Udemy - Machine Learning and Deep Learning Bootcamp in Python Free Download

Udemy – Machine Learning and Deep Learning Bootcamp in Python
English | Tutorial | Size: 5.86 GB

Machine Learning, Neural Networks, Deep Learning and Reinforcement Learning, GAN in Keras and TensorFlow

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!

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