Machine learning with many practical examples. Regression, Classification and much more

What you will learn from this Course:

• Create machine learning applications in Python as well as R
• Apply Machine Learning to own data
• You will learn Machine Learning clearly and concisely
• Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. …)
• No dry mathematics – everything explained vividly
• Use popular tools like Sklearn, and Caret
• You will know when to use which machine learning model

Requirements for this Course

• You should have programmed a little before.
• No knowledge of Python or R is required.
• All necessary tools (R, RStudio, Anaconda, …) will be installed together in the course.

Description:

AI is just truly fun when you assess genuine information. That is the reason you examine a lot of pragmatic models in this course:

• Gauge the worth of trade-in vehicles
• Compose a spam channel
• Analyze bosom disease

All code models are displayed in both programming dialects – so you can pick whether you need to see the course in Python, R, or in the two dialects!

After the course you can apply Machine Learning to your own information and settle on educated choices:

You know when which models may come into question and how to analyze them. You can investigate which segments are required, regardless of whether extra information is required, and know which information should be ready ahead of time.

This course covers the significant subjects:

• Relapse
• Arrangement

On this load of subjects, you will find out with regards to various calculations. The thoughts behind them are essentially clarified – not dry numerical equations, yet clear graphical clarifications.

We utilize normal devices (Sklearn, NLTK, caret, data. table, …), which are likewise utilized for genuine AI projects.

What do you realize?

• Relapse
• Straight Regression
• Polynomial Regression
• Arrangement
• Calculated Regression
• Guileless Bayes
• Choice trees
• Arbitrary Forest

You will likewise figure out how to utilize Machine Learning:

• Peruse in information and set it up for your model
• With the complete viable model, clarified bit by bit
• Track down the best hyper boundaries for your model
• “Boundary Tuning”
• Contrast models and one another:
• How the precision worth of a model can delude you and what can be done
• K-Fold Cross-Validation
• Coefficient of assurance

Who this course is for:

• Developers interested in Machine Learning

Course content:

• Introduction
• Setting Up The Python Environment
• Setting Up The R Environment
• Basics Machine-Learning
• Linear Regression
• Project: Linear Regression
• Train/Test
• Linear Regression With Multiple Variables
• Compare Models: Coefficient of Determination
• Practical Project:Coefficient of Determination

Learn Complete Machine Learning Bootcamp with Python. Build 5 Complete Machine Learning Real World Projects with Python.

What you will learn from this Course:

• Theory and practical implementation of linear regression using sklearn
• Theory and practical implementation of logistic regression using sklearn
• Feature selection using RFECV
• Data transformation with linear and logistic regression.
• Evaluation metrics to analyze the performance of models
• Industry relevance of linear and logistic regression
• Mathematics behind KNN, SVM and Naive Bayes algorithms
• Implementation of KNN, SVM and Naive Bayes using sklearn
• Attribute selection methods- Gini Index and Entropy
• Mathematics behind Decision trees and random forest
• Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost
• Different Algorithms for Clustering
• Different methods to deal with imbalanced data
• Correlation Filtering
• Variance Filtering
• PCA & LDA
• Content and Collaborative based filtering
• Singular Value Decomposition
• Different algorithms used for Time Series forecasting
• Case studies

Requirements for this Course:

• To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language.

Description:

Wild about Data Science and Machine Learning?

This course is an ideal fit for you.

This course will make you stride by venture into the universe of Machine Learning.

AI is the investigation of PC calculations that computerizes scientific model structure. It is a part of Artificial Intelligence dependent on the possibility that frameworks can gain from information, recognize examples and settle on choices with negligible human intercession.

AI is effectively being utilized today, maybe in a lot a bigger number of spots than one world anticipates.

It contains a ton of points and this course will cover all bit by bit.

This Machine Learning course will give you hypothetical just as useful information on Machine Learning.

This Machine Learning course is fun just as invigorating.

It will cover all normal and significant calculations and will give you the experience of dealing for certain genuine tasks.

This course will cover the accompanying points:-

1. Hypothesis and viable execution of direct relapse utilizing sklearn.
2. Hypothesis and viable execution of strategic relapse utilizing sklearn.
3. Element determination utilizing RFECV.
4. Information change with direct and strategic relapse.
5. Assessment measurements to investigate the exhibition of models
6. Industry significance of direct and strategic relapse.
7. Science behind KNN, SVM, and Naive Bayes calculations.
8. Execution of KNN, SVM, and Naive Bayes utilizing sklearn.
9. Quality determination techniques Gini Index and Entropy.
10. Science behind Decision trees and irregular backwoods.
11. Boosting calculations:- Adaboost, Gradient Boosting, and XgBoost.
12. Various calculations for bunching
13. Various strategies to manage imbalanced information.
14. Connection sifting
15. Change sifting
16. PCA and LDA
17. Content and Collaborative based sifting
18. Particular Value disintegration
19. Various calculations utilized for Time Series guaging.
20. Contextual investigations

Who this course is for:

• Anyone who want to start a career in Machine Learning.
• Students who have at least knowledge in linear algebra, calculus, statistics, probability and who want to start their journey in Machine Learning.
• Any people who want to level up their Machine Learning Knowledge.
• Software developers or programmers or Tech lover who want to change their career path to machine learning.
• Technologists who are curious about how Machine Learning works in the real world.
• Anyone who has already started their data science journey and now want to master in machine learning.
• If you have no prior coding or scripting experience, This course is completely for you. This Course also includes Python Fundamental for beginners.

Course content:

• Python Fundamentals
• Mastering Python Data Structures
• Python Functions Deep Drive
• Python For Data Science
• Data Cleaning
• Data Visualization
• Feature Engineering
• Data Processing
• Linear Regression
• Logistic Regression