Machine Learning, Neural Networks, Computer Vision, Deep Learning and Reinforcement Learning in Keras and TensorFlow

RS 499

What Will I Learn?

- Solving regression problems (linear regression and logistic regression)
- Using neural networks (feedforward neural networks, deep neural networks, convolutional neural networks and recurrent neural networks
- Face detection with OpenCV
- Deep learning - deep neural networks, convolutional neural networks (CNNS), recurrent neural networks (RNNs)
- Solving classification problems (naive Bayes classifier, Support Vector Machines - SVMs)
- The most up to date machine learning techniques used by firms such as Google or Facebook
- TensorFlow and Keras
- Reinforcement learning - Q learning and deep Q learning approaches

Curriculum For This Course

6 Sections
9 Lessons
01:14:10 Hours

Machine Learning and Deep Learning Bootcamp in Python

1 Lessons
00:07:50 Hours

- Introduction 00:07:50 Preview

Environment Setup

3 Lessons
00:18:55 Hours

- Installing Phython 00:03:34
- Insatalling Pycharm 00:09:43
- Insatalling TensorFlow & Keras 00:05:38

Artificial Intelligence Basic

2 Lessons
00:28:11 Hours

- Why to learn artificial intelligence and machine learning? 00:15:20
- Types of artificial intelligence learning 00:12:51

Machine Learning

1 Lessons
00:07:52 Hours

- Machine Learning Section 00:07:52

Linear Regression

1 Lessons
00:02:34 Hours

- What is linear regression 00:02:34

Logistic Regression

1 Lessons
00:08:48 Hours

- What is logistic regression? 00:08:48

Requirements

- Basic Python - we will use Panda and Numpy as well (we will cover the basics during implementations)

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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.) 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

**Numerical Optimization (in Machine Learning)**

gradient descent algorithm

stochastic gradient descent theory and implementation

ADAGrad and RMSProp algorithms

ADAM optimizer explained

ADAM algorithm implementation

**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 imageconstructing

*histograms*of oriented gradientsusing 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*algorithmsregion 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!**

**1**Reviews**51**Courses

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Hello, my name is Victor Kercado. I'm a successful podcaster and life coach in the area of personal growth. I've been fortunate to help many people improve their lives thru mindfulness, communication, and spirituality. I look forward to sharing my knowledge with you thru courses that will inspire and motivate you to improve in all areas of your life. Thank you for listening!

RS 499

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