Machine Learning
Overview
Course Objective
By the end of this course:
• You will understand what Machine Learning is
• You will understand the life cycle of any project in the field of machine learning
• You will be familiar with most common datasets
• You will be able to use different algorithms used in supervised and unsupervised learning
Who Should Attend
This course is ideal for individuals who are interested in pursuing a career or enhancing their knowledge in the field of machine learning, including but not limited to:
Aspiring data scientists
Software developers looking to integrate machine learning into their applications
Engineers and IT professionals seeking to expand their skillset
Researchers and analysts interested in machine learning techniques
Business analysts who want to leverage machine learning for data-driven decision-making
Prerequisites
Basic knowledge of programming (preferably in Python)
Familiarity with fundamental concepts in statistics and linear algebra
A basic understanding of data structures and algorithms
Experience with using development environments (e.g., Jupyter Notebooks, IDEs)
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Getting Started with ML’
• What is Machine Learning
• Machine Learning Steps
• What is scikit-learn
• Installing scikit-learn
Module 2 - Datasets
• What is Dataset
• Iris Dataset
• Handwritten Digits Dataset
• Boston Housing Price Dataset
Module 3 - Data Preprocessing’
• Get the Dataset
• Missing Data
• Categorical Data
• Feature Scaling
• Splitting the Dataset Into the Training Set and Test Set
• Data Preprocessing Template
Module 4 - Supervised Learning
• What is Supervised Learning
• Key Classifiers Algorithms – KNN, SVM, GNB, DT, Ensemble
• Performance Metric and Errors
• Regression
Module 5 - Supervised Learning Evaluation
• Confusion Matrix
• Accuracy
• Recall
• Precision
• ROC
• F-Score
Module 6 - Unsupervised Learning
• What is Unsupervised Learning
• Key Clustering Algorithms – K-Means, Agglomerative
• Dendrogram
• Dimensionality Reduction – PCA
Module 7 - Evaluation
• Evaluation Metrics if the Truth is Available
• Evaluation Metrics if the Truth is not Available
Module 8 - Ensemble Learning
• Bootstrap Aggregating (Bagging)
• Boosting
Module 9 - Practical project
FAQs
General Questions:
Q: What is the Machine Learning course about?
This 3-day course introduces participants to the fundamentals of machine learning, including the differences between Artificial Intelligence, Machine Learning, and Deep Learning. The course covers both supervised and unsupervised learning algorithms, data preprocessing, and how to structure and build a machine learning-based application. Participants will gain hands-on experience by working on a comprehensive project at the end of the course.
Q: Who should attend this course?
Aspiring data scientists
Software developers looking to integrate machine learning into their applications
Engineers and IT professionals seeking to expand their skillset
Researchers and analysts interested in machine learning techniques
Business analysts who want to leverage machine learning for data-driven decision-making
Q: What are the prerequisites for this course?
Basic knowledge of programming (preferably in Python)
Familiarity with fundamental concepts in statistics and linear algebra
A basic understanding of data structures and algorithms
Experience with using development environments (e.g., Jupyter Notebooks, IDEs)
Q: How long is the course?
The course spans 3 days.
Q: What key topics are covered in this course?
Module 1: Getting Started with ML
Module 2: Datasets
Module 3: Data Preprocessing
Module 4: Supervised Learning
Module 5: Supervised Learning Evaluation
Module 6: Unsupervised Learning
Module 7: Evaluation
Module 8: Ensemble Learning
Module 9: Practical Project
Q: Will I receive a certification after completing the course?
Yes, participants will receive a certificate of completion, recognizing their skills in machine learning.
Program Content & Skills:
Q: What foundational Machine Learning concepts will I learn in this course?
You’ll explore the core principles of machine learning, understanding how it applies to real-world data problems. Key topics include supervised and unsupervised learning algorithms, data preprocessing techniques, model evaluation, and performance metrics. You’ll also learn how to structure and build a machine learning-based application and practice applying your knowledge in a comprehensive project.
Q: How does the course help me apply Machine Learning to real-world data problems?
You’ll work with real-world datasets to apply various machine learning algorithms, including classification, regression, and clustering. This hands-on approach helps you understand how to solve practical problems across industries such as healthcare, finance, marketing, and more.
Q: What skills will I develop in managing and optimizing Machine Learning models?
You’ll learn how to administer and optimize machine learning models, including techniques for model selection, hyperparameter tuning, and performance evaluation. You’ll also master best practices for managing large datasets, addressing missing data, and optimizing model performance for real-world applications.
Q: Will I learn how to handle different types of data in Machine Learning?
Yes. The course covers working with various data types, including structured, unstructured, and time-series data. You’ll learn data preprocessing techniques such as feature scaling, handling missing values, and splitting datasets into training and test sets, along with optimization methods for different data types.
Q: How does this course prepare me for applying Machine Learning in a professional context?
You’ll gain technical knowledge and practical skills to integrate machine learning into business solutions, such as predictive modeling, data analysis, and decision-making. By applying what you learn in real-world projects, you’ll be prepared to tackle machine learning challenges in industries such as finance, e-commerce, healthcare, and more.
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