Course Overview
This course provides a comprehensive introduction to machine learning, focusing on its application in data analysis. Participants will gain a solid understanding of core machine learning concepts, algorithms, and techniques. Through hands-on exercises and real-world case studies, participants will develop the skills to extract valuable insights from data, build predictive models, and make data-driven decisions.
Course Duration
10 Days
Who Should Attend
- Data Analysts and Scientists
- Business Analysts
- Statisticians and Researchers
- IT Professionals and Developers
- Professionals interested in gaining practical skills in machine learning
- Individuals with a background in data analysis who want to incorporate machine learning into their skillset
Course Objectives
By the end of this course, participants will be able to:
- To understand the fundamentals of machine learning and its role in data analysis.
- To explore various machine learning algorithms and their applications in solving data problems.
- To develop the ability to pre-process data and prepare it for machine learning models.
- To gain proficiency in evaluating and tuning machine learning models for optimal performance.
- To learn to implement machine learning techniques using popular tools and libraries like Python and R.
- To apply machine learning models to real-world data sets and interpret the results.
- To understand the ethical considerations and limitations of machine learning in data analysis.
- To develop problem-solving skills by working on practical machine learning projects.
- To stay updated with the latest trends and advancements in machine learning.
- To build a foundation for advanced studies or a career in machine learning and data science.
Course Outline:
Module 1: Introduction to Machine Learning
- Definition and types of machine learning
- Supervised vs. unsupervised learning
- The machine learning process
- Python programming fundamentals for machine learning
Module 2: Data Exploration and Preprocessing
- Data loading and inspection
- Exploratory data analysis (EDA)
- Data cleaning and handling missing values
- Feature engineering and selection
- Data visualization techniques
Module 3: Linear Regression
- Simple and multiple linear regression
- Model evaluation metrics
- Overfitting and underfitting
- Regularization techniques
Module 4: Logistic Regression
- Logistic regression for classification
- Model evaluation metrics
- Odds and logit
- Decision boundaries
Module 5: Decision Trees and Random Forests
- Decision tree algorithm
- Random forest algorithm
- Feature importance
- Hyperparameter tuning
Module 6: Support Vector Machines (SVM)
- SVM for classification and regression
- Kernel trick
- Model selection and hyperparameter tuning
Module 7: Clustering
- K-means clustering
- Hierarchical clustering
- Evaluation of clustering results
Module 8: Model Evaluation and Selection
- Performance metrics for classification and regression
- Cross-validation
- Model comparison and selection
- Bias-variance trade-off
Module 9: Model Deployment and Interpretation
- Model deployment options
- Model interpretation techniques
- Explainable AI
- Ethical considerations in machine learning
Module 10: Advanced Topics
- Deep learning introduction
- Neural networks
- Natural language processing
- Time series analysis
- Model optimization and scalability
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
A certificate from Ideal Workplace Solutions is awarded upon successful completion.
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:
