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[FTUForum.com] [UDEMY] Machine Learning Basics Building Regression Model In Python [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
• 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.
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FILE LIST
Filename
Size
1. Introduction/1. Welcome to the course!.mp4
20.6 MB
1. Introduction/1. Welcome to the course!.vtt
2.6 KB
1. Introduction/2. Course contents.mp4
63.9 MB
2. Basics of Statistics/1. Types of Data.mp4
25.9 MB
2. Basics of Statistics/1. Types of Data.vtt
4.3 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/3. Describing data Graphically.mp4
82.2 MB
2. Basics of Statistics/3. Describing data Graphically.vtt
11.3 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/5. Practice Exercise 1.html
357 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/7. Practice Exercise 2.html
300 B
2. Basics of Statistics/7.1 Exercise 2.pdf.pdf
469.9 KB
3. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4
18.6 MB
3. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.vtt
2.2 KB
3. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4
73.1 MB
3. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.vtt
8 KB
3. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4
51.3 MB
3. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.vtt
10.8 KB
3. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4
15.9 MB
3. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.vtt
3.5 KB
3. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4
80.6 MB
3. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.vtt
14.3 KB
3. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4
73.7 MB
3. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.vtt
14.6 KB
3. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4
54.1 MB
3. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.vtt
9.1 KB
3. Setting up Python and Jupyter Notebook/8. Working with Panda Library of Python.mp4
56.5 MB
3. Setting up Python and Jupyter Notebook/8. Working with Panda Library of Python.vtt
7.2 KB
3. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4
48.9 MB
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
16.3 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
8.6 KB
4. Introduction to Machine Learning/3. Introduction to Machine learning quiz.html
166 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/10. Outlier Treatment in Python.mp4
86.6 MB
5. Data Preprocessing/10. Outlier Treatment in Python.vtt
11.2 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/13. Missing Value Imputation in Python.mp4
28.6 MB
5. Data Preprocessing/13. Missing Value Imputation in Python.vtt
3.6 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/16. Bi-variate analysis and Variable transformation.mp4
113.7 MB
5. Data Preprocessing/16. Bi-variate analysis and Variable transformation.vtt
16.1 KB
5. Data Preprocessing/17. Variable transformation and deletion in Python.mp4
53.4 MB
5. Data Preprocessing/17. Variable transformation and deletion in Python.vtt
6.5 KB
5. Data Preprocessing/18. Project Exercise 5.html
285 B
5. Data Preprocessing/19. Non-usable variables.mp4
23.9 MB
5. Data Preprocessing/19. Non-usable variables.vtt
4.8 KB
5. Data Preprocessing/2. Data Exploration.mp4
23.4 MB
5. Data Preprocessing/2. Data Exploration.vtt
3.2 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/21. Dummy variable creation in Python.mp4
33.9 MB
5. Data Preprocessing/21. Dummy variable creation in Python.vtt
4.8 KB
5. Data Preprocessing/22. Project Exercise 6.html
202 B
5. Data Preprocessing/23. Correlation Analysis.mp4
81.3 MB
5. Data Preprocessing/23. Correlation Analysis.vtt
9.7 KB
5. Data Preprocessing/24. Correlation Analysis in Python.mp4
68 MB
5. Data Preprocessing/24. Correlation Analysis in Python.vtt
5.8 KB
5. Data Preprocessing/25. Project Exercise 7.html
288 B
5. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4
78.6 MB
5. Data Preprocessing/3. The Dataset and the Data Dictionary.vtt
6.9 KB
5. Data Preprocessing/3.1 House_Price.csv.csv
53.5 KB
5. Data Preprocessing/4. Importing Data in Python.mp4
32.5 MB
5. Data Preprocessing/4. Importing Data in Python.vtt
4.9 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/7. EDD in Python.mp4
75.1 MB
5. Data Preprocessing/7. EDD in Python.vtt
9 KB
5. Data Preprocessing/8. Project Exercise 2.html
177 B
5. Data Preprocessing/9. Outlier Treatment.mp4
27.8 MB
5. Data Preprocessing/9. Outlier Treatment.vtt
4 KB
6. Linear Regression/10. Multiple Linear Regression in Python.mp4
88.1 MB
6. Linear Regression/10. Multiple Linear Regression in Python.vtt
10.8 KB
6. Linear Regression/11. Project Exercise 9.html
327 B
6. Linear Regression/12. Test-train split.mp4
49.1 MB
6. Linear Regression/13. Bias Variance trade-off.mp4
29.6 MB
6. Linear Regression/14. Test train split in Python.mp4
57.8 MB
6. Linear Regression/15. Linear models other than OLS.mp4
19.2 MB
6. Linear Regression/15. Linear models other than OLS.vtt
3.9 KB
6. Linear Regression/16. Subset selection techniques.mp4
87.1 MB
6. Linear Regression/16. Subset selection techniques.vtt
11.2 KB
6. Linear Regression/17. Shrinkage methods Ridge and Lasso.mp4
52.5 MB
6. Linear Regression/17. Shrinkage methods Ridge and Lasso.vtt
7.2 KB
6. Linear Regression/18. Ridge regression and Lasso in Python.mp4
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