Stop Being a Beginner in Machine Learning in 2024 | Python

سرفصل های دوره

Master Machine Learning | Data Science using Python only 10 Hours with real-world practices - machine learning projects.

1 - Introduction

1 - Introduction

2 - Course Structure

2 - Interview-Questions.pdf

2 - telecom-customer-churn.csv

2 - telecom-zipcode-population.csv

2 - What is Data Science Machine Learning and Data Science Project Process

3 - Lets Begin

4 - All about Machine LearningLets make first Machine Learning model without code

5 - Data Science Project Process

3 - Environment Setup

6 - Anaconda Installation Windows

7 - Anaconda Installation MacOS

4 - Toolkit Intro Statistics and python pandas numpy matplotlib and seaborn Recap

8 - Basic Statistics Intro

9 - pandas Intro

10 - numpy Intro

11 - matplotlib and seaborn Intro

5 - Data Preprocessing with Handson Python

12 - First Glance to Our Dataset

13 - Reading Data into Python

14 - Detecting Data Leak and Eliminate the Leakage

15 - Null Handling

16 - Encoding

17 - Feature Engineering on Our Geoghraphical Data

6 - Machine Learning Classification Algorithms All the Logic Behind Them

18 - Logistic Regression Logic

19 - Logistic Regression Key Takeaways

20 - kNN Classifier Logic and Key Takeaways

21 - Decision Tree Classifier Logic

22 - Logistic Regression kNN and Decision Tree Algorithms Wrapup

23 - There Are Some Inexpensive Lunches in Machine Learning

24 - Random Forest Classifier Logic Bagging Algorithm

25 - LightGBM Logic Boosting Algorithm

26 - XGBoost Logic

7 - General Modelling Concepts

27 - Train Test Split and OverfitUnderfit

28 - More on OverfitUnderfit Concept

8 - Classification Model Evaluation Metrics

29 - Classification Model Evaluation Metrics

9 - Logistic Regression Classifier and kNN Classifier Handson in Python

30 - Data Recap Separation and Train Test Split

31 - Outlier Elimination

32 - Take a Look at the Test Set Considering Outliers

33 - Feature Scaling

34 - Update the Train Labels After Outlier Elimination

35 - Logistic Regression in Python

36 - kNN Classifier in Python

10 - Decision Tree Classifier and Random Forest Classifier Handson in Python

37 - Decision Tree Classifier in Python

38 - Random Forest Classifier in Python

11 - LightGBM Classifier and XGBoost Classifier Handson in Python

39 - LightGBM Classifier in Python

40 - XGBoost Classifier in Python

12 - Classification Model Selection Feature Importance and Final Delivery

41 - Classification Model Selection

42 - Feature Importance Concept

43 - LightGBM Classifier Feature Importance

44 - LightGBM Classifier Retrain with Top Features

45 - Final Prediction for Joined Customers

13 - MultiClass Classification Handson in Python

46 - MultiClass Classification Explanation

47 - MultiClass Classification in Python

14 - Machine Learning Regression Models Algorithms and Evaluation

48 - Regression Introduction

49 - Linear Regression Logic

50 - kNN Decision Tree Random Forest LGBM and XGBoost Regressors Logic

51 - Regression Model Evaluation Metrics

15 - Regression Models in Python Handson Modelling

52 - Linear Regression in Python

53 - LightGBM Regressor in Python

16 - Unsupervised Learning Clustering Logic and Python Implementation

54 - Unsupervised Learning Logic and Use Cases

55 - K Means Clustering Logic

56 - Evaluation of Clustering

57 - Do the Scaling Before KMeans

58 - KMeans Clustering in Python

17 - You Made It

59 - Congratz

59 - telecom-churn-data-science.zip

139,000 تومان

افزودن به سبد خرید

خرید دانلودی فوری

در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

تولید کننده: Udemy