Machine Learning is a technique that automates machines to build mathematical models to predict the output.
It gives computers the ability to learn without being explicitly programmed.
Machine Learning is the subset of Artificial Inteligence.
- Power BI is a major application/use of machine learning and artificial intelligence.
- Predicting housing prices or stock prices is a very common example of supervised machine learing or a very basic Regresion technique.
- Predicting the customer's liklihood of buying something.
Topics known:
1. Various Machine Learning Algorithmns
a. Linear Regression
b. Logistic Regression
c. Decision Tree
d. Support Vector Machines(SVM)
e. Naive Bayes
f. kNN
g. K-Means
h. Random Forest
2. Gradient Boosting algorithms
XGBoost
Common Loss Functions. Model performance is at the crux of any machine learning algorithm, and this is done by the use of loss functions. Choosing the right loss function can help your model learn better, and choosing the wrong loss function might lead to your model not learning anything of significance.
- Mean Squared Error(MSE)
- Mean Absolute Error(MAE)
- Hubber Loss
- Binary Cross-Entropy or Log-loss error
- Hinge Loss
Knows about the difference between parameters and hyperparameters.
Hyperparameter tuning.
Metrics to evaluate your Machine Learning Models
- Classification Accuracy
- Logarithmic Loss
- Confusion Matrix
- Area under Curve
- F1 Score
- Mean Absolute Error
- Mean Squared Error