
What you’ll learn
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The fundamental concepts and techniques of machine learning, including supervised and unsupervised learning
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The implementation of various machine learning algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, etc.
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Techniques for building and evaluating machine learning models, such as feature selection, feature engineering, and model evaluation techniques.
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The different types of model evaluation metrics, such as accuracy, precision, and recall and how to interpret them.
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The use of machine learning libraries such as scikit-learn and pandas to build and evaluate models.
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Hands-on experience working on real-world datasets and projects that will give students the opportunity to apply the concepts and techniques learned throughout.
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The ability to analyze, interpret and present the results of machine learning models.
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Understanding of the trade-offs between different machine learning algorithms, and their advantages and disadvantages.
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Understanding of the best practices for developing, implementing, and interpreting machine learning models.
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Skills in troubleshooting common machine learning problems and debugging machine learning models.
Can I download 2023 Python for Machine Learning: A Step-by-Step Guide course?
You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The 2023 Python for Machine Learning: A Step-by-Step Guide course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.Are there any other coupons available for this course?
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Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.