Applied Machine Learning With Python (2022)

[LeeAndro]

Published 10/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChGenre: eLearning | Language: English | Duration: 17 lectures (3h 29m) | Size: 2.9 GB

Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Clustering: K-Means, Hierarchical Clustering
Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Basic knowledge of computer programming

Interested in the field of Machine Learning​

Then this course is for you! This course has been designed by two professional Data Scientists 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 challeg yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same , 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

Part 5 - Association Rule Learning: Apriori, Eclat

Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP

Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA

Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can and use on your own projects.

Important updates (June 2020)

CODES ALL UP TO DATE

DEEP LEARNING CODED IN TENSORFLOW 2.0

TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!

Just some high school mathematics level and Working professionals also

HomePage:

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