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Dettagli torrent per "Udemy | Machine Learning Basics Building Regression Model in Python [FTU] GloDLS"

Udemy | Machine Learning Basics Building Regression Model in Python [FTU] GloDLS

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Nome:	Udemy \| Machine Learning Basics Building Regression Model in Python [FTU] GloDLS
Descrizione: Use Linear Regression to solve business problems and master the basics of Machine Learning Linear Regression in Python Created by: Start-Tech Academy Last updated: 3/2019 Language: English Caption (CC): Included - But not available for a few videos. Torrent Contains: 125 Files, 6 Folders Course Source: https://www.udemy.com/machine-learning-basics-building-regression-model-in-python/ What you'll learn • Learn how to solve real life problem using the Linear Regression technique • Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression • Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm • Understand how to interpret the result of Linear Regression model and translate them into actionable insight • Understanding of basics of statistics and concepts of Machine Learning • Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem • Learn advanced variations of OLS method of Linear Regression • Course contains a end-to-end DIY project to implement your learnings from the lectures • How to convert business problem into a Machine learning Linear Regression problem • Basic statistics using Numpy library in Python • Data representation using Seaborn library in Python • Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python Requirements • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same Description The course "Machine Learning Basics: Building Regression Model in Python" teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. What is the Linear regression technique of Machine learning? Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value. Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x). When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, the method is known as multiple linear regression. Why learn Linear regression technique of Machine learning? There are four reasons to learn Linear regression technique of Machine learning: 1. Linear Regression is the most popular machine learning technique 2. Linear Regression has fairly good prediction accuracy 3. Linear Regression is simple to implement and easy to interpret 4. It gives you a firm base to start learning other advanced techniques of Machine Learning How much time does it take to learn Linear regression technique of machine learning? Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 4 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python Understanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use Python for data Machine Learning? Understanding Python is one of the valuable skills needed for a career in Machine Learning. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. What's special about this course? The course is created on the basis of three pillars of learning: 1. Know (Study) 2. Do (Practice) 3. Review (Self feedback) Know We have created a set of concise and comprehensive videos to teach you all the Regression related skills you will need in your professional career. Do We also provide Exercises to complement the learning from the lecture video. These exercises are carefully designed to further clarify the concepts and help you with implementing the concepts on practical problems faced on-the-job. Review Check if you have learnt the concepts by executing your code and analyzing the result set. Ask questions in the discussion board if you face any difficulty. The Authors of this course have several years of corporate experience and hence have curated the course material keeping in mind the requirement of Regression analysis in today's corporate world. Who this course is for: • People pursuing a career in data science • Working Professionals beginning their Data journey • Statisticians needing more practical experience • Anyone curious to master Linear Regression from beginner to Advanced in short span of time.
YouTube Video:
Categoria:	Tutorials
Lingua:	English
Dimensione totale:	2.79 GB
Info Hash:	9D1AC7B3551F437C84BC0620165919088ABA8C60
Aggiunto di:	Prom3th3uS
Data di aggiunta:	2019-04-12 17:00:55
Stato torrent:	Torrent Verified

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File List:

File	Dimensione
1. Introduction/1. Welcome to the course!.mp4	20.60 MB
1. Introduction/1. Welcome to the course!.vtt	2.63 kB
1. Introduction/2. Course contents.mp4	63.90 MB
2. Basics of Statistics/1. Types of Data.mp4	25.86 MB
2. Basics of Statistics/1. Types of Data.vtt	4.32 kB
2. Basics of Statistics/2. Types of Statistics.mp4	13.23 MB
2. Basics of Statistics/2. Types of Statistics.vtt	2.68 kB
2. Basics of Statistics/3. Describing data Graphically.mp4	82.20 MB
2. Basics of Statistics/3. Describing data Graphically.vtt	11.30 kB
2. Basics of Statistics/4. Measures of Centers.mp4	45.67 MB
2. Basics of Statistics/4. Measures of Centers.vtt	6.42 kB
2. Basics of Statistics/5. Practice Exercise 1.html	357.00 B
2. Basics of Statistics/5.1 Exercise 1.pdf.pdf	553.83 kB
2. Basics of Statistics/6. Measures of Dispersion.mp4	28.38 MB
2. Basics of Statistics/6. Measures of Dispersion.vtt	4.72 kB
2. Basics of Statistics/7. Practice Exercise 2.html	300.00 B
2. Basics of Statistics/7.1 Exercise 2.pdf.pdf	469.93 kB
3. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4	18.61 MB
3. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.vtt	2.25 kB
3. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4	73.07 MB
3. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.vtt	7.98 kB
3. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4	51.30 MB
3. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.vtt	10.77 kB
3. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4	15.92 MB
3. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.vtt	3.50 kB
3. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4	80.63 MB
3. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.vtt	14.30 kB
3. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4	73.67 MB
3. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.vtt	14.62 kB
3. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4	54.12 MB
3. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.vtt	9.12 kB
3. Setting up Python and Jupyter Notebook/8. Working with Panda Library of Python.mp4	56.46 MB
3. Setting up Python and Jupyter Notebook/8. Working with Panda Library of Python.vtt	7.24 kB
3. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4	48.87 MB
4. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4	123.89 MB
4. Introduction to Machine Learning/1. Introduction to Machine Learning.vtt	16.30 kB
4. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4	45.26 MB
4. Introduction to Machine Learning/2. Building a Machine Learning Model.vtt	8.59 kB
4. Introduction to Machine Learning/3. Introduction to Machine learning quiz.html	166.00 B
5. Data Preprocessing/1. Gathering Business Knowledge.mp4	25.11 MB
5. Data Preprocessing/1. Gathering Business Knowledge.vtt	3.45 kB
5. Data Preprocessing/10. Outlier Treatment in Python.mp4	86.57 MB
5. Data Preprocessing/10. Outlier Treatment in Python.vtt	11.17 kB
5. Data Preprocessing/11. Project Exercise 3.html	233.00 B
5. Data Preprocessing/12. Missing Value Imputation.mp4	27.56 MB
5. Data Preprocessing/12. Missing Value Imputation.vtt	3.61 kB
5. Data Preprocessing/13. Missing Value Imputation in Python.mp4	28.59 MB
5. Data Preprocessing/13. Missing Value Imputation in Python.vtt	3.57 kB
5. Data Preprocessing/14. Project Exercise 4.html	238.00 B
5. Data Preprocessing/15. Seasonality in Data.mp4	20.89 MB
5. Data Preprocessing/15. Seasonality in Data.vtt	3.30 kB
5. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4	113.73 MB
5. Data Preprocessing/16. Bi-variate analysis and Variable transformation.vtt	16.09 kB
5. Data Preprocessing/17. Variable transformation and deletion in Python.mp4	53.40 MB
5. Data Preprocessing/17. Variable transformation and deletion in Python.vtt	6.49 kB
5. Data Preprocessing/18. Project Exercise 5.html	285.00 B
5. Data Preprocessing/19. Non-usable variables.mp4	23.94 MB
5. Data Preprocessing/19. Non-usable variables.vtt	4.80 kB
5. Data Preprocessing/2. Data Exploration.mp4	23.41 MB
5. Data Preprocessing/2. Data Exploration.vtt	3.21 kB
5. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4	40.62 MB
5. Data Preprocessing/20. Dummy variable creation Handling qualitative data.vtt	4.31 kB
5. Data Preprocessing/21. Dummy variable creation in Python.mp4	33.89 MB
5. Data Preprocessing/21. Dummy variable creation in Python.vtt	4.76 kB
5. Data Preprocessing/22. Project Exercise 6.html	202.00 B
5. Data Preprocessing/23. Correlation Analysis.mp4	81.30 MB
5. Data Preprocessing/23. Correlation Analysis.vtt	9.75 kB
5. Data Preprocessing/24. Correlation Analysis in Python.mp4	68.02 MB
5. Data Preprocessing/24. Correlation Analysis in Python.vtt	5.81 kB
5. Data Preprocessing/25. Project Exercise 7.html	288.00 B
5. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4	78.58 MB
5. Data Preprocessing/3. The Dataset and the Data Dictionary.vtt	6.89 kB
5. Data Preprocessing/3.1 House_Price.csv.csv	53.49 kB
5. Data Preprocessing/4. Importing Data in Python.mp4	32.46 MB
5. Data Preprocessing/4. Importing Data in Python.vtt	4.89 kB
5. Data Preprocessing/4.1 House_Price.csv.csv	53.49 kB
5. Data Preprocessing/5. Project exercise 1.html	431.00 B
5. Data Preprocessing/5.1 Movie_collection_train.csv.csv	43.31 kB
5. Data Preprocessing/6. Univariate analysis and EDD.mp4	27.29 MB
5. Data Preprocessing/6. Univariate analysis and EDD.vtt	3.10 kB
5. Data Preprocessing/7. EDD in Python.mp4	75.07 MB
5. Data Preprocessing/7. EDD in Python.vtt	9.00 kB
5. Data Preprocessing/8. Project Exercise 2.html	177.00 B
5. Data Preprocessing/9. Outlier Treatment.mp4	27.78 MB
5. Data Preprocessing/9. Outlier Treatment.vtt	3.99 kB
6. Linear Regression/1. The Problem Statement.mp4	14.85 MB
6. Linear Regression/1. The Problem Statement.vtt	1.44 kB
6. Linear Regression/10. Multiple Linear Regression in Python.mp4	88.13 MB
6. Linear Regression/10. Multiple Linear Regression in Python.vtt	10.81 kB
6. Linear Regression/11. Project Exercise 9.html	327.00 B
6. Linear Regression/12. Test-train split.mp4	49.13 MB
6. Linear Regression/13. Bias Variance trade-off.mp4	29.59 MB
6. Linear Regression/14. Test train split in Python.mp4	57.77 MB
6. Linear Regression/15. Linear models other than OLS.mp4	19.18 MB
6. Linear Regression/15. Linear models other than OLS.vtt	3.87 kB
6. Linear Regression/16. Subset selection techniques.mp4	87.14 MB
6. Linear Regression/16. Subset selection techniques.vtt	11.24 kB
6. Linear Regression/17. Shrinkage methods Ridge and Lasso.mp4	52.49 MB
6. Linear Regression/17. Shrinkage methods Ridge and Lasso.vtt	7.18 kB
6. Linear Regression/18. Ridge regression and Lasso in Python.mp4	252.01 MB
6. Linear Regression/19. Project Exercise 10.html	424.00 B
6. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4	50.26 MB
6. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.vtt	8.74 kB
6. Linear Regression/20. Final Project Exercise.html	301.00 B
6. Linear Regression/20.1 Movie_collection_test.csv.csv	11.72 kB
6. Linear Regression/21. Course Conclusion.html	1.63 kB
6. Linear Regression/3. Simple Linear Regression in Python.mp4	78.63 MB
6. Linear Regression/3. Simple Linear Regression in Python.vtt	9.82 kB
6. Linear Regression/4. Project Exercise 8.html	322.00 B
6. Linear Regression/5. Assessing accuracy of predicted coefficients.mp4	104.41 MB
6. Linear Regression/5. Assessing accuracy of predicted coefficients.vtt	13.99 kB
6. Linear Regression/6. Assessing Model Accuracy RSE and R squared.mp4	49.71 MB
6. Linear Regression/6. Assessing Model Accuracy RSE and R squared.vtt	7.06 kB
6. Linear Regression/7. Multiple Linear Regression.mp4	38.90 MB
6. Linear Regression/7. Multiple Linear Regression.vtt	5.08 kB
6. Linear Regression/8. The F - statistic.mp4	64.12 MB
6. Linear Regression/8. The F - statistic.vtt	7.98 kB
6. Linear Regression/9. Interpreting results of Categorical variables.mp4	27.13 MB
6. Linear Regression/9. Interpreting results of Categorical variables.vtt	4.68 kB
Discuss.FTUForum.com.html	31.89 kB
FreeCoursesOnline.Me.html	108.30 kB
FTUForum.com.html	100.44 kB
How you can help Team-FTU.txt	235.00 B
Torrent Downloaded From GloDls.to.txt	84.00 B
[TGx]Downloaded from torrentgalaxy.org.txt	524.00 B

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Udemy | Machine Learning Basics Building Regression Model in Python [FTU] GloDLS