Machine Learning From Basic to Advanced
Machine Learning From Basic to Advanced
Duration: 02:59:27 | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.32 GB
Genre: eLearning | Language: English
Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.
What you'll learn
Master Machine Learning on Python
Make accurate predictions
Make robust Machine Learning models
Use Machine Learning for personal purpose
Have a great intuition of many Machine Learning models
Know which Machine Learning model to choose for each type of problem
Use SciKit-Learn for Machine Learning Tasks
Make predictions using linear regression, polynomial regression, and multiple regression
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, etc.
Requirements
Some basic python programming experience.
Basic understanding of python libraries like numpy, pasdas and matDescriptionlib.
Some high school mathematics.
Description
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.
We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:
Part 1 - Data Preprocessing
Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.
Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 4 - Clustering: K-Means, Hierarchical Clustering.
And as a bonus, this course includes Python code templates which you can download and use on your own projects.
Who this course is for:
Anyone interested in Machine Learning.
Students who have at least high school knowledge in math and who want to start learning Machine Learning.
Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science.
Any people who want to create added value to their business by using powerful Machine Learning tools.
Machine Learning From Basic to Advanced
Duration: 02:59:27 | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.32 GB
Genre: eLearning | Language: English
Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.
What you'll learn
Master Machine Learning on Python
Make accurate predictions
Make robust Machine Learning models
Use Machine Learning for personal purpose
Have a great intuition of many Machine Learning models
Know which Machine Learning model to choose for each type of problem
Use SciKit-Learn for Machine Learning Tasks
Make predictions using linear regression, polynomial regression, and multiple regression
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, etc.
Requirements
Some basic python programming experience.
Basic understanding of python libraries like numpy, pasdas and matDescriptionlib.
Some high school mathematics.
Description
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.
We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:
Part 1 - Data Preprocessing
Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.
Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 4 - Clustering: K-Means, Hierarchical Clustering.
And as a bonus, this course includes Python code templates which you can download and use on your own projects.
Who this course is for:
Anyone interested in Machine Learning.
Students who have at least high school knowledge in math and who want to start learning Machine Learning.
Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science.
Any people who want to create added value to their business by using powerful Machine Learning tools.
Download link:
Код:
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