Download this Udemy course and learn Regression, Naive Bayes Classifier, Support Vector Machines, Random Forest Classifier, and Deep Neural Networks by downloading free Udemy course.
What you’ll learn
- You will learn how to Solve regression problems
- You will be able to classify problems
- You will learn how to use neural networks
- You will learn the most up to date machine learning techniques which are used by firms such as Google or Facebook
- You will learn how to Face detection with OpenCV
- You will learn TensorFlow too.
You should have the following requirements:-
- Basic python
This course covers the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very popular and nowadays because these learning algorithms can be used in different fields from software engineering to investment banking.
With good Learning algorithms, you 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
Size: 1.82 GB