**Regression, Naive Bayes Classifier, Support Vector Machines, Random Forest Classifier and Deep Neural Networks**

**What you’ll learn**

- Solving regression problems
- Solving classification problems
- Using neural networks
- The most up to date machine learning techniques used by firms such as Google or Facebook
- Face detection with OpenCV
- TensorFlow

**Requirements**

**Description**

This course is about the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very good guess about stock prices movement in the market.

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 Algorithms**: regression and classification problems with Linear Regression, Logistic Regression, Naive Bayes Classifier, kNN algorithm, Support Vector Machines (SVMs) and Decision Trees- Machine Learning approaches in
**finance**: how to use learning algorithms to predict stock prices - Computer Vision and
**Face Detection**with OpenCV **Neural Networks**: what are feed-forward neural networks and why are they useful**Deep Learning**:**Recurrent Neural Networks**and**Convolutional Neural Networks**and their applications such as sentiment analysis or stock prices forecast**Reinforcement Learning**: Markov Decision processes (MDPs) and Q-learning

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 or students looking for a quick refresher

**Created by Holczer Balazs****Last updated 4/2019****English****English [Auto-generated]**

**Size: 1.82 GB**