Published 6/2023
Created by Akhil Vydyula
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 17 Lectures ( 7h 44m ) | Size: 4.2 GB
Learn Data Science through a comprehensive course curriculum encompassing essential topics like statistics etc.
What you’ll learn
Know which Machine Learning model to choose for each type of problem
Make powerful analysis
Have a great intuition of many Machine Learning models
Master Machine Learning on Python & R
Requirements
Just some high school mathematics level.
Description
Interested in the field of Machine Learning? Then this course is for you!This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.Over 900,000 students world-wide trust this course.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 can be completed by either doing either the Python tutorials, or R tutorials, or both – Python & R. Pick the programming language that you need for your career.This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:Part 1 – Data PreprocessingPart 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest RegressionPart 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationPart 4 – Clustering: K-Means, Hierarchical ClusteringPart 5 – Association Rule Learning: Apriori, EclatPart 6 – Reinforcement Learning: Upper Confidence Bound, Thompson SamplingPart 7 – Natural Language Processing: Bag-of-words model and algorithms for NLPPart 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural NetworksPart 9 – Dimensionality Reduction: PCA, LDA, Kernel PCAPart 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoostEach section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.this course includes both Python and R code templates which you can download and use on your own projects.
Who this course is for
Anyone interested in 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 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 satisfied with their job and who want to become a Data Scientist.
Students who have at least high school knowledge in math and who want to start learning 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.
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