Description
Course 2023 Machine Learning and Deep Learning Bootcamp in Python. Interested in machine learning, deep learning and computer vision? Then this course is right for you! This course is about the basic concepts of machine learning, deep learning, reinforcement learning and machine learning. These topics are 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 of all these algorithms, then we will implement these problems together. We will use Python with SkLearn, Keras and TensorFlow.
### machine learning ###
1.) Linear regression
 Understanding the linear regression model
 Correlation and covariance matrix
 Linear relationships between random variables
 Gradient descent and design matrix approaches
2.) Logistic regression
 Understanding logistic regression
 Principles of classification algorithms
 Maximum likelihood function and estimation
3.) KNearest Neighbors Classifier
 What is the knearest neighbor classifier?
 Nonparametric machine learning algorithms
4.) Simple Bayes Algorithm
 What is Simple Bayes Algorithm?
 Classification based on probability
 Cross validation
 Too much and too little
5.) Support Vector Machines (SVM)
 Support Vector Machines (SVM) and Support Vector Classifiers (SVC)
 Maximum Margin Classifier
 Core trick
6.) Decision trees and random forests
 Decision tree classification
 Random forest classification
 Composition of weak learners
7.) Backpacking and strengthening
 What is bagging and strengthening?
 AdaBoost algorithm
 Combination of weak learners (swarm intelligence)
8.) Clustering algorithms
 What are clustering algorithms?
 kmeans clustering and elbow method
 DBSCAN algorithm
 Hierarchical clustering
 Market segmentation analysis
### Neural networks and deep learning ###
9.) Feed forward neural networks
 Single layer perceptron model
 feed.forward neural networks
 Activation functions
 Back propagation algorithm
10.) Deep Neural Networks
 What are deep neural networks?
 ReLU activation functions and the vanishing gradient problem
 Training deep neural networks
 loss functions (cost functions)
11.) Convolutional Neural Networks (CNN)
 What are convolutional neural networks?
 Feature selection with core
 Feature detectors
 Gather and flatten
12.) Recurrent Neural Networks (RNN)
 What are recurrent neural networks?
 Training of recurrent neural networks
 Explosive gradient problem
 LSTM and GRU
 Time series analysis with LSTM networks
Numerical optimization (in machine learning)
 Gradient Descent Algorithm
 Theory and implementation of stochastic gradient descent
 ADAGrad and RMSProp algorithms
 ADAM optimizer explained
 Implementation of ADAM algorithm
13.) Reinforcement learning
 Markov Decision Processes (MDPs)
 Value repetition and policy repetition
 Exploration versus exploitation problem
 The problem of multiarmed bandits
 Q learning and Q deep learning
 Learning tictactoe with cue learning and deep cue learning
### computer vision ###
14.) Basics of image processing:
 Theory of computer vision
 What are pixel intensity values?
 Complexity and kernels (filters)
 Nucleus blur
 Core sharpening
 Edge detection in computer vision (edge detection kernel)
15.) Surfdriving cars and lane detection
 How to use computer vision approaches in line detection
 Canny Algorithm
 How to use the Hough transform to find lines based on pixel intensity
16.) Face recognition with Viola Jones algorithm:
 Viola Jones approach in computer vision
 What is the sliding windows approach?
 Face recognition in images and videos
17. Algorithm of histogram oriented gradients (HOG).
 How to outperform the Viola Jones algorithm with better approaches
 How to detect gradients and edges in an image
 Making a histogram of directional gradients
 Using support vector machines (SVM) as machine learning algorithms
18. Approaches based on complexity neural networks (CNN).
 What’s wrong with the sliding windows approach?
 Area suggestions and selective search algorithms
 RegionBased Convolutional Neural Networks (CRNN)
 Fast CRNNs
 Faster CRNNs
19. You only look once object detection algorithm (YOLO).
 What is the YOLO approach?
 Making bounding boxes
 How to recognize objects in an image at a glance?
 Union Intersection Algorithm (IOU).
 How to keep the most relevant bounding box with nonmaximal suppression?
20.) SDD Object Detection Algorithm of Single Shot Multiple Box Detector (SSD)
 What is the main idea behind the SSD algorithm?
 Construction of anchor box
 VGG16 and MobileNet architecture
 SSD implementation with realtime videos
You get lifetime access to over 150 lectures plus slides and source code for the lectures! So what are you waiting for? Learn machine learning, deep learning, and computer vision in a way that will advance your career and increase your knowledge, all in a fun and practical way!
What you will learn in the 2023 Machine Learning and Deep Learning Bootcamp in Python course

Solving regression problems (linear regression and logistic regression)

Solving classification problems (simple Bayes classifier, support vector machines – SVM)

Use of neural networks (feedback neural networks, deep neural networks, convolutional neural networks and recurrent neural networks)

The most uptodate machine learning techniques used by companies such as Google or Facebook

Face recognition with OpenCV

Tensorflow and Cross

Deep Learning – Deep Neural Networks, Convolutional Neural Networks (CNNS), Recurrent Neural Networks (RNN)

Reinforcement learning – Q learning and Q deep learning approaches
This course is suitable for people who
 This course is intended for beginners unfamiliar with machine learning, deep learning, computer vision, and reinforcement learning, or students looking for a quick refresher.
Details of the 2023 Machine Learning and Deep Learning Bootcamp in Python course
 Publisher: Udemy
 teacher: Holczer Balazs
 Training level: beginner to advanced
 Training duration: 32 hours and 37 minutes
 Number of courses: 339
Headlines of the 2023 Machine Learning and Deep Learning Bootcamp in Python course on 11/2023
Prerequisites for the 2023 Machine Learning and Deep Learning Bootcamp in Python course
 Basic Python – we will use Panda and Numpy as well (we will cover the basics during implementations)
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