Introduction to Machine Learning
Introduction to Machine Learning
Updated on Jul, 2024
Language - English
Development,Data Science,Machine Learning
Lectures -5
Duration -4 hours
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Course Description
Welcome to our "Introduction to Machine Learning" course, designed to equip you with the essential knowledge and skills to master the fundamentals of machine learning. Throughout this comprehensive program, you will explore a wide range of machine learning concepts, algorithms, and practical applications, focusing on the core principles of predictive modeling and data analysis.
This course covers crucial topics, including the skills required for data science, understanding machine learning and its applications, and the distinctions between machine learning and deep learning. You will delve into supervised and unsupervised learning techniques, gaining hands-on experience with data handling, model building, and implementing various algorithms. Additionally, the course addresses essential aspects of data preparation, such as outlier removal, missing values treatment, standardization, and normalization.
Tailored for aspiring data scientists and machine learning enthusiasts, this course aims to enhance your proficiency in applying machine learning techniques effectively. You will learn to implement foundational algorithms, build and optimize models, and utilize feature engineering strategies to extract meaningful insights from data. The emphasis on practical learning ensures you develop a robust understanding of machine learning principles through application-oriented exercises and projects.
Enroll in our "Introduction to Machine Learning" course to build a solid foundation in this dynamic field. Gain the skills needed to address complex data challenges and embark on a rewarding journey into the world of machine learning.
Who this course is for:
- Data analysts and data scientists
- Students and academic researchers
- Business professionals
- Entrepreneurs and innovators
- Career Changers
- Anyone Interested in Data Analysis
Goals
- Foundations of Machine Learning: Gain a solid understanding of fundamental machine learning concepts and principles
Supervised Learning Techniques: Explore key supervised learning algorithms such as Linear Regression, Logistic Regression, and Decision Trees
Model Evaluation and Validation: Learn techniques for evaluating and validating machine learning models including cross-validation, confusion matrices, and perf
Feature Engineering: Develop skills in creating new features and selecting the most impactful ones for improving model performance
Practical Implementation: Hands-on experience with Python libraries to build, train, and deploy machine learning models.
Ethics and Bias in Machine Learning: Understand the ethical implications and learn strategies to identify and mitigate bias in machine learning models.
Prerequisites
Basic understanding of Python programming, including familiarity with libraries such as NumPy and Pandas
Essential knowledge of linear algebra, calculus, and probability/statistics
Basic understanding of data structures and control flow.
Curriculum
Check out the detailed breakdown of what’s inside the course
Module 1
1 Lectures
-
Module 1
57:24
57:24
Module 2
1 Lectures
Module 3
1 Lectures
Module 4
1 Lectures
Module 5
1 Lectures
Instructor Details
GreyCampus Inc.
About me
GreyCampus helps people power their careers through skills and certifications. We believe continuous upskilling and certifications is key to sustained success in your career. While older skills are fast becoming less relevant, need for newer in-demand skills is growing exponentially. We believe if you stay skilled, you will stay ahead.
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