Course Overview
This comprehensive course will be your guide to learning how to use the power of Python to analyze big data, create beautiful visualizations, and use powerful machine learning algorithms. This course is designed for both beginners with basic programming experience or experienced developers looking to make the jump to Data Science and big data analysis.
Duration
5 days
Target Audience
- Data Scientists
- Machine Learning Engineers
- Data Analysts
- Software Developers
- Research Scientists
- Business Analysts
- IT Professionals
- Academics and Researchers
- Statisticians
- Professionals interested in AI and machine learning applications
Organizational Impacts
- Improved ability to develop and deploy machine learning models
- Enhanced data-driven decision-making capabilities
- Increased efficiency in automating and optimizing business processes
- Better predictive analytics and forecasting
- Strengthened competitive advantage through advanced analytics
- Streamlined data processing and model training workflows
- More accurate and actionable insights from data
Personal Impacts
- Advanced skills in machine learning and Python programming
- Improved ability to design, implement, and evaluate machine learning models
- Increased job market competitiveness with machine learning expertise
- Greater confidence in applying machine learning techniques to real-world problems
- Expanded knowledge in various machine learning algorithms and methods
- Enhanced problem-solving skills through practical machine learning applications
- Increased productivity through automation of complex data analysis tasks
Course Level:
Course Objectives
- Understand the foundational concepts of machine learning and its applications.
- Set up and configure the Python environment for machine learning projects.
- Learn to preprocess and engineer features for machine learning models.
- Implement and evaluate various supervised learning algorithms, including classification and regression.
- Apply unsupervised learning techniques such as clustering and dimensionality reduction.
- Explore advanced machine learning methods, including ensemble techniques and neural networks.
- Perform hyperparameter tuning and optimize machine learning models.
- Deploy machine learning models into production environments and integrate them into real-world applications.
- Monitor and maintain deployed models to ensure their performance and reliability.
Course Outline
Module 1: Introduction to Machine Learning with Python
- Overview of machine learning concepts and algorithms
- Setting up the Python environment for machine learning
- Introduction to key Python libraries (Scikit-learn, NumPy, Pandas)
- Basic data preprocessing and feature engineering
- Case Study: Implement a simple machine learning model to classify a dataset and evaluate its performance.
Module 2: Supervised Learning Techniques
- Understanding supervised learning and its applications
- Implementing classification algorithms (e.g., Logistic Regression, Decision Trees)
- Implementing regression algorithms (e.g., Linear Regression, Ridge Regression)
- Model evaluation and performance metrics
- Case Study: Build and evaluate a classification model to predict customer churn using historical data.
Module 3: Unsupervised Learning and Clustering
- Introduction to unsupervised learning methods
- Implementing clustering algorithms (e.g., K-Means, Hierarchical Clustering)
- Dimensionality reduction techniques (e.g., PCA)
- Analyzing and interpreting clustering results
- Case Study: Apply clustering techniques to segment a customer base into distinct groups based on purchasing behavior.
Module 4: Advanced Machine Learning Techniques
- Introduction to ensemble methods (e.g., Random Forests, Gradient Boosting)
- Implementing support vector machines and neural networks
- Hyperparameter tuning and model optimization
- Handling overfitting and underfitting
- Case Study: Develop an ensemble model to improve the accuracy of a predictive analysis on a complex dataset.
Module 5: Model Deployment and Real-World Applications
- Introduction to model deployment strategies
- Using Python frameworks for model deployment (e.g., Flask, Django)
- Integrating machine learning models into production systems
- Monitoring and maintaining deployed models
- Case Study: Deploy a trained machine learning model to a web application and demonstrate its use for real-time predictions.
Related Courses
Course Administration Details:
Methodology
These instructor-led training sessions are delivered using a blended learning approach and include presentations, guided practical exercises, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience as professionals and trainers in these fields. All facilitation and course materials are offered in English. Participants should be reasonably proficient in the language.
Accreditation
Upon successful completion of this training, participants will be issued 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 to their travel expenses, visa application, insurance, and other personal expenses.
Accommodation and Airport Transfer
Accommodation and Airport Transfer are arranged upon request. For reservations contact the Training Officer.
- Email: [email protected]
- Phone: +254715 077 817
Tailor-Made
This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location. For further inquiries, please contact us on:
- Email: [email protected]
- Phone: +254715 077 817
Payment
Payment should be transferred to the IRES account through a bank on or before the start of the course. Send proof of payment to [email protected]
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