Data Science and Machine Learning Fundamentals [2025]
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This course is an exciting hands-on view of the fundamentals of Data Science and Machine Learning
Data Science and Machine Learning are developing on a massive scale. Everywhere you look in society, the world wide web, or in technology, you will find Data Science and Machine Learning algorithms working behind the scenes to analyze and optimize all aspects of our lives, businesses, and our society. Data Science and Machine Learning with Artificial Intelligence are some of the hottest and fastest-developing areas right now.
This course will teach you the fundamentals of Data Science and Machine Learning. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, and aspires to be one of the best Udemy courses in terms of education and value.
You will learn about
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Regression and Prediction with Machine Learning models using supervised learning. This course has the most complete and fundamental master-level regression analysis content packages on Udemy, with hands-on, useful practical theory, and automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.
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Classification with Machine Learning models using supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifier Ensembles and Voting Classifier Ensembles.
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Cluster Analysis with Machine Learning models using unsupervised learning. In this part of the course, you will learn about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and seven useful Machine Learning clustering algorithms ranging from hierarchical cluster models to density-based cluster models.
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The fundamentals of Data Science and Machine Learning. This course gives a very solid foundation and knowledge base for Data Science and Machine Learning jobs or studies.
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Advanced A.I. prediction models and automatic model creation. This video course includes videos where the use of very powerful algorithms for automatic model creation is taught.
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Advanced Text Mining and Automation. You will learn to mine text data and the fundamentals of Text and Emotion Mining such as Tokenization, text data preparation, spell checking, lemmatization, stemming, and classification of text data.
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Mastering Python for data handling.
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Mastering Pandas for data handling.
This course includes
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a comprehensive and easy-to-follow teaching package for Mastering Python and Pandas for data handling, which makes anyone able to learn the course contents regardless of beforehand knowledge of programming, tabulation software, Python, Pandas, Data Science, or Machine Learning.
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Learn to use Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
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an optional easy-to-follow guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able create a local installation of a Python Data Science and Machine Learning environment.
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content that will teach you many new things, regardless of if you are a beginner or an experienced Data Scientist.
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a large collection of unique content, and will teach you many new things that only can be learned from this course on Udemy.
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A complete masterclass package for Data Science and Machine Learning.
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A course structure built on a proven and professional framework for learning.
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A compact course structure and no killing time.
Is this course for you?
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This course is for you, regardless if you are a beginner or an experienced Data Scientist.
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This course is for you, regardless if you have no education or are experienced with a Ph.D.
Course requirements
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The four ways of counting (+-*/)
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Basic everyday experience with either Windows, Linux, Mac OS, or similar operating systems
After completing this course, you will have
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Knowledge about Data Science and Machine Learning theory, algorithms, methods, best practices, and tasks.
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Deep hands-on knowledge of Data Science and Machine Learning, and know how to do common Data Science and Machine Learning tasks.
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The ability to handle common Data Science and Machine Learning tasks with confidence.
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Knowledge to Master Python for Data Handling.
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Knowledge to Master Pandas for Data Handling.
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Knowledge and practical hands-on knowledge of Scikit-learn, Stats models, Matplotlib, Seaborn, and many other Python libraries.
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Detailed and deep Master knowledge of Regression Prediction, Classification, and Cluster Analysis.
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Advanced knowledge of A.I. prediction models and automatic model creation.
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Advanced Knowledge of Text Mining, Text Mining Tasks, and Emotion Mining.
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1Course introductionVídeo Aula
This video provides an overview of the course contents, a presentation of the instructor, background information about the course, and the course curriculum
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2Setup of the Anaconda Cloud NotebookVídeo Aula
This video describes the setup procedures for using the Anaconda Cloud Notebook
Using Anaconda Cloud Notebook requires internet access and an email address
Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
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3Download and installation of the Anaconda Distribution (optional)Vídeo Aula
This video describes the procedures to download and install the Anaconda Distribution for use with this course
Download requires internet access
Video is optional
Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
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4The Conda Package Management System (optional)Vídeo Aula
This video describes the Conda Package Management System
Conda requires internet access
Video is optional
Note: Conda is a speedily developing environment and this may cause minor differences in graphics and procedures
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5Overview of the first part of this sectionVídeo Aula
This video provides an overview of "Python for data handling", teaches you some Python and Data Handling theory, and presents a table of contents for Python for Data Handling as well as some basic information about the Jupyter IDE with dynamic typing, Python programs organization, and some fundamental Python language syntax
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6Python IntegersVídeo Aula
Learn to use Python Integers
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7Python FloatsVídeo Aula
Learn to use Python Floats
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8Python StringsVídeo Aula
Learn to use Python Strings
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9Python String MethodsVídeo Aula
Learn to use some Python string methods to test, search, transform, change, and manipulate string data
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10Python Strings and DateTime ObjectsVídeo Aula
Learn to use date and time data with Python's Datetime module. Learn to calculate time durations and time event data. Learn advanced knowledge about date and time data plus how computers and Python handle datetime data
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11Python Native Data Storage OverviewVídeo Aula
This video provides an overview of the part of this section about Python's data storage abstractions, the set, tuple, dictionary, and the list
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12Python SetVídeo Aula
Learn to use Python's Set
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13Python TupleVídeo Aula
Learn to use Python's native Tuple and how to unpack Tuples
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14Python DictionaryVídeo Aula
Learn to use Python's native Dictionary
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15Python ListVídeo Aula
Learn to use Python's native List
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16Data Transformers and FunctionsVídeo Aula
An overview of the contents of this subpart of the section, Python's data transformers, and functions
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17The While LoopVídeo Aula
Learn to use Python's native while-loop with some practical examples
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18The For LoopVídeo Aula
Learn to use Python's native for-loop with some practical examples
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19Python Logic OperatorsVídeo Aula
Learn to use some of Python's logic operators and conditional code branching. Use your learned knowledge to edit and tailor basic descriptive statistics at a detailed level
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20Python Functions IVídeo Aula
This video lecture describes the theoretical advantages of Python's functions
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21Python Functions IIVídeo Aula
Learn practical coding with Python's functions. You are introduced to functions and basic protections for functions. You will learn how to create functions from code-examples from earlier video lectures, and you will learn how to generalize functions up to advanced uneven-multitype-object 2-dimensional list of lists.
Learn to create your own functions!
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22Python Object Oriented Programming I : TheoryVídeo Aula
Learn Python OOP theory relevant for data handling tasks and how object-oriented data structures may affect data handling
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23Python Object Oriented Programming II: OOPVídeo Aula
Learn to code object-oriented programming with Python, and to handle Python object-oriented code and custom objects within the ambit of data handling
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24Python Object Oriented Programming III: Files and TablesVídeo Aula
Learn to save files in Python and the practical process of converting custom Python objects to tabular form and saving these into .csv, and Excel files and to load files to Pandas Data Frames
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25Python Object Oriented Programming IV: Recap and MoreVídeo Aula
This video lecture is a recap and extension of earlier video lectures. You will assemble knowledge from earlier lectures into more powerful knowledge. You will learn to construct a tabular data form with additional calculated variables and how to use the tabular data form for plotting, etc. You will learn how Data Handling fits with advanced object-oriented program structures.
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26Master Pandas for Data Handling: OverviewVídeo Aula
This video provides and introduction and overview of this section of the video course. "Master Pandas for Data Handling" is updated to current Pandas 2.2 and all known new changes in the future Pandas 3 version.
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27Pandas theory and terminologyVídeo Aula
Learn the fundamental concepts and language of the Pandas DataFrame, the Pandas Series, and the data or object content of a DataFrame/Series object.
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28Creating a DataFrame from scratchVídeo Aula
Learn to create Pandas DataFrame from scratch using Python and Pandas. You will learn how to create Pandas DataFrames using Python Dictionaries, Lists, and lots more.
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29Pandas File Handling: OverviewVídeo Aula
This video contains an overview of the Pandas File Handing part of this section.
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30Pandas File Handling: The .csv file formatVídeo Aula
Learn to load and save files from/to Pandas DataFrames from .csv files.
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31Pandas File Handling: The .xlsx file formatVídeo Aula
Learn to load and save files from/to Pandas DataFrames from .xlsx files and hierarchical .xlsx files.
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32Pandas File Handling: SQL-database filesVídeo Aula
Learn to load and save files from/to Pandas Dataframes from a SQL-database file.
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33Pandas Operations & Techniques: OverviewVídeo Aula
This video contains an overview of the Pandas Operations and Techniques part of this section.
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34Pandas Operations & Techniques: Object InspectionVídeo Aula
Learn to inspect Pandas Dataframes and Dataframe content with Pandas .info() method, Python's .type() method, and more.
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35Pandas Operations & Techniques: DataFrame InspectionVídeo Aula
Learn to inspect the contents of large-sized Pandas DataFrames. Learn to use the .head, .tail, and other general methods to inspect the contents of a DataFrame.
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36Pandas Operations & Techniques: Column SelectionsVídeo Aula
Learn to select subsets of Columns from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame.
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37Pandas Operations & Techniques: Row SelectionsVídeo Aula
Learn to select subsets of Rows from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame.
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38Pandas Operations & Techniques: Conditional SelectionsVídeo Aula
Learn to make conditional selections of subsets from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame.
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39Pandas Operations & Techniques: Scalers and Standardization.Vídeo Aula
Learn about Scalers, Normalization, and Standardization. Learn to use mean-correction, normalization, and zero-one unity-based normalization.
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40Pandas Operations & Techniques: Concatenate DataFramesVídeo Aula
Learn to Concatenate Pandas DataFrames. Learn to use Pandas .concat() function to add DataFrames together horizontally and vertically. Learn to use the .concat() function with Inner and Outer joins.
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41Pandas Operations & Techniques: Joining DataFramesVídeo Aula
Learn to join Pandas DataFrames. Learn to use Pandas DataFrames .join() method. Learn to use "left joins", "right joins", "inner joins", "outer joins", and "cross joins".
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42Pandas Operations & Techniques: Merging DataFramesVídeo Aula
Learn to merge Pandas DataFrames. Learn to use Pandas DataFrames .merge() method. Learn to use "left joins", "right joins", "inner joins", and "outer joins" to merge different DataFrames on column variables.
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43Pandas Operations & Techniques: Transpose & Pivot FunctionsVídeo Aula
Learn to Transpose and Pivot Pandas DataFrames. Learn to use the transpose, pivot, pivot_table, and melt functions.
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44Pandas Data Preparation I: Overview & workflowVídeo Aula
This video has an overview of the Data Preparation part of the course and includes a workflow for Data preparation or so-called data cleaning.
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45Pandas Data Preparation II: Edit DataFrame labelsVídeo Aula
Learn to edit Pandas DataFrame column names, index, and index labels.
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46Pandas Data Preparation III: DuplicatesVídeo Aula
Learn about Duplicates. Duplicate rows or observations may impact the quality of data products. Learn how to properly handle Duplicates with Pandas functionality.
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47Pandas Data Preparation IV: Missing Data & ImputationVídeo Aula
Learn to handle Missing data and Missing values with Pandas functionality. Learn Imputation and to augment Pandas with scikit-learn to use advanced model-based imputation of missing data.
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48Pandas Data Preparation V: Data Binnings [Extra Video]Vídeo Aula
Learn Data Binning with Pandas. Learn to use Administrative Data Binning, Algorithmic Data Binning, and subjective Data Binning. Learn to use Pandas .qcut() and .cut() functions.
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49Pandas Data Preparation VI: Indicator Features [Extra Video]Vídeo Aula
Learn to create Indicator Features or Dummy Features with Pandas
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50Pandas Data Description I: OverviewVídeo Aula
This video provides an overview of the part of this section about Pandas Data Description.
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51Pandas Data Description II: Sorting and RankingVídeo Aula
Learn to use Pandas functions for Sorting and Ranking data.
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52Pandas Data Description III: Descriptive StatisticsVídeo Aula
Learn to create useful descriptive statistics with Pandas .agg() and .describe() functions. Learn to augment Pandas functions with the powerful .apply() and .value_counts() functions.
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53Pandas Data Description IV: Crosstabulations & GroupingsVídeo Aula
Learn to create crosstabulations with Pandas .crosstab() function and to use the powerful Pandas .groupby() operation. Learn to augment these functions with a selection of Pandas functionality.
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54Pandas Data Visualization I: OverviewVídeo Aula
This video contains an overview of Pandas Data Visualization and gives an overview of the contents of this part of the section Master Pandas for Data Handling.
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55Pandas Data Visualization II: HistogramsVídeo Aula
Learn to make Histograms with Pandas, Matplotlib, and Seaborn. You will learn to make simple Histograms, advanced Histograms, multi-dimensional Histograms, and advanced Jointgrid Histograms.
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56Pandas Data Visualization III: BoxplotsVídeo Aula
Learn to make traditional and modern Boxplots with Pandas, Matplotlib, and Seaborn. You will learn to make Boxplots, Boxenplots, Violinplots, Swarmplots and to create graphs consisting of many types of boxplots.
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57Pandas Data Visualization IV: ScatterplotsVídeo Aula
Learn to make scatterplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple scatterplots, advanced scatterplots, advanced multi-scatterplots, and advanced pairplots of scatterplots.
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58Pandas Data Visualization V: Pie ChartsVídeo Aula
Learn to make Pie Charts with Pandas, Matplotlib, and support from Seaborn. You will learn to make Pie Charts, detailed Pie Charts, multiple Pie Charts, and how to properly use Pie Charts for effect.
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59Pandas Data Visualization VI: Line plotsVídeo Aula
Learn to make Lineplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple Lineplots, advanced Lineplots, advanced Line-area plots, and advanced multidimensional Line-area plots.
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60Regression, Prediction, and Supervised Learning. Section Overview (I)Vídeo Aula
This video provides an overview of this section with a table of contents. The concepts of Regression, Prediction, and Supervised Learning are described.
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61The Traditional Simple Regression Model (II)Vídeo Aula
Learn to use the traditional simple regression model, some fundamental theory and to create a regression model in a theoretically correct environment with the Scikit-learn and Statsmodels libraries
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62The Traditional Simple Regression Model (III)Vídeo Aula
Learn to use the traditional simple regression model, more fundamental theory, and tools to check and inspect model-fit-to-data, and model assumptions. Learn to create powerful residual plots with Pandas and Matplotlib, and learn to use the R-squared and Durbin-Watson statistics from the Statsmodels summary output.
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63Some practical and useful modelling concepts (IV)Vídeo Aula
Learn some practical and useful modeling concepts. Learn about Overfitting, Underfitting, and the Bias-Variance tradeoff.
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64Some practical and useful modelling concepts (V)Vídeo Aula
Learn some practical and useful modeling concepts. Learn to use Generalizations with Interpolation and extrapolation. Learn about model interpretation and learn about the fake sample or non-causality concept and about simple or advanced models.
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65Linear Multiple Regression model (VI)Vídeo Aula
Create a Linear Multiple Regression Model using correlation matrixes and heatmaps. Learn model Diagnostics and Residual Analysis using both standard package Residual plots and more advanced designed Residual plots.
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66Linear Multiple Regression model (VII)Vídeo Aula
Deepen your knowledge about Linear Multiple Regression Models. Introduction to Machine Learning Automatic Model Creation with Forward Selection and Probability-Values.
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67Multivariate Polynomial Multiple Regression models (VIII)Vídeo Aula
Learn theory about Multivariate Polynomial Regression Models and Regression terminology. Learn some theory about Automatic model creation (AI) using Machine Learning backward elimination and Regression Models
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68Multivariate Polynomial Multiple Regression models (VIIII)Vídeo Aula
Learn to code Multivariate Polynomial Multiple Regression Models combined with the Backward Elimination Feature Selection Algorithm for Machine Learning Automatic Model Creation. Learn to make Feature transformations, Residual Analysis, and some about how to plot advanced high-dimensional model predictions in low dimensional spaces, in a simplified fashion.
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69Regression Regularization, Lasso and Ridge models (X)Vídeo Aula
Learn about Regularization and to Regularize regression models using Lasso and Ridge Regression. Example regularizing an overfit Polynomial Multiple Regression Model.
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70Decision Tree Regression models (XI)Vídeo Aula
Learn Decision Tree Regression theory and to implement and regularize Decision Tree Regression models with Scikit-learn. Learn to prepare a dataset for use with Decision Tree Regression models and how to plot Decision Tree graphs and the output of Decision Tree Regression models.
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71Random Forest Regression (XII)Vídeo Aula
Learn to use Random Forest Regression / Ensembles for Prediction and Regularization. Learn to use importances for model creation and feature selection. Learn how importances change over different subsets of a dataset
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72Voting Regression (XIII)Vídeo Aula
Learn to use the Voting Ensemble Regression model for prediction. Learn to use Voting Regression to augment and modify standard Regression models for extended functionality and advanced prediction
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73Classification and Supervised Learning, overviewVídeo Aula
An overview of the Classification section of the video course. A description of the Classification theory and process
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74Logistic Regression ClassifierVídeo Aula
Learn to use the Logistic Regression Classifier with a practical example, learn to create advanced decision surface plots, use exploratory seaborn pair plots, and learn to create useful classification reports and much more…
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75The Naive Bayes ClassifierVídeo Aula
Learn to use the Naive Bayes Classifier. Learn some about Bayes theorem, conditional probability, model extrapolations, data quality effect on accuracy, practical modeling theory and more…
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76K-Nearest Neighbor Classifier (KNN) [Extra Video]Vídeo Aula
Learn to use K-Nearest Neighbor Classifier (KNN). Learn to use heuristics and graphs to determine a useful number of neighbors and learn practical hands-on classification skills for datasets with complex data structures
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77The Decision Tree ClassifierVídeo Aula
Learn to use the Decision Tree Classifier. Learn to Visualize Decision trees and to create corresponding Decision Surfaces.
Learn some tricks to enhance Decision Tree Classifiers performance and more...
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78The Random Forest ClassifierVídeo Aula
Learn to use the Random Forest Classifier. Learn some theory about Random Forest Classifiers and importances. Learn to extract Decision Trees from a Random Forest and learn to graph importances and decision surfaces
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79Linear Discriminant Analysis (LDA) [Extra Video]Vídeo Aula
Learn to use Linear Discriminant Analysis (LDA). Learn to use permutation importances for feature selection to overcome the complexity of environments with many features.
Learn to use ROC-curves, DET-curves, Precision-Recall graphs, and more…
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80The Voting ClassifierVídeo Aula
Learn to use the Voting Classifier Ensemble. Learn to use the Voting Classifier as a tool to create almost arbitrary decision surfaces, Classification models, and more...
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81OverviewVídeo Aula
This video provides an overview of the Master Cluster Analysis and Unsupervised Learning section, and some theory on Cluster Analysis and Unsupervised Learning
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82K-Means Cluster AnalysisVídeo Aula
Learn to use K-Means Cluster Analysis in a deep, practical and hands-on fashion. Learn to use practical and useful knee/elbow inertia plots and silhouette score plots. Use visualization tools to compare K-Means Cluster Analysis with subject matter expert classifications on a dataset
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83Auto-updated K-Means Cluster Analysis, introduction and simulationVídeo Aula
Extend your knowledge about K-means Cluster Analysis to Auto-updated / prototyped simulations. Learn some about the most important and defining tasks within machine learning and data science. Gain understanding about concepts such as truth, predicted truth, and model-based conditional truth.
Learn about data quality, model quality, practical data analysis, simulations and some new ways to study and graph Cluster Analysis models.
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84Density-Based Spatial Clustering of Applications with Noise (DBSCAN)Vídeo Aula
Density-Based Spatial Clustering of Applications with Noise (DBSCAN). An exploratory analysis searching for data structures in the sized California Housing Dataset
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85Four Hierarchical Clustering algorithmsVídeo Aula
Hierarchical Cluster Models. The Ward, Single, Average, and Complete linkage models. Dendrogram graphs for small-sized datasets. Exploratory analysis searching for structures in the California Housing Dataset
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86Principal Component Analysis (PCA)Vídeo Aula
Learn to use Principal Component Analysis in a practical and hands-on fashion with some theory. Learn to use Principal Components as a technique for data transformations and dimensionality reduction
Learn to make Scree plots, heatmaps, and Indices plots plus learn to use these plots for component selections and dimensionality reduction. Learn to create uncorrelated Principal Component Loading to augment supervised learning models
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87OverviewVídeo Aula
This video provides an overview of the Advanced Machine Learning Models and Tasks section
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88Artificial Neural Networks, Feedforward Networks, and the Multi-Layer PerceptronVídeo Aula
This video provides concepts and definitions for Artificial Neural Networks (ANN), Feedforward Networks, and Multi-Layer Perceptrons
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89Feedforward Multi-Layer Perceptrons for Classification tasksVídeo Aula
Learn to use Feedforward Multi-Layer Perceptrons for classification tasks. Some discussions about theory and practical applications
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90Feedforward Multi-Layer Perceptrons for Prediction tasksVídeo Aula
In this video, non-linear Feedforward Multi-Layer Perceptrons are used on the Medical Costs dataset to predict values with some practical adjustments for enhanced extraordinary Prediction performance.