Analytics encompasses many skills and disciplines. Identifying the problem, choosing the modeling approach, selecting the correct features to model, and evaluating the result are at the heart of analytics. The tendency, however, is to focus primarily on the technology rather than the process.
Join us for a problem-focused, applied experience where you learn to apply the analytics process to produce meaningful and valuable insights.
Business analysts, data analysts, and data scientists who need to frame analytic problems and choose the most effective ways to solve those problems; business and technical managers who need to understand the nature of analytics and data science work; BI and analytics developers who work with data scientists; anyone who aspires to become a data analyst, business analyst, or data scientist.
Module 1
What is Machine Learning?
• Why Machine Learning?
• Machine Learning
• Statistics in Machine Learning
• Supervised and Unsupervised Learning
Module 2
The Machine Learning Process
• Machine Learning Framework
• Machine Learning Approaches
• Machine Learning Techniques
• Machine Learning Algorithms
• Machine Learning Process
• CRISP-DM
Module 3
Exploratory Data Analysis
• Exploratory Data Analysis (EDA)
• Sampling
• Data Profiling
• Descriptive Statistics
• Data Relationships
• Outliers and Anomalies
• Important Variables
• Output and Interpretation
• Feature Selection Methods
Module 4
Models and Algorithms
• The Anatomy of a Model
• Classification
o Decision Trees
o Nearest Neighbor
o Probability – Bayes Classification
o Neural Networks
• Statistical Methods
• Clustering
• Association
• Anomaly Detection
• Application of Machine Learning Models
Module 5
Model Validation Techniques
• The Validation Process
• Fitting a Model
• Bias/Variance Tradeoff
• Validation Techniques
o Confidence and Prediction Intervals
o Statistical Significance
o Classification Accuracy
o Prediction Error Methods
o Hold-out
o Cross Validation
Join our public courses in our Russia facilities. Private class trainings will be organized at the location of your preference, according to your schedule.