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Machine Learning and Deep Learning Bootcamp in Python (Udemy.com)

Machine Learning, Neural Networks, Deep Learning and Reinforcement Learning, GAN in Keras and TensorFlow

4.4
1,719 reviews

Created by: Holczer Balazs

Last updated September 2026

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What you will 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)

How it compares

Against the other 155 rated Udemy courses in Machine Learning on CourseDuck:

  • Rating4.4 out of 5higher than 49% of them
  • Reviews1,719#43 of 156 by number of reviews
  • Length31.5 hourslonger than 90% of them
  • Last updatedSeptember 2026more recently than 99% of them

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Quality Score

Content Quality
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Video Quality
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Qualified Instructor
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Course Depth & Coverage
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Overall Score : 88 / 100

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Course Description

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.

### MACHINE LEARNING ###

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

### NEURAL NETWORKS AND DEEP LEARNING ###

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

  • LSTM and 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!
Read More All Levels All Levels product length iconTime: 31.5h

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Instructor Details

Holczer Balazs

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!

Read More

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Reviews

4.4

1,719 ratings on Udemy

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By Josh Swan on 2/9/2026

Everything you need to know for the fundamentals of ML and DL. It is also regularly updated which is great. I will definitely be revisiting sections of this course to deepen my understanding and practice the Maths, so I am not so dependent on libraries all the time.

By Eduardo Morales on 4/9/2025

Explanations are very good considering the theorical and pracical stuff. The only bad thing about the course is that you cant see the complete code line when casting functions as train_test_split which made out of bounds line codes.

By Dušan Trtica on 10/6/2022

As far as machine learning, neural networks and deep neural networks are concerned, this is an amazing course :) Thanks for watching! Joke aside, the course is comprehensive, the examples are excellent, and complete, and the author codes in front of us everything from the ground up. I wouldn't put appendices of this length (5,6 hours) to be mandatory to complete the course and get a certificate as this course already goes through all of these concepts.

By Jose Romero Lopera on 12/15/2021

I have taking this course as a training into machine learning for professional research reasons and I'm very satisfied with the content. The theory is well explained at a surface level, which is enough to understand what the different ML methods are doing under the hood. The only comment I would do is that I miss sometimes a bit more of detailled explanation on some lines of code, which have to be interpreted by yourself (this is in any case not bad for a bit of self-learning, but I don't think this is the best approach). Anyway, overall I'm so far very satisfied with the course and the way the contents are explained.

By Prent Rodgers on 6/10/2021

Wonderful approach to the topic. I am a slow learner, and I need to hear a subject from many perspectives before I can understand well enough implement on my own, and Balazs perspective is unique. Great course!

By Aarjav Jain on 4/3/2021

It's good course if we want to learn the implementation of machine learning directly without going much deep in the algorithms, but I want to suggest that it would be great if we could have some assignments just to get feel of algorithms without using any python library.

By Chuan Huey Ling on 4/14/2020

Good match for general understanding on ML. It would be good to provide a private avenue for the students to clarify some of the doubts they have. For e.g. I had problems getting the software running, as well as, needing to find my own supplementary materials online to back up my understanding on some of the formulas taught.

By Vishal Bhushan on 4/12/2020

All the concepts were well explained. I never felt during the coarse that I have not understanding any point due to unawareness of statistical and probability concept. Trainer has given sufficient back ground which made the concept easy to learn and implement it.

By Pratyay Dutta on 4/11/2020

Its a very good course for complete beginners in the field of machine learning and neural networks. The theory portions have been explained in a very comprehensible manner. There are codes present for implementation of every method and its optimization. The only issue i have in this course is that the codes for implementation are all pretyped and a bit hastily explained. It would have been better if the lectures included code-along activities for a more holistic understanding of the intricacies. But overall its a very wholesome course and beginners will definitely gain much knowledge. Also, I love that Hungarian accent of the instructor XD.

By Jairo Dávila on 4/21/2017

Contiene mucha información útil y en la mayoría de los casos la explicación es suficiente. Solamente en algunas lecturas se debe incluir mayor detalle en la explicación para tener un entendimiento completo de por ejemplo de donde se obtienen los datos que se usan en los ejemplos.

By Allan Hogue

Concise and informative.

By 菅 公彦

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

Showing 12 of 54 reviews

Machine Learning and Deep Learning Bootcamp in Python
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  • Video Duration: icon 31.5h
  • Price: $19.99
  • Provider: icon Udemy
  • Updated: icon September 2026
  • Level: All Levels All Levels
  • Language: icon English
  • Certificate: icon Yes
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