learning all about this amazing Area

here are the bases of ML, things like

• linear Algebra
• plotting of graphs
• Calculus
• Probability
• Convolution and pooling
• advanced linear algebra
• multivariate probability
• Bayesian Probability

• Neurons, Neural Networks, Deep Neural Networks
• Binary Classification
• Multiclass Classification
• Optimization of algorithms of ML
• Error Analisys (bias-variance trade off), confusion Matrix, TP, TN, FP, FN
• keras basics
• Convolutional layers, max and avg Pooling
• Inception Network, ResNet, DenseNet
• Transfer Learning
• Object Detection (Yolo3 and DarkNet)
• Face detection
• Recurrent Neural Networks
• Time Series
• Word Embeddings
• Bleu Score
• Transformers and Attention Method

• Dimensionality Reduction PCA
• Clustering, k_means
• Markov chains
• Bayesian Optimization
• Autoencoders

• Q-learning
• Deep Q-learning

• Pandas
• API's
• Databases