£1,499 – £1,999

Data Science-Machine Learning-AI Bootcamp (Advanced), FREE Cloud Service($1...

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a limited time we’re offering a promo code for IBM Cloud services with this bootcamp (value of over US$1,000)! Practice what you learn and build apps that use databases, leverage machine learning, and use IBM Watson services (to see, talk or chat) with IBM Cloud!


Data Science Bootcamp (Intensive In-Class Hands-On training)

Bootcamp length: 4 days (Aug 28, 2018 - Aug 31, 2018)

Participants must supply their own laptops.

Introduction

Welcome to the four-day Cognitive Class Data Science Bootcamp! This is an intensive hands-on bootcamp, where you can learn the advanced topics of data science from data scientists at IBM. You will learn machine learning, AI, Deep Learning, big data, and Apache Spark. You'll have a chance to apply your new skills to on data science projects.

What you will achieve at the end of the bootcamp:

An understanding of data analysis, machine learning, Apache Spark, and deep learning techniques. Also libraries such as Pandas, Sickitlearn, and TensorFlow. All the materials are delivered through hands-on sessions.

Prerequisite course requirements:

You are either already comfortable programming in Python, or you have successfully completed the following online course: Python 101 (https://cognitiveclass.ai/courses/introduction-to-python/)

This free Python course provides a beginner-friendly introduction to Python. Practice through lab exercises, and you'll be ready to start data analysis in the bootcamp!

Agenda

Day 1: Data Analysis with Python. In first day, you learn about the importance of data, machine learning, and big data. Find out about free online resource, labs and tools for data science education which includes Jupyter (IPython) Notebooks, RStudio IDE, and Apache Spark. 
Learn how to analyze data using Python. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

Topics covered:

  • Learn about data science in a business context
  • Discover some business applications and use cases for data science
  • Importing and cleaning Data sets 

  • Pandas, Numpy and Scipy libraries
  • Data frame manipulation 

  • Histograms and Probability Mass functions Notebook: Calculate and Display data 

  • matplotlib and Plotly library
  • Maps (creating maps using latitude, longitude data)
  • Hands-on Project



Day 2: Machine Learning with Python. 
How can we get machines to learn from the data on their own? In this part you will learn get an overview of machine learning algorithms. To get hands-on practice with machine learning, you will work with real data sets and practice data mining techniques to predict or classify different datasets. Also, you will learn how to choose the best algorithm for different problems in various domains and industries.

Topic covered:

  • Overview of Machine Learning
  • Which ML algorithm is proper for my problem? 

  • Regression
  • Classification (Decision trees and KNN)
  • Clustering (Hierarchical and k-means)
  • Recommender Systems
  • Machine learning libraries, e.g ScikitLearn


Day 3: Big Data with Python. You will learn how to work with Big Data using Apache Spark. Spark is a lightweight front-end library that is used for distributed processing when dealing with big data. You will read data from a big dataset, preprocess and apply preprocessing operations.

Topics covered:

  • Intro to Apache Spark
  • Reading data from a big dataset
  • Selecting data, filtering, and aggregating big data
  • Spark SQL
  • Machine Learning with Spark


Day 4 Morning: Intro to Deep leaning with TensorFlow
. Deep learning is a subset of machine learning that uses neural networks to model high-level abstractions in data, which enables data scientists to create models on complex, unstructured data like images and videos. In this session you will work with specific type of deep learning, called convolutional neural networks, and use TensorFlow library to work with these networks.

Topics covered:

  • Deep Learning libraries
  • Intro to TensorFlow

  • Neural Networks
  • Logistic Regression with TensorFlow
  • Convolutional Neural Networks
  • Recurrent Neural Networks


Day 4 Afternoon: Final exam (optional).

2 hours for optional exam. Participants with a passing grade will receive:

  • an IBM course completion certificate
  • an IBM badge

Both the completion certificate and the badge will be stored and verifiable by Documentorum, an academic credentials blockchain.

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