Introduction

The machine learning using Stata course is designed for individuals who are interested in leveraging machine learning techniques and Stata's capabilities for data analysis and predictive modeling. 

Duration

10 Days

Who Should Attend?

Data analysts.

Researchers.

Statisticians.

Econometricians.

Data scientists.

Anyone with an interest in machine learning.

COURSE LEVEL: Foundation

Course Objectives

At the end of this IRES training course, participants will learn:

1. Understanding the fundamentals: Gain a solid understanding of the basic concepts and principles of machine learning, including supervised and unsupervised learning, model training, evaluation, and interpretation.
2. Familiarity with Stata's machine learning capabilities: Learn how to utilize Stata's built-in machine learning tools and functions, such as regression models, decision trees, support vector machines, neural networks, clustering algorithms, and dimensionality reduction techniques.
3. Data preparation and exploration: Learn how to clean and preprocess data in Stata, handle missing values, perform exploratory data analysis, and engineer relevant features for machine learning tasks.
4. Model selection and evaluation: Develop the ability to choose appropriate machine learning algorithms for different types of problems, understand their strengths and limitations, and evaluate model performance using various metrics and cross-validation techniques.
5. Interpretation and validation: Learn how to interpret model coefficients, feature importance, and other relevant outputs in Stata. Understand how to validate and assess model performance using external datasets and appropriate validation techniques.
6. Practical application and deployment: Gain hands-on experience in applying machine learning techniques to real-world problems using Stata. Learn how to deploy trained models, generate predictions, and incorporate machine learning into practical workflows.
7. Advanced topics and techniques: Explore advanced topics in machine learning with Stata, such as handling imbalanced datasets, ensemble methods, time series analysis, and forecasting. Gain knowledge of best practices, ethical considerations, and addressing biases in machine learning projects.

Course Outline

Module 1: Introduction To Machine Learning

• Understanding the basics of machine learning
• Differentiating between supervised and unsupervised learning
• Overview of common machine learning algorithms and their applications

Module 2: Data Preparation And Exploration

• Data cleaning and handling missing values in Stata
• Exploratory data analysis techniques
• Feature selection and engineering in Stata

Module 3: Supervised Learning Algorithms In Stata

• Linear regression and logistic regression
• Decision trees and random forests
• Support vector machines (SVM)
• Neural networks and deep learning

Module 4: Model Training And Evaluation

• Training machine learning models in Stata
• Cross-validation techniques
• Evaluating model performance using metrics like accuracy, precision, recall, and AUC-ROC

Module 5: Unsupervised Learning Algorithms In Stata

• Clustering algorithms (e.g., k-means, hierarchical clustering)
• Dimensionality reduction techniques (e.g., principal component analysis, t-SNE)

Module 6: Model Interpretation And Validation

• Interpreting model coefficients and feature importance in Stata
• Validating model performance using external datasets and bootstrapping techniques

Module 7: Advanced Topics In Machine Learning With Stata

• Handling imbalanced datasets
• Ensemble methods (e.g., bagging, boosting)
• Time series analysis and forecasting with machine learning in Stata.

Course Administration Details:

Methodology

The instructor-led training are delivered using a blended learning approach and comprises presentations, guided sessions of practical exercise, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience, working as professionals and trainers in these fields.

All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.

ACCREDITATION

Upon successful completion of this training, participants will be issued with an Indepth Research Institute(IRES) certificate certified by the National Industrial Training Authority (NITA).

TRAINING VENUE

The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments, and lunch.

All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.

ACCOMMODATION AND AIRPORT PICKUP

Accommodation and airport pickup are arranged upon request. For reservations contact the Training Officer.

Email:[email protected] / [email protected]

TAILOR- MADE

This training can also be customized to suit the needs of your institution upon request. You can have it delivered at our IRES Training Centre or at a convenient location.

For further inquiries, please contact us on Tel: +254 715 077 817 0r +250 789 621 067

Mob: +254 792516000, +254 792516010   +250 789 621 067 or mail [email protected] / [email protected]

PAYMENT

Payment should be transferred to IRES account through the bank before the course start date

Send proof of payment to [email protected] / [email protected]

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