LabeledPoint

    • Summary Statistics

    • Calculating Correlations

    • Transformers, Estimators and Pipelines

    • Evaluation of Model

    • Walkthrough of a Regression model

    • Walkthrough of a K Means Clustering model

    • Hands-on on Working with Machine Learning Pipelines

  • Module 16 Title Introduction to Deep Learning
  • Module 16 Content

    • What is Deep Learning?

    • Relationship between Deep Learning and Machine Learning

    • Deep Learning Use cases

    • Concepts and Terms

    • How to implement Deep Learning?

    • Various Libraries, Pros & Cons

    • Hands-on: Recap on Machine Learning

  • Module 17 Title Artificial Neural Network
  • Module 17 Content

    • Introduction to Neural Networks

    • Introduction to Perceptron

    • Neural Network Activation Functions

    • Basic Neural Nets

    • Concepts

  • Module 18 Title TensorFlow API
  • Module 18 Content

    • What is TensorFlow?

    • Installing TensorFlow

    • TensorFlow Graphs

    • Variables and Placeholders

    • Activation Functions

    • Hands-on exercises

  • Module 19 Title Convolutional Neural Networks (CNN)
  • Module 19 Content

    • CNN History

    • Understanding CNNs

    • CNN Application

    • Hands-on exercises

  • Module 20 Title Interactive Data exploration using DataLab
  • Module 20 Content

    • Spinning up Cloud DataLab

    • Experiencing Datalab notebooks

    • Exploring Google Cloud Storage

    • Leveraging relational data with Google Cloud SQL

    • Reading and writing streaming Data with Google BigTable

    • Querying Data from Google BigQuery

    • Making Google API Calls from notebooks

  • Module 21 Title CloudML: Scalable Models on GCP
  • Module 21 Content

    • Why Cloud ML?

    • Running TensorFlow model in Local mode

    • Porting TensorFlow models to GCP

    • Deploying Models in Production

    • Model Predictions

    • Hands-on exercise(s)

  • Module 22 Title Conversational AI
  • Module 22 Content

    • Intro to ChatBots

    • Key options available

    • Building ChatBot

    • Hands-on Exercise using DialogFlow API

RM6,800.00
* Training Dates:

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