SUPERVISED LEARNING
Introduction, what is machine learning: definitions,
concepts and applications, coding (basic knowledge).
Linear regression model (Cost function,gradient descent,
learning rate, pseudoinverse matrix formula)
Multiple features (gradient descent for multiple linear regression)
Feature scaling and Z−score,Feature engineering,Polynomial regression
Logistic regression, Decision boundary (cost function for logistic
regression gradient descent implementation). The problem of overfitting
regularization for linear regression and logistic regression

2 UNSUPERVISED LEARNING
The clustering problem, the K-means algorithm, Optimization objective

kNN algorithm, Anomaly detection algorithm
Anomaly detection vs. supervised learning

3 MACHINE LEARNING IN PRACTICE
Hyperparameters, and training strategies. Model evaluation model
selection, overfitting, underfitting and regularization
Baseline level of performance and learning curves
Error analysis and iterative loop of ML development
Transfer learning: using data from a different task,error metrics
for skewed datasets, Trading off precision and recall

4 NEURAL NETWORKS AND DEEP LEARNING

TensorFlow and Matlab implementation
Training Details Activation functions (sigmoid, ReLu, etc)
Multiclass classification and Softmax and advanced implementations

Advanced Optimization.

Additional Layer Types Convolutional neural network height.
Deeplearning applications: Image classification and YOLO

- Abhishek Kumar Pandey, Pramod Singh Rathore, Dr. S. Balamurugan "A Practical Approach for Machine Learning and Deep Learning Algorithms Tools and Technique using MATLAB and Python", BPB Publications, INDIA ISBN: 978-93-88511-13-1
- Aurélien Géron, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Concepts, Tools, and Techniques to Build Intelligent Systems)", O'REILLY
- Ian Goodfellow_Yoshua Bengio_ Aaron Courville - Deep Learning (2016_ The MIT Press)