Course Outline
Foundational Concepts in Machine Learning
- Classifying machine learning approaches: Supervised and Unsupervised Methods
- Historical transition from Statistical Learning to Machine Learning
- Operational Workflow: Business Requirement Analysis, Data Preparation, Model Development, and Deployment
- Selecting appropriate algorithms based on specific analytical objectives
- Mitigating Overfitting and Balancing the Bias-Variance Tradeoff
Python Environment and Machine Learning Ecosystem
- Rationale for Utilizing Programming Languages in Machine Learning Workflows
- Comparative Assessment of R versus Python for Analytical Tasks
- Foundational Python Programming and Utilization of Jupyter Notebooks
- Core Python Libraries: pandas, NumPy, scikit-learn, matplotlib, and seaborn
Algorithm Validation and Performance Assessment
- Assessing Generalization Capabilities, Overfitting Risks, and Model Validation Strategies
- Validation Methodologies: Holdout Testing, Cross-Validation, and Bootstrapping
- Regression Evaluation Metrics: Mean Error, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error
- Classification Evaluation Metrics: Accuracy, Confusion Matrices, and Handling Imbalanced Datasets
- Visualizing Model Performance: Profit Curves, ROC Curves, and Lift Curves
- Optimizing Model Selection and Parameter Tuning via Grid Search
Data Engineering and Preparation
- Importing and Managing Data Structures in Python
- Conducting Exploratory Data Analysis and Generating Summary Statistics
- Strategies for Addressing Missing Values and Anomalous Outliers
- Data Standardization, Normalization, and Feature Transformation Techniques
- Recoding Categorical Variables and Data Manipulation using pandas
Classification Methodologies
- Distinguishing between Binary and Multiclass Classification Tasks
- Logistic Regression and Linear Discriminant Analysis
- Probabilistic and Distance-Based Methods: Naïve Bayes and k-Nearest Neighbors
- Tree-Based Models: CART, Random Forests, Bagging, Boosting, and XGBoost
- Support Vector Machines and Kernel Functions
- Integrated Ensemble Learning Techniques
Regression Analysis and Continuous Prediction
- Ordinary Least Squares Regression and Feature Selection Strategies
- Regularization Techniques: L1 (Lasso) and L2 (Ridge)
- Polynomial Regression and Modeling Nonlinear Relationships
- Regression Trees and Spline Functions
Unsupervised Learning Techniques
- Clustering Algorithms: k-Means, k-Medoids, Hierarchical Clustering, and Self-Organizing Maps
- Dimensionality Reduction Methods: Principal Component Analysis, Factor Analysis, and Singular Value Decomposition
- Applications of Multidimensional Scaling
Text Analytics and Mining
- Text Preprocessing, Tokenization, and Parsing
- Bag-of-Words Models, Stemming, and Lemmatization
- Sentiment Analysis and Term Frequency Assessment
- Visualization of Textual Data using Word Clouds
Recommendation Engine Design
- Collaborative Filtering Approaches: User-Based and Item-Based Strategies
- Architecting and Assessing Recommendation Systems
Pattern Discovery in Associations
- Identifying Frequent Itemsets and Applying the Apriori Algorithm
- Market Basket Analysis and Calculating Lift Ratios
Anomaly and Outlier Identification
- Analyzing Extreme Values in Datasets
- Distance-Based and Density-Based Detection Methods
- Identifying Outliers within High-Dimensional Data Spaces
Applied Machine Learning Case Study
- Defining and Interpreting Business Objectives
- Data Preprocessing and Engineering Predictive Features
- Selecting Appropriate Models and Optimizing Parameters
- Assessing Results and Communicating Findings
- Model Deployment Strategies
Concluding Remarks and Future Directions
Requirements
- Fundamental knowledge of statistics and linear algebra
- Working familiarity with data analysis or business intelligence principles
- Basic programming experience, preferably in Python or R, is advised
- Professional interest in applying machine learning to data-driven projects for government
Target Audience
- Data analysts and scientists
- Statisticians and research professionals
- Developers and IT professionals seeking to integrate machine learning tools
- Professionals engaged in data science or predictive analytics initiatives
Testimonials (3)
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
Course - Machine Learning – Data science
I like that training was focused on examples and coding. I thought that it is impossible to pack so much content into three days of training, but I was wrong. Training covered many topics and everything was done in a very detailed manner (especially tuning of model's parameters - I didn't expected that there will be a time for this and I was gratly surprised).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Course - Machine Learning – Data science
It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback
