Module 1: Introduction to Predictive Modeling with R

• Overview of predictive modeling
• Introduction to R and RStudio
• Data import and preprocessing
• Exploratory data analysis
• Practical session: Data import and preprocessing in R

Module 2: Linear Regression

• Simple linear regression
• Multiple linear regression
• Assumptions and diagnostics
• Model evaluation and validation
• Practical session: Building and evaluating linear regression models in R

Module 3: Logistic Regression

• Binary logistic regression
• Multinomial logistic regression
• Assumptions and diagnostics
• Model evaluation and validation
• Practical session: Building and evaluating logistic regression models in R

Module 4: Decision Trees

• Introduction to decision trees
• Building decision trees in R
• Pruning and tuning decision trees
• Model evaluation and validation
• Practical session: Building and evaluating decision trees in R

Module 5: Random Forests

• Introduction to random forests
• Building random forests in R
• Feature importance and selection
• Model evaluation and validation
• Practical session: Building and evaluating random forests in R

Module 6: Support Vector Machines

• Introduction to support vector machines
• Building SVM models in R
• Kernel functions and tuning
• Model evaluation and validation
• Practical session: Building and evaluating SVM models in R

Module 7: Clustering Techniques

• Introduction to clustering techniques
• K-means clustering
• Hierarchical clustering
• Model evaluation and validation
• Practical session: Building and evaluating clustering models in R

Module 8: Time Series Analysis

• Introduction to time series analysis
• Decomposition of time series
• ARIMA models
• Model evaluation and validation
• Practical session: Building and evaluating time series models in R

Module 9: Ensemble Methods

• Introduction to ensemble methods
• Bagging and boosting
• Gradient boosting machines
• Model evaluation and validation
• Practical session: Building and evaluating ensemble models in R

Module 10: Model Deployment

• Introduction to model deployment
• Saving and loading models in R
• Integrating models into applications
• Monitoring model performance
• Practical session: Deploying predictive models in R

Requirements

• Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588