The Complete Machine Learning Course with Python

Do you want to be a data scientist and build machine learning projects that can solve real-life problems? If yes, then this course is perfect for you.
During the course, you will learn how to:
Set up a Python development environment correctly
Gain complete machine learning toolsets to tackle most real-world problems
Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, and when to use them
Combine multiple models with by bagging, boosting, or stacking
Make use of unsupervised machine learning algorithms such as Hierarchical clustering and k-means clustering to understand your data
Develop in Jupyter (IPython) notebook, Spyder and various IDE
Communicate visually and effectively with Matplotlib and Seaborn
Engineer new features to improve algorithm predictions
Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
Use SVM for handwriting recognition, and classification problems in general
Use decision trees to predict staff attrition
Apply the association rule to retail shopping datasets
By the end of this course, you will have a Portfolio of 12 machine learning projects that will help you land your dream job or enable you to solve real-life problems in your job or personal life with machine learning algorithms.