Data Science, ML and AI (R) training course

SIMPLE CODING WiTH R

Using R for simple Data Analysis
Accessing, querying and manipulating data in R
Data cleansing for accurate modelling
Reducing dimensions with Principal Component Analysis
Extending R with user–defined packages
Facilitating good analytical thinking with data visualisation
Investigating characteristics of a data set through visualisation
Charting data distributions with boxplots, histogrammes and density plots
Identifying outliers in data

Working with Unstructured Data

Mining unstructured data for business applications
Preprocessing unstructured data in preparation for deeper analysis
Describing a corpus of documents with a term–document matrix
Make predictions from textual data

Predicting Outcomes with Regression Techniques

Estimating future values with linear regression
Modelling the numeric relationship between an output variable and several input variables
Correctly interpreting coefficients of continuous data
Assess your regression models for ‘goodness of fit’

Categorising Data with Classification Techniques

Automating the labelling of new data items
Predicting target values using Decision Trees
Constructing training and test data sets for predictive model building
Dealing with issues of overfitting
Assessing model performance
Evaluating classifiers with confusion matrices
Calculating a model’s error rate

Detecting Patterns in Complex Data with Clustering and Social Network Analysis

Identifying previously unknown groupings within a data set
Segmenting the customer market with the K–Means algorithm
Defining similarity with appropriate distance measures
Constructing tree–like clusters with hierarchical clustering
Clustering text documents and tweets to aid understanding
Discovering connections with Link Analysis
Capturing important connections with Social Network Analysis
Exploring how social networks results are used in marketing

Leveraging Transaction Data to Yield Recommendations and Association Rules

Building and evaluating association rules
Capturing true customer preferences in transaction data to enhance customer experience
Calculating support, confidence and lift to distinguish "good" rules from "bad" rules
Differentiating actionable, trivial and inexplicable rules
Constructing recommendation engines
Cross–selling, up–selling and substitution as motivations
Leveraging recommendations based on collaborative filtering

Learning from Data Examples with Neural Networks

Machine learning with neural networks
Learning the weight of a neuron
Learning about how neural networks are being applied to object recognition, image segmentation, human motion and language modelling
Analysing labelled data examples to find patterns in those examples that consistently correlate with particular labels for object recognition

Implementing Analytics within Your Organisation

Expanding analytic capabilities
Breaking down Data Analytics into manageable steps
Integrating analytics into current business processes
Reviewing Hadoop, Spark, and Azure services for machine learning
Dissemination and Data Science policies
Examining ethical questions of privacy in Data Science
Disseminating results to different types of stakeholders
Visualising data to tell a story