Introduction to Machine Learning & Deep Learning in Python (Udemy.com)

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

( 45 Reviews )

Created by: Holczer Balazs

Produced in 2021

What you will 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

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 86 / 100

Course 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

Time: 13h

Instructor Details

Holczer Balazs

4.3 Rating
45 Reviews

Hi!
My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model.
Take a look at my website if you are interested in these topics!

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Reviews

4.3

45 total reviews

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By Allan Hogue

Concise and informative.

By è… å…¬å½¦

This lesson helps us to easily understand how to make cords for testing newral network .

By Francis Campbell

I am french, the accent is a little hard to understand and the transcription is not perfect. But it's well explained!

By Geoffrey Devitt

Very good course, quick over view with plenty of good working examples.

By Tiago Fernandes

Good introduction on the basics regarding machine learning.

By Matej ÄŒamaj

I have just finished 4th section on Naive Bayes Classifier and probably won't watch any other lecture. I'm quite disappointed by this course since it shouldn't be called even an introduction. Basically each lecture consist of two part:
1) Theoretical background - very brief intro of a method that will be discussed in a particular section, simply just a quick overview that I could get after 15 minutes of google search
2) Python example of method - just toy examples, the code is not prepare before the lecture so lot of time is spent on writing the code down (and sometimes looking for a bug)
I wouldn't recommend it to anyone who can use google/quora/online blogs because it doesn't have any added value compared to these sources which are for free.

By Gabriel Oliveira-Barra

Some improvements such as using Jupyter Notebooks, using better plot libraries such as seaborn and dataframes (pandas) would make it more practical, nicer and intuitive.
The mathematical background, on the other hand, is quite good in this course.

By Emanuele Pedrona

Clear and complete course, with examples too. Very good.

It's a great introduction to machine learning. I don't think it gives all the resources to create something on your own, but it sure gives a good map of the possibilities in machine learning. I like the way the course is built with theoretical approach follow by simple to intermediate example. Good job!

By Danny Chan