• 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, 

• What you will learn
  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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Homepage:https://faculty.spagreen.net/demo/course/machine-learning-a-z-ai-python-r-chatgpt-bonus-2023
 

 

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