Course Overview

Artificial Intelligence and Machine Learning for Data Science is a comprehensive professional training program designed to equip participants with the knowledge and practical skills required to leverage intelligent algorithms, predictive models, and advanced analytics for solving complex business, research, and operational challenges. As organizations increasingly adopt Artificial Intelligence (AI), Machine Learning (ML), Data Science, Predictive Analytics, Deep Learning, Business Intelligence, Big Data Analytics, Neural Networks, Data Mining, and AI-Powered Decision Making, professionals must develop the expertise needed to transform large volumes of data into actionable insights. This course provides a solid foundation in AI and machine learning concepts while emphasizing practical implementation using modern data science tools and frameworks.

The training explores the complete AI and machine learning lifecycle, including data collection, preprocessing, exploratory analysis, feature engineering, model development, evaluation, deployment, and monitoring. Participants will learn how to apply supervised learning, unsupervised learning, deep learning, natural language processing, and predictive analytics techniques to solve real-world problems across industries such as healthcare, finance, agriculture, education, marketing, manufacturing, logistics, and public administration. The course combines theoretical foundations with extensive hands-on exercises using real-world datasets.

Participants will gain practical experience in building machine learning models, developing predictive analytics solutions, conducting data mining activities, creating intelligent recommendation systems, performing sentiment analysis, and applying neural network architectures. The course emphasizes model optimization, explainable AI, ethical AI practices, and the interpretation of machine learning results for strategic decision-making. Through practical projects and case studies, participants will learn how to deploy AI solutions that improve efficiency, innovation, and organizational performance.

The training further addresses emerging trends in artificial intelligence, including generative AI, large language models (LLMs), reinforcement learning, computer vision, AI governance, automated machine learning (AutoML), MLOps, cloud AI platforms, and responsible AI implementation. Participants will develop competencies required to design, implement, evaluate, and manage AI-driven data science solutions that support digital transformation and sustainable innovation.

Course Objectives

1. Understand the core concepts and applications of artificial intelligence and machine learning.

2. Apply data science methodologies to solve business and research problems.

3. Prepare and preprocess datasets for machine learning projects.

4. Build, evaluate, and optimize machine learning models.

5. Implement supervised and unsupervised learning techniques.

6. Apply deep learning and neural network architectures.

7. Develop predictive analytics and forecasting solutions.

8. Utilize natural language processing and text analytics techniques.

9. Deploy machine learning models in real-world environments.

10. Understand ethical, governance, and responsible AI principles.

Organizational Benefits

1. Enhanced data-driven decision-making capabilities.

2. Improved operational efficiency through intelligent automation.

3. Better forecasting and predictive planning.

4. Increased innovation through AI-powered solutions.

5. Improved customer insights and personalization strategies.

6. Enhanced risk management and anomaly detection.

7. Faster processing and analysis of large datasets.

8. Reduced operational costs through automation.

9. Improved competitive advantage and digital transformation readiness.

10. Stronger organizational capacity in advanced analytics and emerging technologies.

Target Participants

· Data scientists and data analysts

· Business intelligence professionals

· Software developers and engineers

· Researchers and statisticians

· Monitoring and Evaluation (M&E) specialists

· IT and digital transformation professionals

· Financial and risk analysts

· Healthcare and public health professionals

· Marketing and customer analytics specialists

· Academic faculty and postgraduate students

· Innovation and technology managers

· Anyone interested in AI, machine learning, and advanced analytics

Course Outline

Module 1: Introduction to Artificial Intelligence and Data Science

1. Fundamentals of artificial intelligence and machine learning

2. AI versus traditional analytics approaches

3. Data science lifecycle and workflows

4. Applications of AI across industries

5. AI ecosystem, tools, and technologies

6. Emerging trends in intelligent systems

Case Study:
Developing an AI strategy to improve organizational performance and innovation.

Module 2: Data Collection, Preparation, and Feature Engineering

1. Data acquisition and integration techniques

2. Data cleaning and preprocessing methods

3. Handling missing values and outliers

4. Feature selection and extraction techniques

5. Data transformation and normalization

6. Building machine learning-ready datasets

Case Study:
Preparing customer transaction data for predictive analytics applications.

Module 3: Exploratory Data Analysis and Visualization

1. Descriptive analytics and summary statistics

2. Exploratory data analysis techniques

3. Data visualization best practices

4. Pattern identification and trend analysis

5. Correlation and dependency analysis

6. Data storytelling and communication

Case Study:
Analyzing consumer behavior data to identify purchasing patterns.

Module 4: Supervised Machine Learning

1. Classification and regression concepts

2. Decision trees and random forests

3. Support Vector Machines (SVM)

4. k-Nearest Neighbors (KNN)

5. Ensemble learning techniques

6. Model training and validation

Case Study:
Predicting customer churn using supervised learning models.

Module 5: Unsupervised Learning and Clustering

1. Clustering algorithms and applications

2. K-means clustering techniques

3. Hierarchical clustering methods

4. Dimensionality reduction approaches

5. Principal Component Analysis (PCA)

6. Market segmentation analytics

Case Study:
Segmenting customers for targeted marketing campaigns.

Module 6: Advanced Predictive Analytics and Forecasting

1. Predictive modeling frameworks

2. Time series analysis concepts

3. Forecasting techniques and methods

4. Model performance measurement

5. Scenario analysis and simulations

6. Business forecasting applications

Case Study:
Forecasting product demand and inventory requirements.

Module 7: Deep Learning and Neural Networks

1. Introduction to deep learning architectures

2. Artificial neural networks fundamentals

3. Forward and backward propagation

4. Convolutional Neural Networks (CNNs)

5. Recurrent Neural Networks (RNNs)

6. Deep learning model optimization

Case Study:
Developing image classification models for automated quality inspection.

Module 8: Natural Language Processing (NLP)

1. Fundamentals of text analytics

2. Text preprocessing and tokenization

3. Sentiment analysis techniques

4. Topic modeling and classification

5. Language models and transformers

6. NLP applications in business and research

Case Study:
Analyzing customer reviews and social media sentiment using NLP.

Module 9: Computer Vision and Image Analytics

1. Introduction to computer vision

2. Image processing techniques

3. Object detection and recognition

4. Facial recognition systems

5. Video analytics applications

6. Computer vision deployment strategies

Case Study:
Using computer vision to automate defect detection in manufacturing.

Module 10: MLOps and AI Deployment

1. Machine learning operations (MLOps) principles

2. Model deployment workflows

3. Monitoring and maintaining AI systems

4. Cloud-based AI platforms

5. API integration and automation

6. Scalable AI solution deployment

Case Study:
Deploying a predictive analytics model into a production environment.

Module 11: Responsible AI, Ethics, and Governance

1. Ethical considerations in AI systems

2. Bias detection and fairness assessment

3. Explainable AI and model transparency

4. Data privacy and security considerations

5. AI governance frameworks

6. Regulatory and compliance requirements

Case Study:
Evaluating ethical risks and governance requirements for an AI-driven decision-support system.

Module 12: Generative AI and Future Trends in Data Science

1. Generative AI and large language models

2. Automated machine learning (AutoML)

3. Reinforcement learning fundamentals

4. AI-powered business intelligence

5. Future applications of artificial intelligence

6. Emerging innovations in data science and machine learning

Case Study:
Designing an enterprise AI ecosystem that integrates machine learning, deep learning, NLP, computer vision, predictive analytics, generative AI, MLOps, governance frameworks, and intelligent decision-support systems to enhance innovation, operational excellence, customer experience, and strategic growth.

Essential Information

Our courses are customizable to suit the specific needs of participants.
Participants are required to have proficiency in the English language.
Our training sessions feature comprehensive guidance through presentations, practical exercises, web-based tutorials, and collaborative group activities. Our facilitators boast extensive expertise, each with over a decade of experience.
Upon fulfilling the training requirements, participants will receive a prestigious Global King Project Management certificate.
Training sessions are conducted at various Global King Project Management Centers, including locations in Nairobi, Mombasa, Kigali, Dubai, Lagos, and others.
Organizations sending more than two participants from the same entity are eligible for a generous 20% discount.
The duration of our courses is adaptable, and the curriculum can be adjusted to accommodate any number of days.
To ensure seamless preparation, payment is expected before the commencement of training, facilitated through the Global King Project Management account.
For inquiries, reach out to us via email at training@globalkingprojectmanagement.org or by phone at +254 114 830 889.
Additional amenities such as tablets and laptops are available upon request for an extra fee. The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a certificate of successful completion. Participants are responsible for arranging and covering their travel expenses, including airport transfers, visa applications, dinners, health insurance, and any other personal expenses.

Artificial Intelligence and Machine Learning for Data Science Training Course