成为2024年的数据科学家：Python机器学习课程

《欢迎来到“成为2024年的数据科学家：Python机器学习”》是一门全面且适合初学者的课程，旨在快速引领你进入数据科学的世界。这门课程不仅仅是关于理论的学习，更是关于在真实世界中体验数据科学，由类似高级数据科学家的专业人士指导。课程中的每个环节都经过精心设计，以反映专业人士在该领域面临的日常挑战和场景。你将发现自己深入研究机器学习的核心方面，探索Python在数据分析中的实际应用，并揭开预测建模的奥秘。我们的方法独特之处在于，它将详细的视频教程与指导性的项目工作相结合，确保你学到的每个概念都通过实际应用得以强化。

随着你在课程中的进步，你将建立起Python编程的坚实基础，这对于任何渴望成为数据科学家的人来说都是必不可少的。我们深入探讨数据处理和可视化，教你如何将原始数据转化为富有洞察力和可操作性的信息。课程还涵盖了诸如统计分析、机器学习算法和模型评估等关键主题，为你提供了一个全面的技能组合。

这门课程的独特之处在于它强调真实世界的应用。你将参与模拟实际数据科学任务的动手项目工作。这种基于项目的学习方法不仅增强了你对主题的理解，还为你未来从事数据科学工作做好了准备。

在这10小时的学习之旅结束时，你不仅将学到数据科学和机器学习的基础知识，还将获得在实际情况下应用这些技能的信心。这门课程是你成为熟练数据科学家的第一步，它将为你在当今技术驱动的世界中备受追捧的知识和技能提供装备。

现在就加入“成为数据科学家：10小时内学会Python机器学习”的课程，踏上一场学习冒险，让你走上成为2024年及以后成功数据科学家之路的征程！

MP4 |视频:h264，1920×1080 |音频:AAC，44.1 KHz
语言:英语|大小:3.71 GB |时长:10小时7分钟


Practical Data Science Skills, Python, Real-World Machine Learning, Predictive Modeling, Project-Based Learning

What you’ll learn
Define the roles of Data Scientist
Model and interpret a complete machine learning project on python
Be able to answer most-asked Data Scientist interview questions
Explain the logic and all the fundamentals about Machine Learning algorithms

Requirements
No Machine Learning experience needed
High school level algebra
Very basic understanding about some programming terms (what is a ‘for loop’, what is ‘if conditions’ etc.)

Overview
Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Structure

Section 2: What is Data Science, Machine Learning and Data Science Project Process ?

Lecture 3 Let’s Begin!

Lecture 4 All about Machine Learning.Let’s make first Machine Learning model without code!

Lecture 5 Data Science Project Process

Section 3: Environment Setup

Lecture 6 Anaconda Installation – Windows

Lecture 7 Anaconda Installation – MacOS

Section 4: Toolkit Intro: Statistics and python pandas, numpy, matplotlib and seaborn Recap

Lecture 8 Basic Statistics Intro

Lecture 9 pandas Intro

Lecture 10 numpy Intro

Lecture 11 matplotlib and seaborn Intro

Section 5: Data Preprocessing with Hands-on Python

Lecture 12 First Glance to Our Dataset

Lecture 13 Reading Data into Python

Lecture 14 Detecting Data Leak and Eliminate the Leakage

Lecture 15 Null Handling

Lecture 16 Encoding

Lecture 17 Feature Engineering on Our Geoghraphical Data

Section 6: Machine Learning Classification Algorithms – All the Logic Behind Them

Lecture 18 Logistic Regression Logic

Lecture 19 Logistic Regression Key Takeaways

Lecture 20 kNN Classifier Logic and Key Takeaways

Lecture 21 Decision Tree Classifier Logic

Lecture 22 Logistic Regression, kNN and Decision Tree Algorithms Wrap-up

Lecture 23 There Are Some Inexpensive Lunches in Machine Learning

Lecture 24 Random Forest Classifier Logic – Bagging Algorithm

Lecture 25 LightGBM Logic – Boosting Algorithm

Lecture 26 XGBoost Logic

Section 7: General Modelling Concepts

Lecture 27 Train Test Split and Overfit-Underfit

Lecture 28 More on Overfit-Underfit Concept

Section 8: Classification Model Evaluation Metrics

Lecture 29 Classification Model Evaluation Metrics

Section 9: Logistic Regression Classifier and kNN Classifier – Hands-on in Python

Lecture 30 Data Recap, Separation and Train Test Split

Lecture 31 Outlier Elimination

Lecture 32 Take a Look at the Test Set Considering Outliers

Lecture 33 Feature Scaling

Lecture 34 Update the Train Labels After Outlier Elimination

Lecture 35 Logistic Regression in Python

Lecture 36 kNN Classifier in Python

Section 10: Decision Tree Classifier and Random Forest Classifier – Hands-on in Python

Lecture 37 Decision Tree Classifier in Python

Lecture 38 Random Forest Classifier in Python

Section 11: LightGBM Classifier and XGBoost Classifier – Hands-on in Python

Lecture 39 LightGBM Classifier in Python

Lecture 40 XGBoost Classifier in Python

Section 12: Classification Model Selection, Feature Importance and Final Delivery

Lecture 41 Classification Model Selection

Lecture 42 Feature Importance Concept

Lecture 43 LightGBM Classifier Feature Importance

Lecture 44 LightGBM Classifier Re-train with Top Features

Lecture 45 Final Prediction for Joined Customers

Section 13: Multi-Class Classification – Hands-on in Python

Lecture 46 MultiClass Classification Explanation

Lecture 47 MultiClass Classification in Python

Section 14: Machine Learning Regression Models – Algorithms and Evaluation

Lecture 48 Regression Introduction

Lecture 49 Linear Regression Logic

Lecture 50 kNN, Decision Tree, Random Forest, LGBM and XGBoost Regressors’ Logic

Lecture 51 Regression Model Evaluation Metrics

Section 15: Regression Models in Python – Hands-on Modelling

Lecture 52 Linear Regression in Python

Lecture 53 LightGBM Regressor in Python

Section 16: Unsupervised Learning – Clustering Logic and Python Implementation

Lecture 54 Unsupervised Learning Logic and Use Cases

Lecture 55 K Means Clustering Logic

Lecture 56 Evaluation of Clustering

Lecture 57 Do the Scaling Before KMeans

Lecture 58 KMeans Clustering in Python

Section 17: You Made It !

Lecture 59 Congratz!

People who are curious about Machine Learning,People who have less than 10 hours to learn about Machine Learning