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TensorFlow Beginner: Real World Applications
Learn to solve real-world problems using machine learning techniques like classification, Pandas data processing, and Seaborn visualizationPreview the first lesson free — get full access to all 4 lessons.
Beginner Provider : Josh Turner 4 Lessons · 18m in Arabic, German, English, Spanish, French, Portuguese, Chinese
Course Description
In these lessons, you will learn how to apply machine learning techniques to solve complex real-world problems in various domains such as finance and biology. You will learn about classification models and how they can be used to solve challenges such as stress/strain analysis for a dam, as a notable example. You will also discover how to use Pandas for data processing and Seaborn for flexible data visualization, enabling you to analyze and interpret large datasets effectively.
Additionally, you will explore how to create training and testing datasets using the train_test_split() function and evaluate model performance using metrics like accuracy as well as confusion matrices.
By gaining familiarity with techniques such as classification, Pandas data processing, and Seaborn visualization, you will greatly enhance your real-world abilities across multiple industries and areas of practice.
What You'll Learn
- Apply machine learning techniques to solve real-world problems in domains such as finance and biology
- Build and apply classification models to challenges like stress/strain analysis for a dam, and interpret the results
- Process and analyze large datasets using Pandas
- Visualize data patterns and relationships using Seaborn
- Create training and testing datasets using the train_test_split() function
- Evaluate model performance using metrics like accuracy and confusion matrices
Key Takeaways
- Machine learning techniques can be applied to solve complex real-world problems across domains such as finance and biology.
- Classification models can address challenges such as stress/strain analysis for a dam.
- Pandas enables effective processing, analysis, and interpretation of large datasets.
- Seaborn provides flexible data visualization for exploring data patterns and relationships.
- Model performance can be evaluated using metrics like accuracy and confusion matrices, with datasets split via the train_test_split() function.
Frequently Asked Questions
What will I learn in this course?
You will learn to apply machine learning techniques to real-world problems, build and interpret classification models, process data with Pandas, and visualize data using Seaborn. The course also covers creating training and testing datasets with train_test_split() and evaluating models using accuracy and confusion matrices.
What real-world example does the course use for classification?
It uses stress/strain analysis for a dam as a notable example of applying classification models to real-world challenges.
Which tools and skills does this course cover?
The course covers TensorFlow, machine learning and machine learning methods, machine learning model training, training datasets, Pandas for data processing, Seaborn for visualization, and Azure Machine Learning.
What topics are covered in the lessons?
The lessons cover Real World Problems, the Classification Data Set, training a model, and testing the classification data set.
