Full set

Interested in Machine Learning and Deep Learning ? Then this course is for you!

This course is about the fundamental concepts of machine learning, deep learning, reinforcement learning and machine learning. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking.

In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with SkLearn, Keras and TensorFlow.

### MACHINE LEARNING ###

Linear Regression

• understanding linear regression model

• correlation and covariance matrix

• linear relationships between random variables

• gradient descent and design matrix approaches

Logistic Regression

• understanding logistic regression

• classification algorithms basics

• maximum likelihood function and estimation

K-Nearest Neighbors Classifier

• what is k-nearest neighbour classifier?

• non-parametric machine learning algorithms

Naive Bayes Algorithm

• what is the naive Bayes algorithm?

• classification based on probability

• cross-validation

• overfitting and underfitting

Support Vector Machines (SVMs)

• support vector machines (SVMs) and support vector classifiers (SVCs)

• maximum margin classifier

• kernel trick

Decision Trees and Random Forests

• decision tree classifier

• random forest classifier

• combining weak learners

Bagging and Boosting

• what is bagging and boosting?

• AdaBoost algorithm

• combining weak learners (wisdom of crowds)

Clustering Algorithms

• what are clustering algorithms?

• k-means clustering and the elbow method

• DBSCAN algorithm

• hierarchical clustering

• market segmentation analysis

### NEURAL NETWORKS AND DEEP LEARNING ###

Feed-Forward Neural Networks

• single layer perceptron model

• feed.forward neural networks

• activation functions

• backpropagation algorithm

Deep Neural Networks

• what are deep neural networks?

• ReLU activation functions and the vanishing gradient problem

• training deep neural networks

• loss functions (cost functions)

Convolutional Neural Networks (CNNs)

• what are convolutional neural networks?

• feature selection with kernels

• feature detectors

• pooling and flattening

Recurrent Neural Networks (RNNs)

• what are recurrent neural networks?

• training recurrent neural networks

• exploding gradients problem

• LSTM and GRUs

• time series analysis with LSTM networks

Transformers

• word embeddings

• query, key and value matrices

• attention and attention scores

• training a transformer

• ChatGPT and transformers

Generative Adversarial Networks (GANs)

• what are GANs

• generator and discriminator

• how to train a GAN

• implementation of a simple GAN architecture

Numerical Optimization (in Machine Learning)

• gradient descent algorithm

• stochastic gradient descent theory and implementation

• ADAGrad and RMSProp algorithms

• ADAM optimizer explained

• ADAM algorithm implementation

Reinforcement Learning

• Markov Decision Processes (MDPs)

• value iteration and policy iteration

• exploration vs exploitation problem

• multi-armed bandits problem

• Q learning and deep Q learning

• learning tic tac toe with Q learning and deep Q learning

You will get lifetime access to 150+ lectures plus slides and source codes for the lectures!

This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you'll get your money back.

So what are you waiting for? Learn Machine Learning, Deep Learning in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Thanks for joining the course, let's get started!