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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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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