Data Science and Machine Learning for Testers

SMART TEST ENGINEER Live: $799 | Video Only: $599

Data Science is an essential skill for analyzing and deriving useful insights from data, big and small. McKinsey estimates that by 2020, a 700,000 strong workforce of data scientists will be needed in US alone. The resulting talent gap must be filled by a new generation of data scientists.

Python has become the de facto programming language of data scientists and data analysts. It’s concise, easy to learn and data friendly, making it ideal for data analysis. We will start with a crash course on Python before getting into machine learning using Python. We will also look at Python libraries like NumPy, Pandas, and SciKit-Learn that are needed to perform machine learning in Python.

This course will take you from zero to machine learning hero in 10 days. You don’t need to know any programming language to enroll in this course.

Attendees will have enhanced their working knowledge of developing, testing, and deploying software in today’s Agile environment and collaborating with key players throughout the software delivery process.

SAMPLE COURSE OUTLINE

Day 1 - Introduction to Data Science & Machine Learning

Day 2 - Python Programming

Day 3 - Advanced Python & Overview of Python Libraries

Day 4 - Introduction to Data Analytics/Data Wrangling

Day 5 - Introduction to Machine Learning

Day 6 - Introduction to TensorFlow

Day 7 - Introduction to Keras

Day 8 - Introduction to NLP

Day 9 - Going Deeper in to Neural Networks

Day 10 - Deploying TensorFlow models to the Cloud

Data Science for Testers

Live Training $799 | Video Only: $599

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Topics Covered

Introduction to Data Science & Machine Learning
Discussing what is AI and Deep Learning
Setting up Working Environment
Python Fundamentals
Keywords, Statements Data types, Operators
If else, for loop, while loop Functions & arguments
Python programming using Modules & packages
Lists, Tuples, Strings, Sets, Dictionary
File operations, File Directory Exceptional Handling
Minimum Math needed for ML:Linear Algebra Fundamentals Matrices Scalar Vectors
Overview of Python Libraries, NumPy, Pandas, Matplotlib, SeaBorn and Scikit-learn
Introduction to Data Analytics/Data Wrangling
Extracting Exploring Processing Data with Pandas DataFrame Basics Pandas Data Structure
Introduction to Machine Learning
Types of Machine Learning & ML Workflow
Supervised Unsupervised Learning, Algorithms in Machine Language
Introduction to Training and Testing a Model
Introduction to TensorFlow, Building our first model in TensorFlow
Going Deeper in to Neural Networks
Discussing Layers, Activation Functions, BackPropagation Overfitting
Hyperparameters Batch Normalization
Visualizing Graphs in TensorBoard
Deploying TensorFlow models on Cloud
ML Workflow with SageMaker
Deploying the TensorFlow model on AWS using Docker