Lasso and Ridge Regression, Elastic Net Regression, Linear Regression, Logistic Regression, pickle, tempfile.
Are you interested in Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!
What you’ll learn
- Tensorflow.
- Tensorboard.
- pandas.
- ReLU activation function..
- Seaborn.
- Google Colab.
- Import data from the UCI repository..
- scikit-learn.
- Logistic Regression..
- Linear Regression..
- numpy.
- pickle.
- tempfile.
- Lasso and Ridge Regression.
- Elastic Net Regression.
- Multiple and multivariate linear regression.
- TensorFlow Keras API.
Course Content
- Introduction –> 4 lectures • 13min.
- Linear Regression Theory and Practice –> 7 lectures • 1hr 19min.
- Logistic Regression Theory and Practice –> 5 lectures • 44min.
- Advanced Linear and Logistic Regression –> 9 lectures • 2hr 12min.
- Logistic Implementation with Diabetes Project –> 1 lecture • 17min.
- Thank you –> 1 lecture • 1min.
Requirements
Are you interested in Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!
A software engineer has designed this course. With the experience and knowledge I gained throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries.
I will walk you into the world of Linear and Logistic Regression. These are fundamental concepts in machine learning, deep learning, and artificial intelligence. Understanding these basic concepts makes it easier to understand more complex concepts in machine learning, deep learning, and artificial intelligence. There are no courses out there that cover Linear and Logistic Regression. However, Linear and Logistic Regression techniques are used in many applications. So it is essential to learn and understand Linear and Logistic Regression. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time, we dive deep into Linear and Logistic Regression. Throughout the brand new version of the course, we cover tons of tools and technologies, including:
- Google Colab
- Scikit-learn
- Logistic Regression.
- Linear Regression.
- Seaborn
- Lasso and Ridge Regression
- Keras.
- Pandas.
- TensorFlow.
- TensorBoard
- Matplotlib.
- Elastic Net Regression
- Import data from the UCI repository.
- Multiple and multivariate linear regression.
- TensorFlow Keras API
Moreover, the course is packed with practical exercises based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your models. There are several big projects in this course. These projects are listed below:
- Diabetes project.
- Breast Cancer Project.
- Housing project.
- MNIST Project.
By the end of the course, you will have a deep understanding of Linear and Logistic Regression, and you will get a higher chance of getting promoted or a job by knowing Linear and Logistic Regression.