Data Science and Machine Learning Fundamentals [2024]

سرفصل های دوره

Learn to master Data Science and Machine Learning Fundamentals with Python and Pandas

1. Introduction

1. Course introduction

2. Setup of the Anaconda Cloud Notebook

3. Download and installation of the Anaconda Distribution (optional)

4. The Conda Package Management System (optional)

2. Master Python for data handling

1. Overview of the first part of this section

2. Python Integers

3. Python Floats

4. Python Strings I

5. Python Strings II Intermediate String Methods

6. Python Strings III DateTime Objects and Strings

7. Python Native Data Storage Overview

8. Python Set

9. Python Tuple

10. Python Dictionary

11. Python List

12. Data Transformers and Functions

13. The While Loop

14. The For Loop

15. Python Logic Operators

16. Python Functions I

17. Python Functions II

18. Python Object Oriented Programming I Theory

19. Python Object Oriented Programming II OOP

20. Python Object Oriented Programming III Files and Tables

21. Python Object Oriented Programming IV Recap and More

Files.zip

3. Master Pandas for Data Handling

1. Master Pandas for Data Handling Overview

2. Pandas theory and terminology

3. Creating a DataFrame from scratch

4. Pandas File Handling Overview

5. Pandas File Handling The .csv file format

6. Pandas File Handling The .xlsx file format

7. Pandas File Handling SQL-database files

8. Pandas Operations & Techniques Overview

9. Pandas Operations & Techniques Object Inspection

10. Pandas Operations & Techniques DataFrame Inspection

11. Pandas Operations & Techniques Column Selections

12. Pandas Operations & Techniques Row Selections

13. Pandas Operations & Techniques Conditional Selections

14. Pandas Operations & Techniques Scalers and Standardization.

15. Pandas Operations & Techniques Concatenate DataFrames

16. Pandas Operations & Techniques Joining DataFrames

17. Pandas Operations & Techniques Merging DataFrames

18. Pandas Operations & Techniques Transpose & Pivot Functions

19. Pandas Data Preparation I Overview & workflow

20. Pandas Data Preparation II Edit DataFrame labels

21. Pandas Data Preparation III Duplicates

22. Pandas Data Preparation IV Missing Data & Imputation

23. Pandas Data Preparation V Data Binnings [Extra Video]

24. Pandas Data Preparation VI Indicator Features [Extra Video]

25. Pandas Data Description I Overview

26. Pandas Data Description II Sorting and Ranking

27. Pandas Data Description III Descriptive Statistics

28. Pandas Data Description IV Crosstabulations & Groupings

29. Pandas Data Visualization I Overview

30. Pandas Data Visualization II Histograms

31. Pandas Data Visualization III Boxplots

32. Pandas Data Visualization IV Scatterplots

33. Pandas Data Visualization V Pie Charts

34. Pandas Data Visualization VI Line plots

Files.zip

4. Regression and Prediction with Machine Learning models

1. Regression, Prediction, and Supervised Learning. Section Overview (I)

2. The Traditional Simple Regression Model (II)

3. The Traditional Simple Regression Model (III)

4. Some practical and useful modelling concepts (IV)

5. Some practical and useful modelling concepts (V)

6. Linear Multiple Regression model (VI)

7. Linear Multiple Regression model (VII)

8. Multivariate Polynomial Multiple Regression models (VIII)

9. Multivariate Polynomial Multiple Regression models (VIIII)

10. Regression Regularization, Lasso and Ridge models (X)

11. Decision Tree Regression models (XI)

12. Random Forest Regression (XII)

13. Voting Regression (XIII)

Files.zip

5. Classification with Machine Learning models

1. Classification and Supervised Learning, overview

2. Logistic Regression Classifier

3. The Naive Bayes Classifier

4. The Decision Tree Classifier

5. The Random Forest Classifier

6. Linear Discriminant Analysis (LDA) [Extra Video]

7. The Voting Classifier

Files.zip

6. Cluster Analysis and Unsupervised Learning

1. Cluster Analysis, an overview

2. K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms

3. Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

4. Four Hierarchical Clustering algorithms

Files.zip

7. Advanced Machine Learning models and tasks

1. Overview

2. Artificial Neural Networks, Feedforward Networks, and the Multi-Layer Perceptron

3. Feedforward Multi-Layer Perceptrons for Classification tasks

4. Feedforward Multi-Layer Perceptrons for Prediction tasks

Files.zip

8. Text Mining and NLP

1. Text Mining and NLP introduction

2. Text Mining Setup

3. Text Mining Tasks

4. Text Mining Process

5. Text Indexing Process

6. The Tokenization Process

7. Spelling correction and stop words

8. Lemmatization and Stemming

9. The Bag of Words Data Structure and some models

10. The TF-IDF Data Structure and some models

11. The N-grams Data Structure

12. Attention-based models and Generative Pre-trained Transformer models

13. Emotion Mining and Sentiment Analysis

Files.zip

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تولید کننده: Udemy