Machine Learning Using Python | Machine Learning Tutorial | Part 1- Data Preprocessing

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Machine Learning with Python & R| Machine Learning Tutorial | Machine Learning Tutorial for Beginners 🔥

Description:
Interested in the field of Machine Learning with Python & R? Then this course is for you! This course has been designed by the professional Data Scientists so that they can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. This is a 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. Learn to create Machine Learning Algorithms in Python and R from Data Science experts. Code templates included.

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Course Covered:
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
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

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Please subscribed the channel to watch complete free course of master machine learning on Python & R, make accurate predictions, make robust machine learning models, use machine learning for personal purpose, handle advanced techniques like dimensionality reduction, and build an army of powerful machine learning models and know how to combine them to solve any problem.

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