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Data Science Courses
Udemy - Data Science and Machine Learning Fundamentals 2025 2025-9
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Sep 2025
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Overview
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Udemy - Data Science and Machine Learning Fundamentals 2025 2025-9
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Course content
Sections:
8
•
Activities:
1
•
Resources:
103
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Section 1
Introduction
Announcements
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
Section 2
Master Python for data handling
1 Overview
2 Python Integers
3 Python Floats
4 Python Strings
5 Python String Methods
6 Python Strings and DateTime Objects
7 Python Data Storage Overview
8 Python Set
9 Python Tuple
10 Python Dictionary
11 Python List
12 Data Transformers and Functions Overview
13 Python While Loop
14 Python For Loop
15 Python Conditional Code Branching and Logic Operators
16 Python Function Theory
17 Python Functions create your own functions
18 Python Object Oriented Programming Some 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
Section 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 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 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
Section 4
Master 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
Section 5
Master Classification with Machine Learning models
1 Classification and Supervised Learning overview
2 Logistic Regression Classifier
3 The Naive Bayes Classifier
4 K Nearest Neighbor Classifier KNN Extra Video
5 The Decision Tree Classifier
6 The Random Forest Classifier
7 Linear Discriminant Analysis LDA Extra Video
8 The Voting Classifier
Section 6
Master Cluster Analysis and Unsupervised Learning
1 Overview
2 K Means Cluster Analysis
3 Auto updated K Means Cluster Analysis introduction and simulation
4 Density Based Spatial Clustering of Applications with Noise DBSCAN
5 Four Hierarchical Clustering algorithms
6 Principal Component Analysis PCA
Section 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
Section 8
Text Mining and NLP
1 Text Mining and NLP introduction and overview
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
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Udemy - Data Science and Machine Learning Fundamentals 2025 2025-9
Course modified date:
7 Sept 2025
Udemy - Data Science and Machine Learning Fundamentals 2025 2025-9
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