Data Science and Machine Learning Fundamentals [2024]
Data Science and Machine Learning Fundamentals [2024], available at $19.99, has an average rating of 4.88, with 101 lectures, based on 238 reviews, and has 696 subscribers.
You will learn about Knowledge about Data Science and Machine Learning theory, algorithms, methods, best practices, and tasks Deep hands-on knowledge about Data Science and Machine Learning, and know how to do common Data Science and Machine Learning tasks The ability to handle common Data Science and Machine Learning tasks with confidence Master Python for Data Handling Master Pandas for Data Handling Knowledge and practical hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and many other Python libraries Detailed and deep, Master knowledge of Regression, Regression Analysis, Prediction, Classification, and Cluster analysis Advanced knowledge of A.I. prediction models and automatic model creation Advanced Knowledge of Text Mining, Text Mining Tasks, and Emotion Mining Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources This course is ideal for individuals who are This course is for you, regardless if you are a beginner or experienced Data Scientist, regardless if you have a Ph.D., or no education or experience at all. It is particularly useful for This course is for you, regardless if you are a beginner or experienced Data Scientist, regardless if you have a Ph.D., or no education or experience at all.
Enroll now: Data Science and Machine Learning Fundamentals [2024]
Summary
Title: Data Science and Machine Learning Fundamentals [2024]
Price: $19.99
Average Rating: 4.88
Number of Lectures: 101
Number of Published Lectures: 101
Number of Curriculum Items: 101
Number of Published Curriculum Objects: 101
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Knowledge about Data Science and Machine Learning theory, algorithms, methods, best practices, and tasks
- Deep hands-on knowledge about Data Science and Machine Learning, and know how to do common Data Science and Machine Learning tasks
- The ability to handle common Data Science and Machine Learning tasks with confidence
- Master Python for Data Handling
- Master Pandas for Data Handling
- Knowledge and practical hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and many other Python libraries
- Detailed and deep, Master knowledge of Regression, Regression Analysis, Prediction, Classification, and Cluster analysis
- Advanced knowledge of A.I. prediction models and automatic model creation
- Advanced Knowledge of Text Mining, Text Mining Tasks, and Emotion Mining
- Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
Who Should Attend
- This course is for you, regardless if you are a beginner or experienced Data Scientist, regardless if you have a Ph.D., or no education or experience at all.
Target Audiences
- This course is for you, regardless if you are a beginner or experienced Data Scientist, regardless if you have a Ph.D., or no education or experience at all.
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 useCloud 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.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Course introduction
Lecture 2: Setup of the Anaconda Cloud Notebook
Lecture 3: Download and installation of the Anaconda Distribution (optional)
Lecture 4: The Conda Package Management System (optional)
Chapter 2: Master Python for data handling
Lecture 1: Overview of the first part of this section
Lecture 2: Python Integers
Lecture 3: Python Floats
Lecture 4: Python Strings I
Lecture 5: Python Strings II: Intermediate String Methods
Lecture 6: Python Strings III: DateTime Objects and Strings
Lecture 7: Python Native Data Storage Overview
Lecture 8: Python Set
Lecture 9: Python Tuple
Lecture 10: Python Dictionary
Lecture 11: Python List
Lecture 12: Data Transformers and Functions
Lecture 13: The While Loop
Lecture 14: The For Loop
Lecture 15: Python Logic Operators
Lecture 16: Python Functions I
Lecture 17: Python Functions II
Lecture 18: Python Object Oriented Programming I : Theory
Lecture 19: Python Object Oriented Programming II: OOP
Lecture 20: Python Object Oriented Programming III: Files and Tables
Lecture 21: Python Object Oriented Programming IV: Recap and More
Chapter 3: Master Pandas for Data Handling
Lecture 1: Master Pandas for Data Handling: Overview
Lecture 2: Pandas theory and terminology
Lecture 3: Creating a DataFrame from scratch
Lecture 4: Pandas File Handling: Overview
Lecture 5: Pandas File Handling: The .csv file format
Lecture 6: Pandas File Handling: The .xlsx file format
Lecture 7: Pandas File Handling: SQL-database files
Lecture 8: Pandas Operations & Techniques: Overview
Lecture 9: Pandas Operations & Techniques: Object Inspection
Lecture 10: Pandas Operations & Techniques: DataFrame Inspection
Lecture 11: Pandas Operations & Techniques: Column Selections
Lecture 12: Pandas Operations & Techniques: Row Selections
Lecture 13: Pandas Operations & Techniques: Conditional Selections
Lecture 14: Pandas Operations & Techniques: Scalers and Standardization.
Lecture 15: Pandas Operations & Techniques: Concatenate DataFrames
Lecture 16: Pandas Operations & Techniques: Joining DataFrames
Lecture 17: Pandas Operations & Techniques: Merging DataFrames
Lecture 18: Pandas Operations & Techniques: Transpose & Pivot Functions
Lecture 19: Pandas Data Preparation I: Overview & workflow
Lecture 20: Pandas Data Preparation II: Edit DataFrame labels
Lecture 21: Pandas Data Preparation III: Duplicates
Lecture 22: Pandas Data Preparation IV: Missing Data & Imputation
Lecture 23: Pandas Data Preparation V: Data Binnings [Extra Video]
Lecture 24: Pandas Data Preparation VI: Indicator Features [Extra Video]
Lecture 25: Pandas Data Description I: Overview
Lecture 26: Pandas Data Description II: Sorting and Ranking
Lecture 27: Pandas Data Description III: Descriptive Statistics
Lecture 28: Pandas Data Description IV: Crosstabulations & Groupings
Lecture 29: Pandas Data Visualization I: Overview
Lecture 30: Pandas Data Visualization II: Histograms
Lecture 31: Pandas Data Visualization III: Boxplots
Lecture 32: Pandas Data Visualization IV: Scatterplots
Lecture 33: Pandas Data Visualization V: Pie Charts
Lecture 34: Pandas Data Visualization VI: Line plots
Chapter 4: Regression and Prediction with Machine Learning models
Lecture 1: Regression, Prediction, and Supervised Learning. Section Overview (I)
Lecture 2: The Traditional Simple Regression Model (II)
Lecture 3: The Traditional Simple Regression Model (III)
Lecture 4: Some practical and useful modelling concepts (IV)
Lecture 5: Some practical and useful modelling concepts (V)
Lecture 6: Linear Multiple Regression model (VI)
Lecture 7: Linear Multiple Regression model (VII)
Lecture 8: Multivariate Polynomial Multiple Regression models (VIII)
Lecture 9: Multivariate Polynomial Multiple Regression models (VIIII)
Lecture 10: Regression Regularization, Lasso and Ridge models (X)
Lecture 11: Decision Tree Regression models (XI)
Lecture 12: Random Forest Regression (XII)
Lecture 13: Voting Regression (XIII)
Chapter 5: Classification with Machine Learning models
Lecture 1: Classification and Supervised Learning, overview
Lecture 2: Logistic Regression Classifier
Lecture 3: The Naive Bayes Classifier
Lecture 4: K-Nearest Neighbor Classifier (KNN) [Extra Video]
Lecture 5: The Decision Tree Classifier
Lecture 6: The Random Forest Classifier
Lecture 7: Linear Discriminant Analysis (LDA) [Extra Video]
Lecture 8: The Voting Classifier
Chapter 6: Cluster Analysis and Unsupervised Learning
Lecture 1: Cluster Analysis, an overview
Lecture 2: K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms
Lecture 3: Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
Lecture 4: Four Hierarchical Clustering algorithms
Chapter 7: Advanced Machine Learning models and tasks
Lecture 1: Overview
Lecture 2: Artificial Neural Networks, Feedforward Networks, and the Multi-Layer Perceptron
Lecture 3: Feedforward Multi-Layer Perceptrons for Classification tasks
Lecture 4: Feedforward Multi-Layer Perceptrons for Prediction tasks
Chapter 8: Text Mining and NLP
Lecture 1: Text Mining and NLP introduction and overview
Lecture 2: Text Mining Setup
Lecture 3: Text Mining Tasks
Lecture 4: Text Mining Process
Instructors
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Henrik Johansson
Instructor at Udemy
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 2 votes
- 3 stars: 7 votes
- 4 stars: 14 votes
- 5 stars: 214 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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