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