Machine Learning, Deep Learning and Bayesian Learning

这是一门有关机器学习，深度学习（Tensorflow + PyTorch）和贝叶斯学习的课程（是将所有三个主题都放在一个地方！！！）。是Pytorch和Tensorflow都适用于深度学习。

我们首先使用pandas分析数据，然后使用Numpy从头开始实现一些算法。这些算法包括线性回归，分类和回归树（CART），随机森林和梯度增强树。

我们首先将TensorFlow用于我们的深度学习课程。这将包括前馈网络，卷积神经网络（CNN）和递归神经网络（RNN）。对于更高级的深度学习课程，我们将PyTorch与PyTorch Lightning结合使用。

我们专注于本课程的编程和数学/统计方面。这是为了确保您准备好在面试中回答这些理论问题，同时能够将机器学习付诸实践。

我们讨论的机器学习的其他其他关键领域包括无监督学习，时间序列分析和自然语言处理。Scikit学习是我们在整个课程中使用的必不可少的工具。

我们在功能设计上花费了大量时间，并确保我们的模型不会过拟合。通过分为训练和测试以及查看正确的指标来诊断机器学习（和深度学习）模型可以带来很大的不同。

我想强调一下，我们谈论的是机器学习部署，因为这是一个鲜为人知的话题。成为一名出色的数据科学家的关键在于拥有一个不会在生产中衰减的模型。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 149 lectures (13h 43m) | Size: 8.84 GB
Learn Machine Learning, Deep Learning, Bayesian Learning and Model Deployment in Python.

What you’ll learn:
Deep Learning with Tensorflow!!!
Bayesian learning with PyMC3
Data Analysis with Pandas
Algorithms from scratch using Numpy
Using Scikit-learn to its full effect
Model Deployment
Model Diagnostics
Natural Language Processing
Unsupervised Learning
Natual Language Processing with Spacy
Time series modelling with FB Prophet
Python

Requirements
Willingness to learn

Description
This is a course on Machine Learning, Deep Learning (Tensorflow + PyTorch) and Bayesian Learning (yes all 3 topics in one place!!!). Yes BOTH Pytorch and Tensorflow for Deep Learning.

We start off by analysing data using pandas, and implementing some algorithms from scratch using Numpy. These algorithms include linear regression, Classification and Regression Trees (CART), Random Forest and Gradient Boosted Trees.

We start off using TensorFlow for our Deep Learning lessons. This will include Feed Forward Networks, Convolutional Neural Nets (CNNs) and Recurrent Neural Nets (RNNs). For the more advanced Deep Learning lessons we use PyTorch with PyTorch Lightning.

We focus on both the programming and the mathematical/ statistical aspect of this course. This is to ensure that you are ready for those theoretical questions at interviews, while being able to put Machine Learning into solid practice.

Some of the other key areas in Machine Learning that we discuss include, unsupervised learning, time series analysis and Natural Language Processing. Scikit-learn is an essential tool that we use throughout the entire course.

We spend quite a bit of time on feature engineering and making sure our models don’t overfit. Diagnosing Machine Learning (and Deep Learning) models by splitting into training and testing as well as looking at the correct metric can make a world of difference.

I would like to highlight that we talk about Machine Learning Deployment, since this is a topic that is rarely talked about. The key to being a good data scientist is having a model that doesn’t decay in production.

I hope you enjoy this course and please don’t hesitate to contact me for further information.

Who this course is for
Anyone interested in Machine Learning.