The Complete Linear and Logistic Regression Course in Python | Udemy

Lasso and Ridge Regression, Elastic Net Regression, Linear Regression, Logistic Regression, pickle, tempfile.

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

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 the Naive Bayes Algorithm. 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 Naive Bayes Algorithm. However, Naive Bayes Algorithm 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.

Who this course is for:
Anyone interested in Machine Learning.
Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science
Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.




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