Course 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
Course Outline :: Machine Learning
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
Course Outline :: 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
Programme scope, delivery arrangements and suitability are confirmed during enquiry.