Training on Introduction to AI, Data Science & Machine Learning with Python
Master AI, data science, and machine learning with Python. Build intelligent systems, analyze data, and make informed decisions.
Next intake
20 Jul 2026 · Nakuru
Duration
5 days
Live instruction
Delivery
Physical + Virtual
Cohort based
Level
Intermediate
Working professionals
Certification
NITA reimbursable
For Kenyan cohorts
Language
English
All materials
About this programme
This comprehensive course is designed to equip professionals with the foundational skills and techniques needed to succeed in the rapidly growing field of data science. Participants will learn the data science lifecycle, from data analysis and visualization using Python and its libraries to preprocessing unstructured data and building AI/ML models. The training provides a strategic framework for applying these techniques to solve real-world problems.
Duration
5 Days
Who Should Attend:
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Aspiring data scientists and machine learning engineers
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Data analysts and business intelligence professionals
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Managers and team leaders seeking to understand data-driven strategies
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Professionals in marketing, finance, and operations
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Anyone interested in a career in AI, data science, and analytics
What you'll walk away with
By the end of this training, participants will be able to:
- Differentiate between Predictive AI and Generative AI.
- Translate everyday business questions and problems into Machine Learning tasks to make data-driven decisions.
- Use Python Pandas, Matplotlib & Seaborn libraries to explore, analyze, and visualize data from various sources, including the web, word documents, email, NoSQL stores, databases, and data warehouses.
- Train a Machine Learning Classifier using different algorithmic techniques from the Scikit-Learn library, such as Decision Trees, Logistic Regression, and Neural Networks.
- Re-segment your customer market using K-Means and Hierarchical algorithms to better align products and services to customer needs.
- Discover hidden customer behaviors from Association Rules and build a Recommendation Engine based on behavioral patterns.
- Investigate relationships & flows between people and business-relevant entities using Social Network Analysis.
- Build predictive models of revenue and other numeric variables using Linear Regression.
- Leverage continued support with after-course one-on-one instructor coaching and computing sandbox.
What we cover, module by module
Module 1: The Strategic Role of a Data Scientist
- Required technical and non-technical skillsets
- Distinction between Data Scientist and Data Engineer
- Full lifecycle of data science initiatives
- Translating business problems into AI/ML solutions
- Understanding data sources for analytical insights
- Generative AI vs Discriminative AI
- Case Study: Using data science to solve a real business problem (e.g., customer churn prediction)
- Practical: Mapping business questions to data science solutions
Module 2: Data Manipulation & Visualization with Python
- Introduction to Python for data science
- Data import, export, and handling from multiple sources
- Data manipulation using Pandas (filtering, grouping, transformations)
- Handling missing data, duplicates, normalization, and scaling
- Data visualization with Matplotlib and Seaborn
- Case Study: Cleaning and analyzing raw business data for insights
- Practical: Performing data cleaning, transformation, and visualization
Module 3: Natural Language Processing (NLP) & Unstructured Data
- Preprocessing text data (emails, web content, documents)
- Techniques: stemming, tokenization, stop-word removal
- Building term-document matrices (TDM)
- Integrating Large Language Models (LLMs) in analysis
- Case Study: Analyzing customer feedback and sentiment from text data
- Practical: Processing and analyzing unstructured text datasets
Module 4: AI Ethics, Big Data & Professional Communication
- Cloud-based analytics (Azure, AWS, Google Cloud)
- Ethical considerations in AI and data usage
- Data privacy, bias, and governance
- Communication and storytelling with data
- Career growth and continuous learning
- Case Study: Ethical challenges in AI deployment and decision-making
- Practical: Developing an ethical AI framework and presenting insights
Module 5: Machine Learning Evaluation, Classification & Clustering
- Classification methods (logistic regression, neural networks)
- Activation functions and model fundamentals
- Naive Bayes and probability-based models
- Model evaluation (ROC, AUC, precision, recall, confusion matrix)
- Clustering techniques (K-Means, hierarchical clustering)
- Applications on structured and unstructured data
- Case Study: Customer segmentation and predictive modeling in business
- Practical: Building and evaluating machine learning models
Where the change lands
Organisational Impact
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Strengthens organisational capacity to leverage data-driven insights for strategic decision-making.
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Enhances competitiveness by equipping teams with skills in AI, data science, and machine learning applications.
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Reduces dependency on external consultants by building in-house expertise for data analysis and predictive modeling.
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Improves operational efficiency through automation and intelligent systems powered by machine learning.
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Supports innovation in products, services, and customer engagement through data-driven strategies.
Personal Impact
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Equips participants with foundational skills in Python for data science, AI, and machine learning.
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Builds confidence in applying key algorithms such as regression, classification, and clustering to solve problems.
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Provides hands-on experience in real-world applications like customer churn prediction and recommendation systems.
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Expands career opportunities in the rapidly growing fields of AI, data science, and analytics.
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Empowers learners to build a strong portfolio of projects showcasing applied skills in AI and ML.
Dates and locations
Upcoming intakes
Every intake is limited to a small cohort. Booking closes when a date fills or three weeks before the start, whichever comes first.
| City | Starts | Ends | Delivery | Book |
|---|---|---|---|---|
NakuruNext | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Kigali | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Accra | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Kisumu | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Johannesburg | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Dakar | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
- NakuruNext
20 Jul → 24 Jul·In-Person
Book this intake - Kigali
20 Jul → 24 Jul·In-Person
Book this intake - Accra
20 Jul → 24 Jul·In-Person
Book this intake - Kisumu
27 Jul → 31 Jul·In-Person
Book this intake - Johannesburg
27 Jul → 31 Jul·In-Person
Book this intake - Dakar
27 Jul → 31 Jul·In-Person
Book this intake
Common questions.
Still not sure? Send us a note and a facilitator will get back to you within a business day.
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