Machine Learning and Predictive Models
5 DaysMachine Learning and Predictive Models
Course Methodology
This course includes interactive discussion and the use of exercises and case studies. Each Machine Learning algorithm is supported by its own case study with step-by-step outputs that go in parallel with its multi-stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS, SAS, Statistica and Excel.
Course Objectives
By the end of the course, participants will be able to:
- Gain a clear understanding of Machine Learning concepts
- Differentiate between Data Analysis and Machine Learning methodologies
- Apply testing and validation techniques to Machine Learning models
- Present an overview of optimal analytic solutions
- Build and fine-tune predictive models for accurate estimations
Target Audience
Any level of professional interested in how Machine Learning can assist their organization, would benefit from this course. These include professionals from industries including, but not limited to, banking, insurance, retail, government, manufacturing, healthcare, telecom, and airlines.
Target Competencies
- Predictive Analysis
- Predictive Models
- Data Analysis
- Data Analytic Models
Course Outline
- Data Analysis and Simple Regression
- Fundamentals of Data Analysis Logic
- Comparing two groups: Means and proportions testing
- Visualizing group profiles in a single chart
- Analyzing multiple groups: Means and proportions testing
- Profiling multiple groups in one chart
- Introduction to Simple Regression
- Regression vs. Correlation
- Sensitivity analysis for quantitative variables
- Multiple and Logistic Regressions
- Overview of Machine Learning principles
- Understanding Gradient Descent logic
- Differences between Multiple and Simple Regression
- Variability analysis in estimations
- Utilizing dummy variables in models
- Key distinctions between Logistic and Multiple Regressions
- Simplifying complex models through Stepwise Regression
- Discriminant Analysis
- Optimized profiling techniques
- Two-Group Discriminant Function Analysis
- Case attribution and model evaluation
- Classification functions and Mahalanobis squared distances
- Probability-based methods and model reduction
- Generalized Discriminant Analysis
- Decision Trees
- Introduction to Decision Trees
- Binary Trees and their quality assessment
- Rules and techniques for pruning
- CART Models: Classification and Regression Trees
- CHAID Trees and Random Forest Trees
- Nearest Neighbor, Bayesian, Neural Network and Deep Learning
- Understanding conditional probabilities for prediction
- Prediction using probability models
- Distance-based predictions (Nearest Neighbor)
- K-Nearest Neighbors methodology
- Neural Network models: Weights, hidden layers, pros, and cons
- Introduction to Deep Learning concepts
- Overview of Big Data principles
Mawred Certificate
Mawred Certificate courses by Al mawred Training & Consulting are designed for those willing to challenge themselves and go the extra distance...
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Classroom Schedule
Virtual Schedule
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