We've talked about, speculated and often seen different applications for Artificial Intelligence - But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd?

ChatBots are here, and they came change and shape-shift how we've been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement.

If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you!

Who this course is for:
  • Any students in college who want to start a career in Data Science
  • Any Data Science enthusiast
  • Anyone interested in creating their own ChatBot
  • Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning and its applications

Course Curriculum

  • 1

    Welcome to the course!

    • Get Excited!

    • Applications
  • 2

    Deep NLP Intuition

    • What You'll Need For This Module

    • Plan of Attack

    • Types of Natural Language Processing

    • Classical vs Deep Learning Models

    • End-to-end Deep Learning Models

    • Bag-of-words model

    • Seq2Seq Architecture (Part 1)

    • Seq2Seq Architecture (Part 2)

    • Seq2Seq Training

    • Beam Search Decoding

    • Attention Mechanisms (Part 1)

    • Attention Mechanisms (Part 2)
  • 3

    Building a ChatBot with Deep NLP

    • ChatBot - Step 1

    • ChatBot - Step 2

    • ChatBot - Step 3
  • 4

    ---------- PART 1 - DATA PREPROCESSING ----------

    • Welcome to Part 1 - Data Preprocessing

    • ChatBot - Step 4

    • ChatBot - Step 5

    • ChatBot - Step 6

    • ChatBot - Step 7

    • ChatBot - Step 8

    • ChatBot - Step 9

    • ChatBot - Step 10

    • ChatBot - Step 11

    • ChatBot - Step 12

    • ChatBot - Step 13

    • ChatBot - Step 14

    • ChatBot - Step 15

    • ChatBot - Step 16

    • ChatBot - Step 17

    • Part 1 Checkpoint!
  • 5

    ---------- PART 2 - BUILDING THE SEQ2SEQ MODEL ----------

    • What You'll Need For This Module

    • Welcome to Part 2 - Building the Seq2Seq Model

    • ChatBot - Step 18

    • ChatBot - Step 19

    • ChatBot - Step 20

    • ChatBot - Step 21

    • ChatBot - Step 22

    • ChatBot - Step 23

    • ChatBot - Step 24

    • Part 2 Checkpoint!
  • 6

    ---------- PART 3 - TRAINING THE SEQ2SEQ MODEL ----------

    • What You'll Need For This Module

    • Welcome to Part 3 - Training the Seq2Seq Model

    • ChatBot - Step 25

    • ChatBot - Step 26

    • ChatBot - Step 27

    • ChatBot - Step 28

    • ChatBot - Step 29

    • ChatBot - Step 30

    • ChatBot - Step 31

    • ChatBot - Step 32

    • ChatBot - Step 33

    • ChatBot - Step 34

    • ChatBot - Step 35

    • ChatBot - Step 36

    • Part 3 Checkpoint!
  • 7

    ---------- PART 4 - TESTING THE SEQ2SEQ MODEL ----------

    • What You'll Need For This Module

    • Welcome to Part 4 - Testing the Seq2Seq Model

    • ChatBot - Step 37

    • ChatBot - Step 38

    • ChatBot - Step 39

    • ChatBot - Step 40

    • Part 4 Checkpoint!
  • 8

    ---------- PART 5 - IMPROVING & TUNING THE SEQ2SEQ MODEL ----------

    • ChatBot - Step 41: Improving & Tuning the ChatBot

    • ChatBot - Step 42: Introduction to a new model & setup

    • ChatBot - Step 42: Introduction to a new model & setup (Resource Download)

    • ChatBot - Step 43: Chatbot model discussion

    • ChatBot - Step 44: Tensorboard

    • ChatBot - Step 45: Run the new chatbot model

    • ChatBot - Step 45: Run the new chatbot model (Resource Download)
  • 9

    Other ChatBot Implementations

    • What You'll Need For This Module

    • The Best ChatBot

    • The Best ChatBot (Download)

    • A ChatBot Implementation in TensorFlow 1.4

    • A ChatBot Implementation in PyTorch
  • 10

    Annex 1: Artificial Neural Networks

    • Plan of Attack

    • The Neuron

    • The Activation Function

    • How do Neural Networks work?

    • How do Neural Networks learn?

    • Gradient Descent

    • Stochastic Gradient Descent

    • Backpropagation
  • 11

    Annex 2: Recurrent Neural Networks

    • Plan of Attack

    • What are Recurrent Neural Networks?

    • Vanishing Gradient Problems for RNNs

    • Long Short Term Memory

    • Practical Intuition

    • Long Short Term Memory Variations

About the instructor

IT Manager

Eric Chu

A proven and experienced IT Manager, customer-focused and equipped with a technical background. I have over 8 years’ experience in IT Service Delivery Management and leading small to medium crossfunctional teams, and over 15 years of experience in IT Training and Sales.Through my passion for technology, science, and innovation, I have fostered extensive practical knowledge of emerging technologies, complex networks, and data centre eco-systems.I have had the opportunity to work across the Asia Pacific region as an experienced Business Development Manager and IT Project Manager delivering end to end solutions.

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