[2022] Machine Learning and Deep Learning Bootcamp in Python
Machine Learning, Neural Networks, Computer Vision, Deep Learning and Reinforcement Learning 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
Requirements

Basic Python – we will use Panda and Numpy as well (we will cover the basics during implementations)
Description
Interested in Machine Learning, Deep Learning and Computer Vision? 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 ###
1.) Linear Regression
 understanding linear regression model
 correlation and covariance matrix
 linear relationships between random variables
 gradient descent and design matrix approaches
2.) Logistic Regression
 understanding logistic regression
 classification algorithms basics
 maximum likelihood function and estimation
3.) KNearest Neighbors Classifier
 what is knearest neighbour classifier?
 nonparametric machine learning algorithms
4.) Naive Bayes Algorithm
 what is the naive Bayes algorithm?
 classification based on probability
 crossvalidation
 overfitting and underfitting
5.) Support Vector Machines (SVMs)
 support vector machines (SVMs) and support vector classifiers (SVCs)
 maximum margin classifier
 kernel trick
6.) Decision Trees and Random Forests
 decision tree classifier
 random forest classifier
 combining weak learners
7.) Bagging and Boosting
 what is bagging and boosting?
 AdaBoost algorithm
 combining weak learners (wisdom of crowds)
8.) Clustering Algorithms
 what are clustering algorithms?
 kmeans clustering and the elbow method
 DBSCAN algorithm
 hierarchical clustering
 market segmentation analysis
### NEURAL NETWORKS AND DEEP LEARNING ###
9.) FeedForward Neural Networks
 single layer perceptron model
 feed.forward neural networks
 activation functions
 backpropagation 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 (CNNs)
 what are convolutional neural networks?
 feature selection with kernels
 feature detectors
 pooling and flattening
12.) 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
13.) Reinforcement Learning
 Markov Decision Processes (MDPs)
 value iteration and policy iteration
 exploration vs exploitation problem
 multiarmed bandits problem
 Q learning and deep Q learning
 learning tic tac toe with Q learning and deep Q learning
### COMPUTER VISION ###
14.) Image Processing Fundamentals:
 computer vision theory
 what are pixel intensity values
 convolution and kernels (filters)
 blur kernel
 sharpen kernel
 edge detection in computer vision (edge detection kernel)
15.) SerfDriving Cars and Lane Detection
 how to use computer vision approaches in lane detection
 Canny’s algorithm
 how to use Hough transform to find lines based on pixel intensities
16.) Face Detection with ViolaJones Algorithm:
 ViolaJones approach in computer vision
 what is slidingwindows approach
 detecting faces in images and in videos
17.) Histogram of Oriented Gradients (HOG) Algorithm
 how to outperform ViolaJones algorithm with better approaches
 how to detects gradients and edges in an image
 constructing histograms of oriented gradients
 using support vector machines (SVMs) as underlying machine learning algorithms
18.) Convolution Neural Networks (CNNs) Based Approaches
 what is the problem with slidingwindows approach
 region proposals and selective search algorithms
 region based convolutional neural networks (CRNNs)
 fast CRNNs
 faster CRNNs
19.) You Only Look Once (YOLO) Object Detection Algorithm
 what is the YOLO approach?
 constructing bounding boxes
 how to detect objects in an image with a single look?
 intersection of union (IOU) algorithm
 how to keep the most relevant bounding box with nonmax suppression?
20.) Single Shot MultiBox Detector (SSD) Object Detection Algorithm SDD
 what is the main idea behind SSD algorithm
 constructing anchor boxes
 VGG16 and MobileNet architectures
 implementing SSD with realtime videos
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 and Computer Vision 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
Created by Holczer Balazs
Last updated 1/2022
English
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Size: 7.11 GB
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