Complete Machine Learning Course With Python

About Complete Machine Learning Course With Python

This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include: supervised learning (generative/discriminative learning, parametric

on-parametric learning, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins);

Gain complete machine learning tool sets 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,etc. and when to use them.

Combine multiple models with by bagging, boosting or stacking

Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data

Develop in 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 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

And much much more!

No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area.

If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!

Take this course and become a machine learning engineer!