Скачать или смотреть Part 3: Machine Learning and Deep Learning using Python, Scikit-Learn, TensorFlow, and PyTorch

Full course: Machine Learning and Deep Learning using Python, Scikit-Learn, TensorFlow, and PyTorch - Practical code examples

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What to Expect:

I'll walk through practical examples of machine learning using Python, Scikit-Learn, TensorFlow, and PyTorch, showcasing real code. We'll dive into topics like data preprocessing, regression, classification, clustering, and deep learning using Pytorch and TensorFlow.

Why You Should Attend:

Get hands-on experience with popular Python libraries
Dive into real-world code examples for a practical understanding
Explore the ins and outs of data preprocessing, regression, classification, clustering, and deep learning

Whether you're just starting out or already deep into the AI world, this presentation is designed to boost your machine-learning skills.

Can't wait to share this knowledge-filled session with you!

Full course:
Part 1: Introduction
Part 2: Data preprocessing
Part 3: Regression
Part 4: Classification
Part 5: Clustering
Part 6: Dimensionality Reduction
Part 9: Model Selection and Boosting
Part 10: Association Rule Learning
Part 11: Reinforcement Learning
Part 12: Natural Language Processing
Part 13: Computer Vision
Part 14: Deep Learning

Timecodes:
0:00 - Introduction
0:15 - About Ramon Figueiredo - Instructor for the course of Machine Learning and Deep Learning with practical code examples
0:27 - What is regression analysis?
0:46 - Why is regression important?
1:16 - Example for Linear Regression using Scikit-Learn
1:50 - Example for Linear Regression using TensorFlow
2:25 - Example for Linear Regression using PyTorch
3:00 - Regression summary
3:22 - Exploring regression code examples for many regression algorithms
3:34 - Linear Regression code example
4:02 - Multiple Linear Regression code example
4:32 - Polynomial Regression code example
4:51 - Support Vector Regression code example
5:10 - Decision Tree Regressor code example
5:28 - Random Forest Regressor code example
5:51 - Regression conclusion
6:11 - Let's explore real-world examples using regression algorithms
6:20 - Using Simple Linear Regression to predict house prices based on the square footage of a house
6:34 - Using Multiple Linear Regression to predict car prices based on features like horsepower, fuel efficiency, and model year
6:40 - Using Polynomial Regression to predict stock prices based on historical data
7:02 - Using Support Vector Regression to predict house prices based on the square footage of a house
7:18 - Using Decision Tree Regressor to predict air quality index based on factors like temperature, humidity, and wind speed
7:37 - Using Random Forest Regressor to predict energy consumption in a building based on factors like temperature, occupancy, and time of day
7:57 - Regression real-world examples - How to customize the regression code example of other examples?
8:22 - What are hyperparameters and find-tuning?
9:02 - Advantages and disadvantages of many regression algorithms
9:10 - Advantages and disadvantages of the Simple Linear Regression
9:36 - Advantages and disadvantages of the Multiple Linear Regression
10:08 - Advantages and disadvantages of the Polynomial Regression
10:36 - What are overfitting and underfitting?
11:04 - Advantages and disadvantages of the Support Vector Regression
11:39 - Advantages and disadvantages of the Decision Tree Regressor
12:11 - Advantages and disadvantages of the Random Forest Regressor
12:41 - Regression ending - Next let's delve into Classification
12:57 - Thank you! Subscribe reminder. Link to download many machine learning code examples from Ramon Figueiredo's GitHub repository. Incentivation to try coding machine learning algorithms.