成为机器学习工程师:终极指南视频教程

获得真实世界机器学习项目的实践经验，掌握最新的机器学习技术。机器学习是当今科技行业最受欢迎的技能之一。机器学习工程师负责构建和部署解决现实世界问题的机器学习模型。在本课程中，您将学习成为机器学习工程师所需的技能。我们将从介绍机器学习的基础知识开始，包括监督学习、非监督学习和强化学习。然后，我们将讨论不同类型的机器学习模型，如神经网络、决策树和支持向量机。我们还将涵盖最新的机器学习技术和框架，如TensorFlow和PyTorch。除了理论概念，我们还将为您提供真实世界机器学习项目的实践经验。你将建立一个机器学习模型来对图像进行分类，预测客户流失，并推荐产品。本课程结束时，您将具备构建、部署和维护机器学习模型所需的技能。你也将为机器学习工程的职业生涯做好准备。这门课程是为任何想学习机器学习工程的人设计的。不需要有机器学习的经验。本课程以视频形式授课，每堂课都附有幻灯片和代码示例。您还可以访问论坛，在那里您可以提出问题并与其他学员互动。如果你对学习机器学习工程感兴趣，那么这门课程就是为你准备的。Become A Machine Learning Engineer: The Ultimate Guide

立即注册，开始成为机器学习工程师的旅程！以下是参加本课程的一些好处:学习构建和部署机器学习模型所需的技能掌握最新的机器学习技术和框架获得真实世界机器学习项目的实践经验为从事机器学习工程做好准备加入不断壮大的机器学习爱好者社区

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz
语言:英语|大小:3.1 GB |时长:8小时16分钟

你会学到什么
了解机器学习、人工智能、深度学习的基础知识。
能够建立和训练机器学习模型。
能够建立和训练机器学习模型。
能够使用Python进行机器学习。
能够将机器学习模型部署到生产中。
能够使用Python中流行的深度学习库TensorFlow。
能够使用scikit-learn，这是Python中一个流行的机器学习库。

要求
愿意学习:机器学习是一个复杂的话题，所以学习者应该愿意努力学习材料。
一些编程经验:这可以在任何编程语言中使用，但是Python是机器学习最流行的选择。

课程目录：

第一部分:导言

第一讲简介

第2部分:机器学习的基础

第2讲机器学习Vs人工智能Vs神经网络

第3讲使用数据进行机器学习

第4讲机器学习的类型

第三节:张量流

第5讲张量流介绍|第一部分

第六讲探索张量流

第4部分:核心学习算法

第7讲机器学习算法

第5部分:神经网络

第8讲理解神经网络

第6部分:计算机视觉-卷积神经网络

第9讲理解CNN和计算机视觉

第7节:用递归神经网络(RNNs)进行自然语言处理

第10讲自然语言处理中递归神经网络的力量

第8部分:Q-Learning强化学习:掌握智能决策

第11讲最优决策的框架

第9节:结论和后续步骤

第12讲打造自己的ML之旅

初学者:如果你是机器学习的新手，这个课程是一个很好的起点。我会教你入门需要知道的一切，从数学和编程的基础到机器学习的最新趋势。，中级学习者:如果你有一些机器学习的经验，这个课程将帮助你的技能更上一层楼。我将涵盖更高级的主题，如深度学习和神经网络。，专业人士:如果你是一名专业人士，想要跟上机器学习的最新趋势，那么这门课程就是为你准备的。我将报道该领域的最新研究和发展，所以你可以确定你使用的是最新的技术。

Get hands-on experience with real-world machine learning projects and master the latest machine learning techniques.

What you’ll learn
Understand the basics of machine learning, artificial intelligence, and deep learning.
Be able to build and train machine learning models.
Be able to build and train machine learning models.
Be able to use Python for machine learning.
Be able to deploy machine learning models to production.
Be able to use TensorFlow, a popular deep learning library in Python.
Be able to use scikit-learn, a popular machine learning library in Python.

Requirements
A willingness to learn: Machine learning is a complex topic, so learners should be willing to put in the effort to learn the material.
Some experience with programming: This could be in any programming language, but Python is the most popular choice for machine learning.

Description
Machine learning is one of the most in-demand skills in the tech industry today. Machine learning engineers are responsible for building and deploying machine learning models that solve real-world problems. In this course, you will learn the skills you need to become a machine learning engineer.We will start by covering the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. We will then discuss the different types of machine learning models, such as neural networks, decision trees, and support vector machines. We will also cover the latest machine learning techniques and frameworks, such as TensorFlow and PyTorch.In addition to the theoretical concepts, we will also provide you with hands-on experience with real-world machine learning projects. You will build a machine learning model to classify images, predict customer churn, and recommend products.By the end of this course, you will have the skills you need to build, deploy, and maintain machine learning models. You will also be prepared for a career in machine learning engineering.This course is designed for anyone who wants to learn machine learning engineering. No prior experience with machine learning is required.The course is delivered in a video format, with each lecture accompanied by slides and code examples. You will also have access to a forum where you can ask questions and interact with other learners.If you are interested in learning machine learning engineering, then this course is for you. Enroll today and start your journey to becoming a machine learning engineer!Here are some of the benefits of taking this course:Learn the skills you need to build and deploy machine learning modelsMaster the latest machine learning techniques and frameworksGet hands-on experience with real-world machine learning projectsPrepare for a career in machine learning engineeringJoin a growing community of machine learning enthusiasts

Overview
Section 1: Introduction

Lecture 1 Introduction

Section 2: Fundamentals of Machine Learning

Lecture 2 Machine Learning Vs Artificial Intelligence Vs Neural Networks

Lecture 3 Working with Data for Machine Learning

Lecture 4 The Types of Machine Learning

Section 3: TensorFlow

Lecture 5 Introduction to TensorFlow | Part One

Lecture 6 Exploring TensorFlow

Section 4: Core Learning Algorithms

Lecture 7 Machine Learning algorithms

Section 5: Neural Networks

Lecture 8 Understanding Neural Networks

Section 6: Computer Vision – Convolutional Neural Networks

Lecture 9 Understanding CNNs & Computer Vision

Section 7: Natural Language Processing with Recurrent Neural Networks (RNNs)

Lecture 10 the Power of Recurrent Neural Networks (RNNs) for NLP

Section 8: Reinforcement Learning with Q-Learning: Mastering Intelligent Decision-Making

Lecture 11 A Framework for Optimal Decision-Making

Section 9: Conclusion and Next Steps

Lecture 12 Building your own ML journey

Beginners: If you’re new to machine learning, this course is a great place to start. I will teach you everything you need to know to get started, from the basics of mathematics and programming to the latest trends in machine learning.,Intermediate learners: If you have some experience with machine learning, this course will help you take your skills to the next level. I will cover more advanced topics, such as deep learning and neural networks.,Professionals: If you’re a professional who wants to stay up-to-date on the latest trends in machine learning, this course is for you. I will cover the latest research and development in the field, so you can be sure that you’re using the latest techniques.