Machine Learning with SPSS
During the training, you will learn dozens of analytical methodologies using two IBM SPSS tools, SPSS Statistics and SPSS Modeler. These include diagnostic analysis, prediction, machine learning, cluster analysis, time series analysis, model evaluation, and deployment of the established model, applied to real business, sales, finance, healthcare, and government data. SPSS Modeler is the choice for anyone who wants to work with large volumes of data and conduct fast and accurate analytical analysis.
Training table
Machine Learning with SPSS
Machine Learning with SPSS
Maschinelles Lernen mit SPSS
Машинное обучение с SPSS
Information about the training
This training program encompasses performing diagnostic and predictive analyses using SPSS Statistics and SPSS Modeler tools, including methodologies such as Feature Selection, Chi-Square Test, ANOVA, Neural Networks, Logistic Regression, C5, XGBoost, NLP, Sentiment Analysis, Cluster Analysis, Anomaly Detection, and Deployment. Through this training, you will learn to build machine learning algorithms, test, measure, and deploy them in an applied manner. As a result, you will be able to perform the necessary analyses for your data analytics project much more quickly and accurately, which will help you achieve data-driven outcomes. This, in turn, will enhance decision-making speed and improve the quality of your work.
Who is this training for?
Graduates of the Data Analytics with SPSS training include professionals and managers from various fields such as Marketing, Finance, Sales, and Human Resources who wish to learn Data Analytics. Business Managers, Business Analysts, Data Analysts, individuals learning Data Analytics and interested in this field, anyone who wants to work with large datasets, and young professionals and students aspiring to build a career in Data Analytics will benefit from this program.
Certificate
Certificates will be presented to individuals who successfully complete the training. You can see a sample certificate on the right.
Machine Learning/Neural Networks Using SPSS
SPSS for Machine Learning
Andrew R. Timming
In this video, you will learn how to perform machine learning using SPSS. The tutorial covers the step-by-step process of building, testing, and evaluating predictive models with SPSS Modeler. By the end of the video, you'll understand how to apply machine learning techniques to real-world data sets effectively. Video Courtesy: Andrew R. Timming
Syllabus
Session 1
- Data Quality Issues
- Data Preparation - Advantages of the Clem Language
- The Essence of Palettes
- Understanding Nodes and Supernodes
- Creating/Removing New Data
Case Study 1
Data Preparation on Demographic Data
Session 2
- Diagnostic Analysis with SPSS Modeler
- Machine Learning: Regression Models
- Linear Regression Model, Auto Numeric Node
- Model Evaluation
- Root Mean Squared Error, Mean Squared Error, R-squared, Adjusted R-squared.
- Residuals, Durbin-Watson Test, Multicollinearity, VIF Value.
Case Study 2
Predictive Analysis Based on a Regression Model on Sales Data.
Session 3
- Data Integration, Nodes for Visualization
- Graph and Output Palettes, Data Quality Issues
- Machine Learning Classification Models
- CHAID, Support Vector Machines (SVM)
- Classification and Regression Trees (CART)
- Logistic Regression, Auto Classifier Node
- Ensemble Learning
- Confusion Matrix, Precision, Recall, F1 Score, ROC Curve, AUC, Gini Coefficient
Session 4
- Introduction to Unsupervised Learning
- Clustering, K Means Clustering, Two Step, Kohonen Network
- Anomaly Detection
- Auto Cluster Node
- Association Rules, Apriori, Sequence Algorithms
- Deployment
- Antecedent, Consequent, Rule Support, Antecedent Support, Lift
- CRISP-DM Reporting
Case Study 3
Building a Predictive Model by Applying Classification Algorithms to Risk Data.
Case Study 4
Cluster Analysis and Retail Basket Analysis with Transportation Data.
Trainers
Etibar Huseynli
Chief Data Scientist, QSS ANALYTICSWith more than 10 years of experience in the field of analytics, Etibar Huseynli has worked as the Head of CRM and Churn Retention at Bakcell, Research Analyst at the Center for Entrepreneurship Development and Research and Universal Machines LLC, and Lecturer in Statistics at Baku Engineering University. He has high knowledge and skills in SPSS, GRETL, SAS, R, Python, Tableau, Excel, SQL-Data Management programs, having led 5 international and more than 10 local analytics projects. He happens to be a data analytics consultant for Bakcell and a number of companies, including the International Organization for Migration (IMO) and Hyundai.