Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Description

In this course, you will learn:

• Master Machine Learning in Python and R.
• Have excellent intuition for several Machine Learning models.
• Make accurate predictions.
• Make a powerful analysis.
• Create powerful machine learning models.
• Create significant added value for your business.
• Use machine learning for personal purposes.
• Handle specialized areas such as reinforcement learning, NLP, and deep learning.
• Handle sophisticated techniques such as Dimensionality Reduction.
• Know the Machine Learning model to use for each type of problem.
• Create an army of sophisticated Machine Learning models and understand how to use them to solve any problem.

Syllabus:

• Data Preprocessing in Python
• Data Preprocessing in R
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression
• Evaluating Regression Models Performance
• Regression Model Selection in Python
• Regression Model Selection in R
• Logistic Regression
• K-Nearest Neighbors (K-NN)
• Support Vector Machine (SVM)
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification
• Classification Model Selection in Python
• Evaluating Classification Models Performance
• K-Means Clustering
• Hierarchical Clustering
• Apriori
• Eclat
• Upper Confidence Bound (UCB)
• Thompson Sampling
• Artificial Neural Networks
• Convolutional Neural Networks
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA
• Model Selection
• XGBoost
• Annex: Logistic Regression (Long Explanation)