Duration

Machine Learning & Artificial Intelligence

5 days

Outline

Understanding the Big Picture

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

GCP Introduction

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

Getting holistic view: Architectures and Pipelines

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?

Machine Learning APIs

Introduction to Machine Learning APIs
Key ML Use Cases
Vision API
Natural Language API
Translate API
Speech API

Environment for Experiments

Installing Anaconda
Setting up Jupyter Notebook
Experiencing Notebooks
Key Python Syntax Recap

Key Statistics

Summary Statistics
Exploratory Data Analysis
Numerical Computation using Python

Feature Engineering

What is Feature Engineering?
Why Feature Engineering?
How to apply Feature Engineering?
Discussions on various scenarios

Machine Learning using Scikit Learn

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?

Supervised Machine Learning

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

Un-Supervised Machine Learning

Why Un-supervised learning is important?
Where it can be applied?
Principal Component Analysis
Performing Clustering of data

Spark Introduction

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

Spark SQL

Introduction

Using the SQL API – sqlContext.sql
Dataframe API
Working with various Datasources
Inferred Schemas
Querying DataFrames Using Column Expressions
Data formats

Spark Machine Learning

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

Data Visualization

Overview

Using MatPlot Lib
Working with Seaborn
Key types of plots
Exploratory Analysis using Seaborn

Acquiring & Preparing Data

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

Introduction to Deep Learning

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

Artificial Neural Network

Introduction to Neural Networks
Introduction to Perceptron
Neural Network Activation Functions
Basic Neural Nets
Concepts

TensorFlow API

What is TensorFlow?
Installing TensorFlow
TensorFlow Graphs
Variables and Placeholders
Activation Functions

Convolutional Neural Networks (CNN)

CNN History
Understanding CNNs
CNN Application

Interactive Data exploration using DataLab

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

CloudML: Scalable Models on GCP

Why Cloud ML?
Running TensorFlow model in Local mode
Porting TensorFlow models to GCP
Deploying Models in Production
Model Predictions

Conversational AI

Intro to ChatBots
Key options available
Building ChatBot
Hands-on Exercise using DialogFlow AP

Programme scope, delivery arrangements and suitability are confirmed during enquiry.