machine learning and deep learning Bootcamp in Python . we will use Panda and Numpy as well . this course is about the fundamental concepts of machine learning, deep learning, reinforcement and reinforcement . the course will cover the basics during implementations . it will be used by firms such as google or facebookFace detection and openCVTensorFlow and KerasDeep learning – deep neural networks, convolutional neural networks and recurrent neural networks . if you’re interested in machine learning or deep?.. …. … . (2022 ‘dea

What you’ll learn in [2022] Artificial Intelligence as well as Deep Discovering Bootcamp in Python

    Addressing regression problems (straight regression as well as logistic regression) Addressing classification issues (naive Bayes classifier, Assistance Vector Machines – SVMs) Making use of neural networks (feedforward neural networks, deep semantic networks, convolutional semantic networks and persistent neural networksThe most approximately date equipment finding out techniques made use of by companies such as Google or FacebookFace detection with OpenCVTensorFlow and also KerasDeep discovering – deep neural networks, convolutional neural networks (CNNS), recurrent neural networks (RNNs) Support understanding – Q understanding and also deep Q discovering strategies

Requirements

  • Fundamental Python – we will use Panda and also Numpy also (we will cover the fundamentals throughout executions)

Description

Intrigued in Machine Learning, Deep Learning and also Computer Vision? After that this program is for you!

This course has to do with the fundamental ideas of machine learning, deep knowing, reinforcement understanding and also machine learning. These topics are obtaining really warm nowadays because these discovering formulas can be made use of in numerous areas from software design to financial investment financial.

In each area we will certainly discuss the theoretical history for every one of these formulas after that we are mosting likely to apply these problems together. We will certainly utilize Python with SkLearn , Keras and TensorFlow .

### DEVICE LEARNING ###.

1.) Straight Regression.

  • recognizing direct regression design.

  • correlation and also covariance matrix.

  • linear relationships between arbitrary variables.

  • slope descent and also style matrix methods.

2.) Logistic Regression.

  • understanding logistic regression.

  • category formulas essentials.

  • maximum chance function as well as estimate.

3.) K-Nearest Neighbors Classifier.

  • what is k-nearest neighbour classifier?

  • non-parametric device finding out algorithms.

4.) Naive Bayes Formula.

  • what is the ignorant Bayes algorithm?

  • classification based upon possibility.

  • cross-validation.

  • overfitting as well as underfitting.

5.) Assistance Vector Machines ( SVMs).

  • support vector equipments (SVMs) and support vector classifiers (SVCs).

  • maximum margin classifier.

  • bit method.

6.) Decision Trees as well as Random Woodlands.

  • decision tree classifier.

  • arbitrary forest classifier.

  • combining weak learners.

7.) Nabbing and also Improving.

  • what is bagging as well as increasing?

  • AdaBoost algorithm.

  • combining weak students (wisdom of crowds).

8.) Clustering Algorithms.

  • what are clustering algorithms?

  • k-means clustering and also the arm joint method.

  • DBSCAN formula.

  • ordered clustering.

  • market segmentation analysis.

### NEURAL NETWORKS AND DEEP LEARNING ###.

9.) Feed-Forward Neural Networks.

  • single layer perceptron version.

  • feed.forward neural networks.

  • activation features.

  • backpropagation formula.

10.) Deep Neural Networks.

  • what are deep semantic networks?

  • ReLU activation functions and the disappearing slope issue.

  • training deep neural networks.

  • loss features (price functions).

11.) Convolutional Neural Networks (CNNs).

  • what are convolutional neural networks?

  • feature selection with bits.

  • feature detectors.

  • pooling as well as squashing.

12.) Reoccurring Neural Networks (RNNs).

  • what are recurrent semantic networks?

  • training recurring neural networks.

  • blowing up slopes issue.

  • LSTM and GRUs.

  • time series analysis with LSTM networks.

Numerical Optimization (in Machine Learning).

  • gradient descent formula.

  • stochastic slope descent theory and implementation.

  • ADAGrad as well as RMSProp formulas.

  • ADAM optimizer clarified.

  • ADAM formula implementation.

13.) Support Knowing.

  • Markov Decision Processes (MDPs).

  • value version as well as plan model.

  • expedition vs exploitation trouble.

  • multi-armed bandits issue.

  • Q learning as well as deep Q discovering.

  • finding out tic tac toe with Q learning and deep Q discovering.

### COMPUTER VISION ###.

14.) Picture Processing Basics:.

  • computer vision concept.

  • what are pixel intensity values.

  • convolution. and. kernels. ( filters).

  • blur kernel.

  • develop bit.

  • side discovery in computer system vision (edge discovery kernel).

15.) Serf-Driving Cars as well as Lane Discovery.

  • exactly how to utilize computer vision methods in lane detection.

  • Canny’s algorithm.

  • just how to make use of. Hough transform. to locate lines based on pixel strengths.

16.) Face Detection with Viola-Jones Formula:.

  • Viola-Jones approach in computer vision.

  • what is. sliding-windows approach.

  • identifying faces in pictures and also in videos.

17.) Pie Chart of Oriented Gradients (HOG) Formula.

  • how to outshine Viola-Jones algorithm with better techniques.

  • how to identifies. slopes. and also sides in a picture.

  • creating. pie charts. of oriented gradients.

  • using assistance vector makers (SVMs) as underlying machine learning algorithms.

18.) Convolution Neural Networks (CNNs) Based Techniques.

  • what is the issue with sliding-windows strategy.

  • region proposals as well as. selective search. formulas.

  • region based convolutional semantic networks (C-RNNs).

  • fast C-RNNs.

  • faster C-RNNs.

19.) You Only Look When (YOLO) Item Detection Algorithm.

  • what is the YOLO strategy?

  • building bounding boxes.

  • just how to detect things in an image with a single appearance?

  • crossway of union (IOU) algorithm.

  • how to maintain one of the most relevant bounding box with. non-max suppression. ?

20.) Solitary Shot MultiBox Detector (SSD) Item Discovery Algorithm SDD.

  • what is the essence behind SSD algorithm.

  • building anchor boxes.

  • VGG16 as well as MobileNet architectures.

  • implementing SSD with real-time videos.

You will obtain life time accessibility to 150+ talks plus glides and resource codes for the lectures!

This course features a. thirty days cash back ensure! If you are not satisfied in any way, you’ll obtain your money back.

So what are you waiting for? Find Out Artificial Intelligence, Deep Learning and also Computer Vision in a way that will certainly progress your profession and increase your expertise, done in a fun and practical way!

Thanks for joining the program,. allow’s start!

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
File Name :[2022] Machine Learning and Deep Learning Bootcamp in Python free download
Content Source:udemy
Genre / Category:Development
File Size :2.88 gb
Publisher :Holczer Balazs
Updated and Published:08 Aug,2022

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File name: 2022-Machine-Learning-and-Deep-Learning-Bootcamp-in-Python.rar
File Size:2.88 gb
Course duration:5 hours
Instructor Name:Holczer Balazs
Language:English
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