Machine Learning and Deep Learning Bootcamp in Python

Machine Learning, Neural Networks, Deep Learning and Reinforcement Learning, GAN in Keras and TensorFlow

4.58 (1556 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Solving regression problems (linear regression and logistic regression)

Solving classification problems (naive Bayes classifier, Support Vector Machines - SVMs)

Using neural networks (feedforward neural networks, deep neural networks, convolutional neural networks and recurrent neural networks

The most up to date machine learning techniques used by firms such as Google or Facebook

Face detection with OpenCV

TensorFlow and Keras

Deep learning - deep neural networks, convolutional neural networks (CNNS), recurrent neural networks (RNNs)

Reinforcement learning - Q learning and deep Q learning approaches

Transformers (ChatGPT)

Course Gallery

Machine Learning and Deep Learning Bootcamp in Python – Screenshot 1 — Screenshot 1 – Machine Learning and Deep Learning Bootcamp in Python

Machine Learning and Deep Learning Bootcamp in Python – Screenshot 2 — Screenshot 2 – Machine Learning and Deep Learning Bootcamp in Python

Machine Learning and Deep Learning Bootcamp in Python – Screenshot 3 — Screenshot 3 – Machine Learning and Deep Learning Bootcamp in Python

Machine Learning and Deep Learning Bootcamp in Python – Screenshot 4 — Screenshot 4 – Machine Learning and Deep Learning Bootcamp in Python

Loading charts...

Comidoc Review

Our Verdict

This Udemy course on Machine Learning and Deep Learning Bootcamp in Python provides a well-rounded overview of various machine learning techniques, neural networks, deep learning methods, and reinforcement learning. Although some areas like code implementation and mathematical explanations could benefit from additional depth, the course excels in offering in-depth theoretical knowledge and practical examples to help learners grasp essential concepts. The 31.5 hours of content are current and cover a wide array of topics related to machine learning and deep learning, making this course a valuable resource for both beginners and those seeking a refresher.

What We Liked

Comprehensive coverage of machine learning, deep learning, and related techniques
In-depth mathematical explanations for better understanding
Well-explained theory fundamentals for industry applications
Excellent examples that help clarify concepts

Potential Drawbacks

Codes for implementation could be explained more thoroughly
Some essential code samples are missing in neural networks section
More detailed explanations of some lines of code would be helpful
Occasional lack of depth, especially in Mathematics