PARTICIPATE IN THE 4TH INDUSTRIAL REVOLUTION: MACHINE LEARNING AND BIG DATA IS ONE OF THE REVOLUTIONARY TECHNOLOGIES THAT WILL HERALD THE 4TH INDUSTRIAL REVOLUTION.

EARN MINIMUM £75K A YEAR WITH YOUR BIG DATA AND MACHINE LEARNING SKILLS!!!. 

EXCELLENT INTERACTIVE SELF-TEACH TRAINING COURSE WITH REAL-LIFE BASED SCENARIOS. PACKED WITH TASKS, ACTIVITIES AND EXERCISES

PERFECT TRAINING COURSE FOR THOSE NEW TO MACHINE LEARNING, ARTIFICIAL INTELLIGENCE  AND BIG DATA CONCEPTS AND TECHNOLOGIES.

GOOD FOR ALL DATA SCIENCE AND MACHINE LEARNING PLATFORMS AND ENVIRONMENTS.

MACHINE LEARNING WITH BIG DATA SELF-STUDY CBT

How This Course Is Organised

This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems. 

Learn machine learning with experts from WebLearning Publishing partnering with Oracle, IBM and Microsoft to help develop your career as a data scientist.

With the machine learning course, you will learn how to build and derive insights from machine learning models. You will learn key concepts in data acquisition, preparation, exploration and visualization along with examples on how to build a data science solution using Opensource Tools.

The WebLearning hands-on series reduces the time spent investigating what steps are required to perform a task. Because the step-by-step solutions are built for practical real world situations, not only is knowledge gained through valuable hands-on experience, but also the solutions presented may then be used as the foundation for production implementation, dramatically reducing time to deployment.

Audience:
Business and Data Analysts. Consultants
Data and database professionals, DBAs
Database and BI Developers
Programmers, power users,Predictive modellers, Forecasters
Data Science and big data developers

Prerequisites:
To complete this course successfully, you need a basic knowledge of mathematics, including linear algebra. Additionally, some programming experience, ideally in either R or Python, is assumed.

Objectives:

At the end of the course, you will be able to:

Explain what machine learning is
Describe the main categories of machine learning techniques
Identify the steps in the machine learning process
Describe what CRISP-DM is
Describe Data Exploration and Preparation
Describe classification and how classification can be supervised or unsupervised
Explain and Discuss Evaluation of Machine Learning Models
Explain Regression, Cluster Analysis, and Association Analysis
Define what the regression task is
Explain the difference between regression and classification
Construct models that learn from data using widely available open source tools
Analyze big data problems using scalable machine learning algorithms on Spark

Course Outline

Introduction to Machine Learning with Big Data
Machine Learning Overview
Categories Of Machine Learning Techniques
Machine Learning in Everyday Life
Machine Learning Process
Goals and Activities in the Machine Learning Process
CRISP-DM
Scaling Up Machine Learning Algorithms
Tools Used in the Course

Introduction to Open-Source tools
Introduction to the KNIME Platform
Introduction to R, Python and Apache Spark for Data Science
Introduction to RStudio

Installing KNIME
Installing KNIME Analytics Platform 
The KNIME Workbench

Introduction to Big Data
Evolution of Big Data
Big Data Applications: 
Big Data Use Case in Industry
Sources of Big Data
Structured vs. Unstructured Data
Integrating Diverse Data

Big Data Characteristics and Applications
Characteristics of Big Data
Big Data and Dimensions of Scalability
Big Data Volume

Data Exploration
Data Terminology
Data Exploration through Summary Statistics
Data Exploration through Plots
Exploring Data with KNIME Plots / R
Data Exploration in Spark

Data Preparation
Data Quality
Quality Issues with Real Data
Addressing Data Quality Issues
Feature Selection
Feature Transformation
Dimensionality Reduction
Domain Knowledge in Data Preparation
Handling Missing Values in KNIME / R

Classification
Introduction to Classification
Building and Applying a Classification Model
Classification Algorithms
k-Nearest Neighbors
Decision Trees
Naïve Bayes
Classification using Decision Tree in KNIME / R

Evaluation of Machine Learning Models
Generalization and Overfitting
Overfitting in Decision Trees
Using a Validation Set
Metrics to Evaluate Model Performance
Confusion Matrix
Model Interpretability vs. Accuracy
Evaluation of Decision Tree in KNIME / R

Regression, Cluster Analysis, and Association Analysis
Regression Overview
Linear Regression
Cluster Analysis
k-Means Clustering
Clustering Applications
Association Analysis
Association Analysis in Detail
Applications of Association Analysis