What will you in Machine Learning for Absolute Beginners – Level 1 Course
- Introduction to Machine Learning: Understand the basics of machine learning and its applications.
- Data Preprocessing: Learn how to clean and prepare data for machine learning models.
- Supervised Learning: Explore algorithms like Linear Regression and K-Nearest Neighbors.
- Model Evaluation: Understand how to assess the performance of machine learning models.
- Practical Applications: Apply learned concepts to real-world datasets and problems.
Program Overview
Module 1: Introduction to Machine Learning
⏳ 1 hour
Overview of machine learning and its significance in data science.
Understanding the difference between supervised and unsupervised learning.
Module 2: Data Preprocessing
⏳ 2 hours
Techniques for handling missing data.
Normalization and standardization of data.
Splitting data into training and testing sets.
Module 3: Supervised Learning Algorithms
⏳ 3 hours
Implementing Linear Regression for continuous data prediction.
Applying K-Nearest Neighbors for classification tasks.
Understanding the working principles of these algorithms.
Module 4: Model Evaluation
⏳ 2 hours
Using metrics like Mean Squared Error (MSE) and R-squared for regression models.
Evaluating classification models with accuracy, precision, recall, and F1-score.
Module 5: Practical Applications
⏳ 2 hours
Applying machine learning models to real-world datasets.
Building simple projects to reinforce learning.
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Job Outlook
High Demand for Machine Learning Skills: Machine learning expertise is sought after in various industries, including finance, healthcare, and technology.
Career Opportunities: Roles such as Data Scientist, Machine Learning Engineer, and AI Specialist are in high demand.
Industry Adoption: Companies are increasingly adopting machine learning to enhance decision-making and automate processes.
Specification: Machine Learning for Absolute Beginners – Level 1 Course
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FAQs
- Beginner-friendly, no prerequisites needed.
- Introduces basic machine learning concepts step by step.
- Covers supervised learning algorithms like Linear Regression and K-Nearest Neighbors.
- Provides practical examples for hands-on learning.
- Suitable for learners from any background.
- Data cleaning and preprocessing techniques.
- Building supervised learning models (Linear Regression, KNN).
- Evaluating model performance with metrics like MSE and accuracy.
- Applying models to real-world datasets.
- Understanding the basics of machine learning workflows.
- Prepares learners for entry-level ML roles.
- Builds portfolio-ready projects.
- Demonstrates foundational knowledge in AI and ML.
- Supports further learning in advanced ML topics.
- Enhances employability in tech, finance, and healthcare industries.
- Modules: Introduction, Data Preprocessing, Supervised Learning, Model Evaluation, Practical Applications.
- Self-paced online learning.
- Includes hands-on projects and exercises.
- Clear, beginner-friendly explanations of complex topics.
- Focuses on practical application rather than theory alone.
- Self-paced learning with lifetime access.
- Average completion: 10–12 hours depending on practice.
- Certificate awarded upon completion.
- Certificate can be added to resumes and LinkedIn.
- Suitable for showcasing beginner-level ML skills professionally.
