Summary
Summary Learning how to code
- Course
- Information Technology
- Institution
- Durban University Of Technology (DUT)
Learning how to code on Python
[Show more]
Seller
Follow
Content preview
Title: Unleashing the Power of Python in Machine LearningThe dynamic realm of machine learning, driven by algorithms and statistical models, empowers
computers to learn from data and make predictions without explicit programming. Python, with
its extensive collection of libraries, stands as a dominant language for this transformative field.
In this comprehensive study note, we embark on an exciting journey through Python's
application in machine learning, spotlighting pivotal libraries like Scikit-learn for seamless model
development and evaluation.
1. Embracing the World of Machine Learning:
Our students will embark on a captivating introduction to machine learning, unraveling the
concepts of supervised learning, unsupervised learning, and reinforcement learning. Amidst the
exploration, Python's role in facilitating the machine learning workflow will come to light.
2. Data Preprocessing with NumPy and Pandas:
Preparing data is a critical precursor to training machine learning models. Students will navigate
the art of data preprocessing using the powerful duo of NumPy and Pandas, mastering data
manipulation and handling missing values with finesse.
3. Unveiling Scikit-learn:
Scikit-learn takes center stage as a comprehensive machine learning library in Python. Its array
of algorithms for classification, regression, clustering, and dimensionality reduction will unfold
before our students, immersing them in the depths of the Scikit-learn API and its manifold
functionalities.
4. The Journey of Model Training and Evaluation:
The path to training machine learning models using Scikit-learn lies ahead. Guided by our study
note, students will learn to split data into training and testing sets, select appropriate algorithms,
and evaluate model performance using essential metrics like accuracy, precision, recall, and
F1-score.
5. Embracing Cross-Validation:
A pivotal technique emerges to assess model performance and counter overfitting.
Cross-validation takes the stage, and our students will delve into its implementation using
Scikit-learn's powerful tools.
6. Mastering Hyperparameter Tuning:
Hyperparameter tuning becomes an art to perfect, given its influence on model performance.
Techniques like grid search and random search will come to the forefront, equipping students to
optimize model hyperparameters effectively.
7. The Art of Feature Engineering:
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through EFT, credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying this summary from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller kgafedidsg. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy this summary for R109,00. You're not tied to anything after your purchase.
Can Stuvia be trusted?
4.6 stars on Google & Trustpilot (+1000 reviews)
67232 documents were sold in the last 30 days
Founded in 2010, the go-to place to buy summaries for 14 years now