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