Machine Learning and Deep Learning Bootcamp in Python

Machine Learning and Deep Learning Bootcamp in Python, available at $124.99, has an average rating of 4.54, with 345 lectures, 24 quizzes, based on 1435 reviews, and has 15474 subscribers.

You will learn about 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 This course is ideal for individuals who are 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 It is particularly useful 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.

Enroll now: Machine Learning and Deep Learning Bootcamp in Python

Summary

Title: Machine Learning and Deep Learning Bootcamp in Python

Price: $124.99

Average Rating: 4.54

Number of Lectures: 345

Number of Quizzes: 24

Number of Published Lectures: 340

Number of Published Quizzes: 24

Number of Curriculum Items: 369

Number of Published Curriculum Objects: 364

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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
convolutionand 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 histogramsof 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 searchalgorithms
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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Environment Setup

Lecture 1: Installing Python

Lecture 2: Installing PyCharm

Lecture 3: Installing TensorFlow and Keras

Chapter 3: Artificial Intelligence Basics

Lecture 1: Why to learn artificial intelligence and machine learning?

Lecture 2: Types of artificial intelligence learning

Lecture 3: Fundamentals of statistics

Chapter 4: ### MACHINE LEARNING ###

Lecture 1: Machine learning section

Chapter 5: Linear Regression

Lecture 1: What is linear regression?

Lecture 2: Linear regression theory – optimization

Lecture 3: Linear regression theory – gradient descent

Lecture 4: Linear regression implementation I

Lecture 5: Linear regression implementation II

Lecture 6: Mathematical formulation of linear regression

Chapter 6: Logistic Regression

Lecture 1: What is logistic regression?

Lecture 2: Logistic regression and maximum likelihood estimation

Lecture 3: Logistic regression example I – sigmoid function

Lecture 4: Logistic regression example II- credit scoring

Lecture 5: Logistic regression example III – credit scoring

Lecture 6: Mathematical formulation of logistic regression

Chapter 7: Cross Validation

Lecture 1: What is cross validation?

Lecture 2: Cross validation example

Chapter 8: K-Nearest Neighbor Classifier

Lecture 1: What is the k-nearest neighbor classifier?

Lecture 2: Concept of lazy learning

Lecture 3: Distance metrics – Euclidean-distance

Lecture 4: Bias and variance trade-off

Lecture 5: K-nearest neighbor implementation I

Lecture 6: K-nearest neighbor implementation II

Lecture 7: K-nearest neighbor implementation III

Lecture 8: Mathematical formulation of k-nearest neighbor classifier

Chapter 9: Naive Bayes Classifier

Lecture 1: What is the naive Bayes classifier?

Lecture 2: Naive Bayes classifier illustration

Lecture 3: Naive Bayes classifier implementation

Lecture 4: What is text clustering?

Lecture 5: Text clustering – inverse document frequency (TF-IDF)

Lecture 6: Naive Bayes example – clustering news

Lecture 7: Mathematical formulation of naive Bayes classifier

Chapter 10: Support Vector Machines (SVMs)

Lecture 1: What are Support Vector Machines (SVMs)?

Lecture 2: Linearly separable problems

Lecture 3: Non-linearly separable problems

Lecture 4: Kernel functions

Lecture 5: Support vector machine example I – simple

Lecture 6: Support vector machine example II – iris dataset

Lecture 7: Support vector machines example III – parameter tuning

Lecture 8: Support vector machine example IV – digit recognition

Lecture 9: Support vector machine example V – digit recognition

Lecture 10: Advantages and disadvantages

Lecture 11: Mathematical formulation of Support Vector Machines (SVMs)

Chapter 11: Decision Trees

Lecture 1: Decision trees introduction – basics

Lecture 2: Decision trees introduction – entropy

Lecture 3: Decision trees introduction – information gain

Lecture 4: The Gini-index approach

Lecture 5: Decision trees introduction – pros and cons

Lecture 6: Decision trees implementation I

Lecture 7: Decision trees implementation II – parameter tuning

Lecture 8: Decision tree implementation III – identifying cancer

Lecture 9: Mathematical formulation of decision trees

Chapter 12: Random Forest Classifier

Lecture 1: Pruning introduction

Lecture 2: Bagging introduction

Lecture 3: Random forest classifier introduction

Lecture 4: Random forests example I – iris dataset

Lecture 5: Random forests example II – credit scoring

Lecture 6: Random forests example III – OCR parameter tuning

Lecture 7: Mathematical formulation of random forest classifiers

Chapter 13: Boosting

Lecture 1: Boosting introduction – basics

Lecture 2: Boosting introduction – illustration

Lecture 3: Boosting introduction – equations

Lecture 4: Boosting introduction – final formula

Lecture 5: Boosting implementation I – iris dataset

Lecture 6: Boosting implementation II -wine classification

Lecture 7: Boosting vs. bagging

Lecture 8: Mathematical formulation of boosting

Chapter 14: Principal Component Analysis (PCA)

Lecture 1: Principal component analysis (PCA) introduction

Lecture 2: Principal component analysis example

Lecture 3: Principal component analysis example II

Lecture 4: Mathematical formulation of principle component analysis (PCA)

Instructors

Holczer Balazs
Software Engineer

Rating Distribution

1 stars: 17 votes
2 stars: 37 votes
3 stars: 182 votes
4 stars: 496 votes
5 stars: 703 votes

Frequently Asked Questions

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