# Machine Learning

__1. Maths:__

Linear Algebra (Matrix, Vector), Statistics, Probability

__2. Learn Python & its Libraries:__

Numpy, Pandas

__3. Learn ML Algorithms:__

Supervised vs Unsupervised vs Reinforcement, Linear Regression, Logistic Regression, Clustering, KNN (K Nearest Neighbours), SVM (Support Vector Machine), Decision Trees, Random Forests, Overfitting, Underfitting, Regularization, Gradient Descent, Slope, Confusion Matrix.

__4. Data Preprocessing (for higher accuracy):__

Handling Null Values, Standardization, Handling Categorical Values, One-Hot Encoding, Feature Scaling.

__5. Learn ML libraries:__

Scikit learn, Matplotlib, Tensorflow for DL.

__6. Exploring projects on Github__