Description
Course 2023 Machine Learning and Deep Learning Bootcamp in Python. Interested in machine learning, deep learning, and computer vision? Then this course is for you! This course is about the basic concepts of machine learning, deep learning, reinforcement learning, and machine learning. These topics are very relevant nowadays as these learning algorithms can be used in various fields from software development to investment banking. In each section, we will talk about the theoretical background of all these algorithms and then 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.) K-classifier for nearest neighbors
- What is the K-Nearest Neighbors Classifier?
- Nonparametric algorithms for machine learning
4.) Simple Bayes algorithm
- What is a simple Bayesian 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 sagging and strengthening?
- AdaBoost algorithm
- Combination of weak learners (swarm intelligence)
8.) Clustering algorithms
- What are clustering algorithms?
- k-means 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?
- Function selection with core
- Feature detectors
- Collect and press flat
12.) Recurrent neural networks (RNN)
- What are recurrent neural networks?
- Training 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 the ADAM algorithm
13.) Reinforcement learning
- Markov decision processes (MDPs)
- Values repetition and policy repetition
- Exploration versus exploitation problem
- The problem of multi-armed bandits
- Q-Learning and Q-Deep Learning
- Learning Tic-Tac-Toe with Cue Learning and Deep Cue Learning
###ComputerVision ###
14.) Basics of image editing:
- Theory of Computer Vision
- What are pixel intensity values?
- Complexity and kernel (filter)
- Core blurred
- Core sharpening
- Edge detection in computer vision (edge detection kernel)
15.) Surf-driving cars and lane detection
- How to use computer vision approaches in line detection
- sophisticated 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 window approach?
- Facial recognition in images and videos
17. Histogram-oriented gradient (HOG) algorithm.
- How to beat the Viola-Jones algorithm with better approaches
- How to recognize gradients and edges in an image
- Creating 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 window approach?
- Area suggestions and selective search algorithms
- Region-based convolutional neural networks (C-RNN)
- Fast C-RNNs
- Faster C-RNNs
19. You only search once for the object detection algorithm (YOLO).
- What is the YOLO approach?
- Create bounding box
- How do you recognize objects in a picture at first glance?
- Union-Intersection algorithm (IOU).
- How to maintain the most relevant bounding box under non-maximal suppression?
20.) Single Shot Multiple Box Detector (SSD) object detection algorithm
- What is the basic idea behind the SSD algorithm?
- Construction of the anchor box
- VGG16 and MobileNet architecture
- SSD implementation with real-time videos
You get lifetime access to over 150 talks, as well as slides and source code for the talks! So what are you waiting for? Learn machine learning, deep learning, and computer vision in a way that will advance your career and expand your knowledge, all in a fun and practical way!
What you will learn in the Machine Learning and Deep Learning Bootcamp in Python 2023 course
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Solve regression problems (linear regression and logistic regression)
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Solving classification problems (simple Bayes classifier, support vector machines – SVM)
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Use of neural networks (feedback neural networks, deep neural networks, convolutional neural networks and recurrent neural networks)
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The latest machine learning techniques used by companies like Google or Facebook
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Face recognition with OpenCV
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Deep Learning – Deep Neural Networks, Convolutional Neural Networks (CNNS), Recurrent Neural Networks (RNN)
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Reinforcement Learning – Q-Learning and Q-Deep Learning approaches
This course is suitable for people who
- This course is intended for beginners who are new to machine learning, deep learning, computer vision, and reinforcement learning, or for students looking for a quick refresher.
Details of Machine Learning and Deep Learning Bootcamp in Python Course 2023
- Editor: Udemy
- Lecturer: 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 11.11.2023
Prerequisites for the course “Machine Learning and Deep Learning Bootcamp in Python” 2023
- Basic Python – we will also use Panda and Numpy (we will cover the basics during implementation)
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