Öppna kurser

Introduction to Data Science, Machine Learning & AI Training

If you want to become a data scientist, this is the training to begin with. Using open source tools, it covers all the concepts necessary to move through the entire data science pipeline, and whether you intend to continue working with open source tools, or later opt for proprietary services, it will give you the foundation you need to assess which options best suit your needs.

Key features:

  • Choose from blended on-demand and instructor-led learning options
  • Exclusive LinkedIn group membership for peer and SME community support
  • After-course instructor coaching benefit
  • Learning Tree end-of-course exam included
  • After-course computing sandbox included

You Will Learn How To:

  • Translate business questions into Machine Learning problems to understand what your data is telling you
  • Explore and analyse data from the Web, Word Documents, Email, Twitter feeds, NoSQL stores, Relational Databases and more, for patterns and trends relevant to your business
  • Build Decision Tree, Logistic Regression and Naïve Bayes classifiers to make predictions about your customers’ future behaviours as well as other business critical events
  • Use K-Means and Hierarchical Clustering algorithms to more effectively segment your customer market or to discover outliers in your data
  • Discover hidden customer behaviours from Association Rules and Build Recommendation Engines based on behavioral patterns
  • Use biologically-inspired Neural Networks to learn from observational data as humans do
  • Investigate relationships and flows between people, computers and other connected entities using Social Network Analysis


Introduction to R

Exploratory Data Analysis with R
  • Loading, querying and manipulating data in R
  • Cleaning raw data for 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, histograms 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