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What You'll Learn

Learn to implement advanced machine learning algorithms, fine-tune hyperparameters, and deploy models into production. Gain hands-on experience with techniques like ensemble methods, deep learning, and natural language processing.

Course Benefits
Industry Certification

Internationally recognized qualification

Expert Instructors

Learn from industry professionals

Dedicated Support

Assistance during and after training

Practical Skills

Apply knowledge immediately

Comprehensive 10-day curriculum with all materials included
Hands-on exercises and real-world case studies
Valuable networking opportunities with peers and experts
Post-course resources and refresher materials
Training on Advanced Machine Learning with Scikit-Learn - Course Cover Image
Duration 10 Days
Level Advanced
Format In-Person

Course Overview

Featured

In an era where data drives decisions, mastering advanced machine learning techniques is crucial. This course equips participants with the skills needed to stay competitive in the job market. This course delves into the advanced concepts and techniques of machine learning using the powerful Scikit-Learn library in Python. Participants will explore various advanced algorithms, feature engineering methods, and model evaluation techniques essential for tackling complex real-world problems. Through a combination of theoretical insights and practical exercises, attendees will enhance their skills in deploying machine learning models effectively, enabling them to make data-driven decisions in their organizations.

Duration

10 Days

Who Should Attend

  • Data Scientists and Analysts seeking to enhance their machine learning capabilities.
  • Software Engineers interested in applying machine learning to software development.
  • Business Analysts looking to leverage data for strategic decision-making.
  • Researchers and Academics wanting to deepen their understanding of machine learning methods.
  • Anyone with a foundational understanding of machine learning concepts who wants to advance their skills.

Course Impact

Organizational Impact

  • Improve decision-making with accurate, evidence-based data analysis.

  • Uncover hidden trends and opportunities using advanced statistical techniques.

  • Reduce risks from misinterpreted data and flawed strategic choices.

  • Standardize statistical understanding across teams for consistent insights.

Personal Impact

  • Gain a specialized, in-demand skill in data analysis and statistics.

  • Advance into senior data science, analytics, or research roles.

  • Contribute to organizational profitability and strategy with actionable insights.

  • Build confidence to lead and champion data-driven initiatives.

Course Objectives

By the end of this course, participants will be able to:

  • Understand and implement advanced machine learning algorithms using Scikit-Learn.
  • Conduct effective feature engineering and selection to improve model performance.
  • Evaluate and fine-tune machine learning models using best practices.
  • Deploy machine learning models for real-world applications.
  • Analyze and interpret results to derive actionable insights.

Course Outline

Module 1: Introduction to Advanced Machine Learning

  • Overview of Machine Learning
  • Review of Supervised vs. Unsupervised Learning
  • Introduction to Scikit-Learn

Module 2: Advanced Regression Techniques

  • Linear Regression and Regularization Techniques
  • Polynomial Regression
  • Advanced Topics in Regression Analysis

Module 3: Classification Algorithms

  • Support Vector Machines (SVM)
  • Decision Trees and Random Forests
  • Gradient Boosting Machines (GBM) and XGBoost

Module 4: Unsupervised Learning Techniques

  • K-Means Clustering and Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

Module 5: Feature Engineering and Selection

  • Importance of Feature Engineering
  • Techniques for Feature Selection
  • Dealing with Missing Values

Module 6: Model Evaluation and Validation

  • Train-Test Split vs. Cross-Validation
  • Metrics for Evaluation (Accuracy, Precision, Recall, F1 Score)
  • Hyperparameter Tuning with Grid Search and Random Search

Module 7: Ensemble Learning Techniques

  • Bagging vs. Boosting
  • Stacking Models
  • Practical Applications of Ensemble Methods

Module 8: Time Series Analysis with Scikit-Learn

  • Introduction to Time Series Forecasting
  • Feature Engineering for Time Series Data
  • Implementing Models for Time Series Prediction

Module 9: Model Deployment

  • Overview of Model Deployment Techniques
  • Creating REST APIs for Machine Learning Models
  • Monitoring and Maintenance of Deployed Models

Module 10: Capstone Project

  • Real-world project.
  • Presentation of projects and peer reviews.
  • Wrap-up and Q&A session.

Prerequisites

No specific prerequisites required. This course is suitable for beginners and professionals alike.

Course Administration Details

Customized Training

This training can be tailored to your institution needs and delivered at a location of your choice upon request.

Requirements

Participants need to be proficient in English.

Training Fee

The fee covers tuition, training materials, refreshments, lunch, and study visits. Participants are responsible for their own travel, visa, insurance, and personal expenses.

Certification

Upon successful completion of this course, participants will be issued with a certificate from Ideal Workplace Solutions certified by the National Industrial Training Authority (NITA) under License NO: NITA/TRN/2734.

Accommodation

Accommodation can be arranged upon request. Contact via email for reservations.

Payment

Payment should be made before the training starts, with proof of payment sent to outreach@idealworkplacesolutions.org.

For further inquiries, please contact us on details below:

Register for the Course

Select a date and location that works for you.

In-Person Training Schedules


January 2026
Date Days Venue Fee (VAT Incl.) Register
5 Jan - 16 Jan 2026 10 days Nairobi, Kenya KES 198,000 | USD 2,800 Enroll Now
5 Jan - 16 Jan 2026 10 days Cape Town, South Africa USD 7,500 Enroll Now
5 Jan - 16 Jan 2026 10 days Dubai, United Arabs Emirates USD 8,000 Enroll Now
5 Jan - 16 Jan 2026 10 days Zanzibar, Tanzania USD 4,400 Enroll Now
12 Jan - 23 Jan 2026 10 days Mombasa, Kenya KES 230,000 | USD 3,000 Enroll Now
12 Jan - 23 Jan 2026 10 days Kigali, Rwanda USD 3,800 Enroll Now
12 Jan - 23 Jan 2026 10 days Accra, Ghana USD 7,200 Enroll Now
12 Jan - 23 Jan 2026 10 days Kampala, Uganda USD 3,800 Enroll Now
19 Jan - 30 Jan 2026 10 days Dar es Salaam, Tanzania USD 4,300 Enroll Now
19 Jan - 30 Jan 2026 10 days Johannesburg, South Africa USD 6,500 Enroll Now
19 Jan - 30 Jan 2026 10 days Nakuru, Kenya KES 210,000 | USD 2,800 Enroll Now
19 Jan - 30 Jan 2026 10 days Dakar, Senegal USD 6,000 Enroll Now
26 Jan - 6 Feb 2026 10 days Pretoria, South Africa USD 6,300 Enroll Now
26 Jan - 6 Feb 2026 10 days Kisumu, Kenya KES 210,000 | USD 3,000 Enroll Now
26 Jan - 6 Feb 2026 10 days Naivasha, Kenya KES 210,000 | USD 2,800 Enroll Now
26 Jan - 6 Feb 2026 10 days Arusha, Tanzania USD 4,300 Enroll Now
5 Jan - 16 Jan 2026
10 days
Venue:
Nairobi, Kenya
Fee (VAT Incl.):
KES 198,000
USD 2,800
Enroll Now
5 Jan - 16 Jan 2026
10 days
Venue:
Cape Town, South Africa
Fee (VAT Incl.):
USD 7,500
Enroll Now
5 Jan - 16 Jan 2026
10 days
Venue:
Dubai, United Arabs Emirates
Fee (VAT Incl.):
USD 8,000
Enroll Now
5 Jan - 16 Jan 2026
10 days
Venue:
Zanzibar, Tanzania
Fee (VAT Incl.):
USD 4,400
Enroll Now
12 Jan - 23 Jan 2026
10 days
Venue:
Mombasa, Kenya
Fee (VAT Incl.):
KES 230,000
USD 3,000
Enroll Now
12 Jan - 23 Jan 2026
10 days
Venue:
Kigali, Rwanda
Fee (VAT Incl.):
USD 3,800
Enroll Now
12 Jan - 23 Jan 2026
10 days
Venue:
Accra, Ghana
Fee (VAT Incl.):
USD 7,200
Enroll Now
12 Jan - 23 Jan 2026
10 days
Venue:
Kampala, Uganda
Fee (VAT Incl.):
USD 3,800
Enroll Now
19 Jan - 30 Jan 2026
10 days
Venue:
Dar es Salaam, Tanzania
Fee (VAT Incl.):
USD 4,300
Enroll Now
19 Jan - 30 Jan 2026
10 days
Venue:
Johannesburg, South Africa
Fee (VAT Incl.):
USD 6,500
Enroll Now
19 Jan - 30 Jan 2026
10 days
Venue:
Nakuru, Kenya
Fee (VAT Incl.):
KES 210,000
USD 2,800
Enroll Now
19 Jan - 30 Jan 2026
10 days
Venue:
Dakar, Senegal
Fee (VAT Incl.):
USD 6,000
Enroll Now
26 Jan - 6 Feb 2026
10 days
Venue:
Pretoria, South Africa
Fee (VAT Incl.):
USD 6,300
Enroll Now
26 Jan - 6 Feb 2026
10 days
Venue:
Kisumu, Kenya
Fee (VAT Incl.):
KES 210,000
USD 3,000
Enroll Now
26 Jan - 6 Feb 2026
10 days
Venue:
Naivasha, Kenya
Fee (VAT Incl.):
KES 210,000
USD 2,800
Enroll Now
26 Jan - 6 Feb 2026
10 days
Venue:
Arusha, Tanzania
Fee (VAT Incl.):
USD 4,300
Enroll Now

Request Custom Training


We offer customized training solutions tailored to your organization's specific needs:

  • Training at your preferred location
  • Customized content to address your specific challenges
  • Flexible scheduling to accommodate your team
  • Cost-effective solution for training multiple employees
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Frequently Asked Questions

Find answers to common questions about this course

The goal is to equip you with advanced skills to build, optimize, and deploy robust machine learning models using the Scikit-Learn library, tackling real-world complexities.
Scikit-Learn is a powerful and user-friendly Python library that provides a consistent interface for a wide range of machine learning algorithms, making it an industry standard.
You'll learn advanced topics like ensemble methods (Random Forests, Gradient Boosting), dimensionality reduction (PCA), and effective strategies for handling imbalanced datasets.
You'll master techniques like hyperparameter tuning with GridSearchCV and RandomizedSearchCV to find the optimal settings for your models, boosting their performance.
The course teaches you to build end-to-end machine learning pipelines, ensuring your models are reproducible, scalable, and ready for deployment in a production environment.
Training on Advanced Machine Learning with Scikit-Learn

Next class starts 5 Jan 2026

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