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[FTUForum.com] [UDEMY] Machine Learning Basics Building A Regression Model In R [FTU]
TORRENT SUMMARY
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• 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
• How to do basic statistical operations in R
• Advanced Linear regression techniques using GLMNET package of R
• Graphically representing data in R before and after analysis
Requirements
• Students will need to install R and R studio software but we have a separate lecture to help you install the same
Description
The course "Machine Learning Basics: Building a Regression model in R" 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 R 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 R
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 in R where we actually run each query with you.
Why use R for data Machine Learning?
Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R
1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.
2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.
4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.
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.
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FILE LIST
Filename
Size
1. Introduction/1. Welcome to the course!.mp4
19.5 MB
1. Introduction/1. Welcome to the course!.vtt
3.2 KB
1. Introduction/2. Course contents.mp4
63.5 MB
1. Introduction/2. Course contents.vtt
9.4 KB
1. Introduction/2.1 00_Introduction_01.pdf.pdf
791.5 KB
2. Basics of Statistics/1. Types of Data.mp4
41.3 MB
2. Basics of Statistics/1. Types of Data.vtt
4.3 KB
2. Basics of Statistics/1.1 01_01_Lecture_TypesOfData.pdf.pdf
177.7 KB
2. Basics of Statistics/2. Types of Statistics.mp4
13.2 MB
2. Basics of Statistics/2. Types of Statistics.vtt
2.7 KB
2. Basics of Statistics/2.1 01_02_Lecture_TypesOfStatistics.pdf.pdf
171.7 KB
2. Basics of Statistics/3. Describing the data graphically.mp4
82.2 MB
2. Basics of Statistics/3. Describing the data graphically.vtt
11.3 KB
2. Basics of Statistics/3.1 01_03_Lecture_DataSummaryandGraph.pdf.pdf
317.9 KB
2. Basics of Statistics/4. Measures of Centers.mp4
45.7 MB
2. Basics of Statistics/4. Measures of Centers.vtt
6.4 KB
2. Basics of Statistics/4.1 01_04_Lecture_Centers.pdf.pdf
313 KB
2. Basics of Statistics/5. Practice Exercise 1.html
354 B
2. Basics of Statistics/5.1 Exercise 1.pdf.pdf
553.8 KB
2. Basics of Statistics/6. Measures of Dispersion.mp4
28.4 MB
2. Basics of Statistics/6. Measures of Dispersion.vtt
4.7 KB
2. Basics of Statistics/6.1 01_05_Lecture_Dispersion.pdf.pdf
210.6 KB
2. Basics of Statistics/7. Practice Exercise 2.html
295 B
2. Basics of Statistics/7.1 Exercise 2.pdf.pdf
469.9 KB
3. Getting started with R and R studio/1. Installing R and R studio.mp4
40.8 MB
3. Getting started with R and R studio/1. Installing R and R studio.vtt
6.6 KB
3. Getting started with R and R studio/2. Basics of R and R studio.mp4
48.2 MB
3. Getting started with R and R studio/2. Basics of R and R studio.vtt
12.8 KB
3. Getting started with R and R studio/3. Packages in R.mp4
98.7 MB
3. Getting started with R and R studio/3. Packages in R.vtt
12.9 KB
3. Getting started with R and R studio/4. Inputting data part 1 Inbuilt datasets of R.mp4
46.2 MB
3. Getting started with R and R studio/4. Inputting data part 1 Inbuilt datasets of R.vtt
4.9 KB
3. Getting started with R and R studio/5. Inputting data part 2 Manual data entry.mp4
30.9 MB
3. Getting started with R and R studio/5. Inputting data part 2 Manual data entry.vtt
3.3 KB
3. Getting started with R and R studio/6. Inputting data part 3 Importing from CSV or Text files.mp4
69.1 MB
3. Getting started with R and R studio/6. Inputting data part 3 Importing from CSV or Text files.vtt
7.5 KB
3. Getting started with R and R studio/7. Creating Barplots in R.mp4
117.5 MB
3. Getting started with R and R studio/7. Creating Barplots in R.vtt
16.3 KB
3. Getting started with R and R studio/8. Creating Histograms in R.mp4
51.5 MB
3. Getting started with R and R studio/8. Creating Histograms in R.vtt
6.8 KB
4. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4
123.9 MB
4. Introduction to Machine Learning/1. Introduction to Machine Learning.vtt
21 KB
4. Introduction to Machine Learning/1.1 Lecture_machineLearning.pdf.pdf
991.6 KB
4. Introduction to Machine Learning/2. Building a Machine Learning model.mp4
45.3 MB
4. Introduction to Machine Learning/2. Building a Machine Learning model.vtt
11.5 KB
4. Introduction to Machine Learning/2.1 Lecture_machineLearning.pdf.pdf
991.6 KB
4. Introduction to Machine Learning/3. Introduction to Machine learning quiz.html
163 B
5. Data Preprocessing/1. Gathering Business Knowledge.mp4
25.1 MB
5. Data Preprocessing/1. Gathering Business Knowledge.vtt
3.4 KB
5. Data Preprocessing/1.1 03_01_PDE_Business_knowledge.pdf.pdf
153.9 KB
5. Data Preprocessing/10. Outlier Treatment in R.mp4
38 MB
5. Data Preprocessing/10. Outlier Treatment in R.vtt
3.8 KB
5. Data Preprocessing/11. Project Exercise 3.html
233 B
5. Data Preprocessing/12. Missing Value imputation.mp4
27.6 MB
5. Data Preprocessing/12. Missing Value imputation.vtt
3.6 KB
5. Data Preprocessing/12.1 04_05_PDE_Missing_value.pdf.pdf
315.7 KB
5. Data Preprocessing/13. Missing Value imputation in R.mp4
31.8 MB
5. Data Preprocessing/13. Missing Value imputation in R.vtt
3.1 KB
5. Data Preprocessing/14. Project Exercise 4.html
238 B
5. Data Preprocessing/15. Seasonality in Data.mp4
20.9 MB
5. Data Preprocessing/15. Seasonality in Data.vtt
3.3 KB
5. Data Preprocessing/15.1 04_07_PDE_Seasonality.pdf.pdf
364.1 KB
5. Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.mp4
113.8 MB
5. Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.vtt
16.1 KB
5. Data Preprocessing/16.1 04_07_Variable_Transformation.pdf.pdf
422.8 KB
5. Data Preprocessing/17. Variable transformation in R.mp4
67.9 MB
5. Data Preprocessing/17. Variable transformation in R.vtt
8 KB
5. Data Preprocessing/18. Project Exercise 5.html
286 B
5. Data Preprocessing/19. Non Usable Variables.mp4
24 MB
5. Data Preprocessing/19. Non Usable Variables.vtt
2 MB
5. Data Preprocessing/19.1 04_08_PDE_Non_Usable_var.pdf.pdf
138.3 KB
5. Data Preprocessing/2. Data Exploration.mp4
23.4 MB
5. Data Preprocessing/2. Data Exploration.vtt
3.2 KB
5. Data Preprocessing/2.1 03_02_PDE_Data_exploration.pdf.pdf
322.9 KB
5. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4
40.6 MB
5. Data Preprocessing/20. Dummy variable creation Handling qualitative data.vtt
4.3 KB
5. Data Preprocessing/20.1 04_11_Dummy_Var.pdf.pdf
163 KB
5. Data Preprocessing/21. Dummy variable creation in R.mp4
52.3 MB
5. Data Preprocessing/21. Dummy variable creation in R.vtt
4.5 KB
5. Data Preprocessing/22. Project Exercise 6.html
202 B
5. Data Preprocessing/23. Correlation Matrix and cause-effect relationship.mp4
81.3 MB
5. Data Preprocessing/23. Correlation Matrix and cause-effect relationship.vtt
9.7 KB
5. Data Preprocessing/23.1 04_10_Correlation.pdf.pdf
256.9 KB
5. Data Preprocessing/24. Correlation Matrix in R.mp4
95 MB
5. Data Preprocessing/24. Correlation Matrix in R.vtt
8.1 KB
5. Data Preprocessing/25. Project Exercise 7.html
288 B
5. Data Preprocessing/3. The Data and the Data Dictionary.mp4
78.6 MB
5. Data Preprocessing/3. The Data and the Data Dictionary.vtt
6.9 KB
5. Data Preprocessing/3.1 House_Price.csv.csv
53.5 KB
5. Data Preprocessing/3.2 03_03_PDE_Raw_Data_Analysis_Uni.pdf.pdf
332 KB
5. Data Preprocessing/4. Importing the dataset into R.mp4
16 MB
5. Data Preprocessing/4. Importing the dataset into R.vtt
2.3 KB
5. Data Preprocessing/4.1 House_Price.csv.csv
53.5 KB
5. Data Preprocessing/5. Project Exercise 1.html
431 B
5. Data Preprocessing/5.1 Movie_collection_train.csv.csv
43.3 KB
5. Data Preprocessing/6. Univariate Analysis and EDD.mp4
27.3 MB
5. Data Preprocessing/6. Univariate Analysis and EDD.vtt
3.1 KB
5. Data Preprocessing/6.1 03_04_PDE_Univariate_Analysis_Uni.pdf.pdf
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