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Data Preparation for Machine Learning

star 4.49  Beginner level 1.5 learning hrs 7.4K+ Learners

Explore how to prepare data for machine learning in this focused course. Learn techniques for cleaning, transforming, and organizing data to enhance your models' accuracy.

Key Highlights

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About this course

In the free "Preparing Data for Machine Learning" course, participants will delve into crucial techniques for optimizing machine learning models. This comprehensive course covers key topics including preventing Data Leakage, which ensures that the model training process is robust and free from unintentional biases.


Participants will also learn to build efficient pipelines to automate data preparation workflows, enhancing productivity and consistency. The module on k-fold Cross Validation introduces a reliable method for evaluating model performance using different subsets of data.


Additionally, the course addresses Data Balancing Techniques, vital for training models on datasets that accurately reflect diverse scenarios. This course is meticulously designed to equip aspiring data scientists with the skills needed to prepare data effectively, paving the way for advanced machine learning applications.

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Course outline

Case Study

This module introduces you to the case study, where you will get an opportunity to apply your theoretical knowledge of Measures of Central Tendency to a practical scenario.

Agenda For Preparing Data For Machine Learning

Data Leakage

Building Pipelines

k-fold Cross Validation

Data Balancing Techniques

Get access to the complete curriculum once you enroll in the course

Data Preparation for Machine Learning

rating icon 4.49

1.5 Hours

Beginner

7.4K+ learners enrolled so far

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Master in-demand skills & tools

Test your skills with quizzes

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Learner reviews of the Free Courses

4.49
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Reviewer Profile

4.0

India
“Preparing Data for Machine Learning”
Preparing data for machine learning is a crucial step that involves several key processes to ensure the model performs optimally. First, data collection and loading are essential, often done by gathering data from sources like databases, CSV files, or APIs, and using libraries like pandas to load them. Once data is loaded, data cleaning is critical, as missing values, incorrect data types, and outliers can skew results. Missing values can be filled with statistical measures (like mean or median) or dropped if necessary, while outliers can be identified and managed using methods.
Reviewer Profile

5.0

“i got huge knowlegde on what is machine learning and subjects related to machine learning. this is the best free course that i have found.”
I have gained extensive knowledge about machine learning and its related subjects through this course. It’s the best free resource I’ve found, providing clear explanations, practical insights, and a solid foundation in the field.
Reviewer Profile

5.0

India
“Amazing And Informative Program Course”
i really like to the things that impress me and it was such a cute voice , i am really impreessed
Reviewer Profile

5.0

India
“Preparing Data for Machine Learning Involves Key Steps”
Preparing data for machine learning involves several key steps. First, gather and clean the data by handling missing values, removing duplicates, and correcting errors. Next, transform the data by normalizing or scaling numerical features to ensure consistency. Categorical variables should be encoded using techniques like one-hot encoding or label encoding. Feature extraction or selection may be necessary to reduce dimensionality and enhance model performance.
Reviewer Profile

5.0

India
“Highlight of my Learning Experience in Data Preparation for ML”
The Data Preparation for Machine Learning course was incredibly insightful. I particularly enjoyed learning about data cleaning, handling missing values, and feature engineering. The emphasis on transforming raw data into a format suitable for modeling was eye-opening. I gained practical skills like normalization, encoding categorical variables, and understanding the importance of data quality. This foundational knowledge has been crucial for building accurate and efficient ML models.
Reviewer Profile

5.0

India
“Data preparation for machine learning”
Data preparation for Machine Learning is an excellent course that provides clear explanations and practical examples, making complex concepts easy to grasp. It effectively covers essential libraries like NumPy, pandas, and scikit-learn, offering a solid foundation for beginners and enhancing skills for advanced learners. Highly recommended!
Reviewer Profile

5.0

“You have very good curriculum with extraordinarily professor. I am really happy to be a student of your institute.”
Your course is really good with highly qualified tutor and awesome videos
Reviewer Profile
Rabia Altaf

4.0

“Effective Data Preparation for Machine Learning”
I enjoyed learning practical techniques for cleaning and preparing data for ML models.
Reviewer Profile

5.0

“Easy to learn, easy to grasp, and easy to follow”
Easy to understand and grasp the concept. Well organized content.
Reviewer Profile
Muhammad Rashid

5.0

“Good Understanding Achieved with Detailed Lecture”
I gained a thorough understanding of the topic through the detailed and well-structured lecture provided. The comprehensive explanations, real-world examples, and step-by-step approach made the concepts easy to grasp. Additionally, the lecturer's clarity and interactive teaching style helped address any doubts and provided a deeper insight into the subject. The engaging delivery and practical illustrations enhanced my learning experience, ensuring I retained the knowledge effectively and confidently.

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