Crash Course Introduction to Machine Learning
Kickstart Your Machine Learning Journey: Hands-On Projects with Python Libraries
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What you'll learn
- Learn the key concepts of Machine Learning
- Get experienced with Jupyter Notebooks
- Learn how to use Python libraries, such as Scikit-learn, numpy, pandas, matplotlib
- Data handling & cleaning to be used in Machine Learning
- Introduced to common ML algorithms
- Learn to evaluate the performance of a model
- Have hands-on experience with ML algorithms
Description
Welcome to "Crash Course Introduction to Machine Learning"! This course is designed to provide you with a solid foundation in machine learning, leveraging the powerful Scikit-learn library in Python.
What You'll Learn:
The Basics of Machine Learning: Understand the key concepts and types of machine learning, including supervised, unsupervised, and reinforcement learning.
Setting Up Your Environment: Get hands-on experience setting up Python, Jupyter Notebooks, and essential libraries like numpy, pandas, matplotlib, and Scikit-learn.
Data Preprocessing: Learn how to load, clean, and preprocess data, handle missing values, and split data for training and testing.
Building Machine Learning Models: Explore common algorithms such as Linear Regression, Decision Trees, and K-Nearest Neighbors. Train and evaluate models(Linear Regression), and understand performance metrics like accuracy, R^2 and scatter values in plots to measure the prediction.
Model Deployment: Gain practical knowledge on saving your pre-trained model for others to use.
This course is structured to provide you with both theoretical understanding and practical skills. Each section builds on the previous one, ensuring you develop a comprehensive understanding of machine learning concepts and techniques.
Why This Course?
Machine learning is transforming industries and driving innovation. This course equips you with the knowledge and skills to harness the power of machine learning, whether you're looking to advance your career, work on personal projects, or simply explore this exciting field.
Prerequisites:
Basic understanding of Python programming.
No prior knowledge of machine learning is required.
Enroll Today!
Join me on this journey to become proficient in machine learning with Scikit-learn. By the end of this course, you'll have the confidence to build, evaluate, and deploy your machine learning models. Let's get started!
Who this course is for:
Anyone eager enough to learn how machine learning works and to break down the magic to reality
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