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
- Transformers (ChatGPT)
Interested in Machine Learning and Deep Learning ? 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.
###MACHINELEARNING###
Linear Regression
understanding linear regression model
correlation and covariance matrix
linear relationships between random variables
gradient descent and design matrix approaches
Logistic Regression
understanding logistic regression
classification algorithms basics
maximum likelihood function and estimation
K-Nearest Neighbors Classifier
what is k-nearest neighbour classifier?
non-parametric machine learning algorithms
Naive Bayes Algorithm
what is the naive Bayes algorithm?
classification based on probability
cross-validation
overfitting and underfitting
Support Vector Machines(SVMs)
support vector machines (SVMs) and support vector classifiers (SVCs)
maximum margin classifier
kernel trick
Decision Trees and Random Forests
decision tree classifier
random forest classifier
combining weak learners
Bagging and Boosting
what is bagging and boosting?
AdaBoost algorithm
combining weak learners (wisdom of crowds)
Clustering Algorithms
what are clustering algorithms?
k-means clustering and the elbow method
DBSCAN algorithm
hierarchical clustering
market segmentation analysis
### NEURALNETWORKSANDDEEPLEARNING###
Feed-Forward Neural Networks
single layer perceptron model
feed.forward neural networks
activation functions
backpropagation algorithm
Deep Neural Networks
what are deep neural networks?
ReLU activation functions and the vanishing gradient problem
training deep neural networks
loss functions (cost functions)
Convolutional Neural Networks (CNNs)
what are convolutional neural networks?
feature selection with kernels
feature detectors
pooling and flattening
Recurrent Neural Networks (RNNs)
what are recurrent neural networks?
training recurrent neural networks
exploding gradients problem
LSTMand GRUs
time series analysis with LSTM networks
Transformers
word embeddings
query, key and value matrices
attention and attention scores
training a transformer
ChatGPT and transformers
Generative Adversarial Networks (GANs)
what are GANs
generator and discriminator
how to train a GAN
implementation of a simple GAN architecture
Numerical Optimization (in Machine Learning)
gradient descent algorithm
stochastic gradient descent theory and implementation
ADAGrad and RMSProp algorithms
ADAM optimizer explained
ADAM algorithm implementation
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
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 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!
