[2022] Machine Learning and Deep Learning Bootcamp in Python | Udemy


[2022] Machine Learning and Deep Learning Bootcamp in Python | Udemy
English | Size: 7.12 GB
Genre: eLearning

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

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.) K-Nearest Neighbors Classifier

what is k-nearest neighbour classifier?

non-parametric machine learning algorithms

4.) Naive Bayes Algorithm

what is the naive Bayes algorithm?

classification based on probability

cross-validation

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?

k-means clustering and the 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

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

multi-armed 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.) Serf-Driving 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 Viola-Jones Algorithm:

Viola-Jones approach in computer vision

what is sliding-windows approach

detecting faces in images and in videos

17.) Histogram of Oriented Gradients (HOG) Algorithm

how to outperform Viola-Jones 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 sliding-windows approach

region proposals and selective search algorithms

region based convolutional neural networks (C-RNNs)

fast C-RNNs

faster C-RNNs

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 non-max 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 real-time 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

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