Machine Learning and Deep Learning Bootcamp in Python

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

What you'll learn
Solving regression problems (linear regression and logistic regression)
Solving classification problems (naive Bayes classifier, Support Vector Machines - SVMs)
Using neural networks (feedforward neural networks, deep neural networks, convolutional neural networks and recurrent neural networks
The most up to date machine learning techniques used by firms such as Google or Facebook
Face detection with OpenCV
TensorFlow and Keras
Deep learning - deep neural networks, convolutional neural networks (CNNS), recurrent neural networks (RNNs)
Reinforcement learning - Q learning and deep Q learning approaches
Transformers (ChatGPT)

Requirements
Basic Python - we will use Panda and Numpy as well (we will cover the basics during implementations)

Description
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 Regressionunderstanding linear regression modelcorrelation and covariance matrixlinear relationships between random variablesgradient descent and design matrix approachesLogistic Regressionunderstanding logistic regressionclassification algorithms basicsmaximum likelihood function and estimationK-Nearest Neighbors Classifierwhat is k-nearest neighbour classifier?non-parametric machine learning algorithmsNaive Bayes Algorithmwhat is the naive Bayes algorithm?classification based on probabilitycross-validation overfitting and underfittingSupport Vector Machines (SVMs)support vector machines (SVMs) and support vector classifiers (SVCs)maximum margin classifierkernel trickDecision Trees and Random Forestsdecision tree classifierrandom forest classifiercombining weak learnersBagging and Boostingwhat is bagging and boosting?AdaBoost algorithmcombining weak learners (wisdom of crowds)Clustering Algorithmswhat are clustering algorithms?k-means clustering and the elbow methodDBSCAN algorithmhierarchical clusteringmarket segmentation analysis### NEURAL NETWORKS AND DEEP LEARNING ###Feed-Forward Neural Networks single layer perceptron modelfeed.forward neural networksactivation functionsbackpropagation algorithmDeep Neural Networkswhat are deep neural networks?ReLU activation functions and the vanishing gradient problemtraining deep neural networksloss functions (cost functions)Convolutional Neural Networks (CNNs)what are convolutional neural networks?feature selection with kernelsfeature detectorspooling and flatteningRecurrent Neural Networks (RNNs)what are recurrent neural networks?training recurrent neural networksexploding gradients problemLSTM and GRUstime series analysis with LSTM networksTransformersword embeddingsquery, key and value matricesattention and attention scorestraining a transformerChatGPT and transformersGenerative Adversarial Networks (GANs)what are GANsgenerator and discriminatorhow to train a GANimplementation of a simple GAN architectureNumerical Optimization (in Machine Learning)gradient descent algorithmstochastic gradient descent theory and implementationADAGrad and RMSProp algorithmsADAM optimizer explainedADAM algorithm implementationReinforcement LearningMarkov Decision Processes (MDPs)value iteration and policy iterationexploration vs exploitation problemmulti-armed bandits problemQ learning and deep Q learninglearning tic tac toe with Q learning and deep Q learningYou 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!

Who this course is for:
This course is meant for newbies who are not familiar with machine learning, deep learning, computer vision and reinforcement learning or students looking for a quick refresher