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The Complete Artificial Intelligence for Cyber Security 2024

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The Complete Artificial Intelligence for Cyber Security 2024, available at $59.99, has an average rating of 4.3, with 181 lectures, based on 336 reviews, and has 2801 subscribers.

You will learn about Isolation Forest Markov Chains Statsmodels NLP (Natural Language Processing) Linear Regression Logistic Regression Naïve Bayes ANN (Artificial Intelligence) Random Forest K-means HMM Eigenfaces and Eigenvalues SVM (Support Vector Machine) XGBOOST Pandas Numpy matplotlib IF-IDF Tensorflow Scikit-Learn Cyber security Google Colab Data Pre-processing. Analysing Data. Data standardization. Splitting Data into Training Set and Test Set. One-hot Encoding. Understanding Machine Learning Algorithm. Training Neural Network. Model building. Analysing Results. Model compilation. A Comparison Of Categorical And Binary Problem. Make a Prediction. Testing Accuracy. Confusion Matrix. Keras. This course is ideal for individuals who are Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning in Cyber Security or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science. or Anyone passionate about Artificial Intelligence. or Data Scientists who want to take their AI Skills to the next level. or AI experts who want to expand on the field of applications. or Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or Any people who are not satisfied with their job and who want to become a Data Scientist. It is particularly useful for Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning in Cyber Security or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science. or Anyone passionate about Artificial Intelligence. or Data Scientists who want to take their AI Skills to the next level. or AI experts who want to expand on the field of applications. or Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or Any people who are not satisfied with their job and who want to become a Data Scientist.

Enroll now: The Complete Artificial Intelligence for Cyber Security 2024

Summary

Title: The Complete Artificial Intelligence for Cyber Security 2024

Price: $59.99

Average Rating: 4.3

Number of Lectures: 181

Number of Published Lectures: 181

Number of Curriculum Items: 186

Number of Published Curriculum Objects: 186

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Isolation Forest
  • Markov Chains
  • Statsmodels
  • NLP (Natural Language Processing)
  • Linear Regression
  • Logistic Regression
  • Naïve Bayes
  • ANN (Artificial Intelligence)
  • Random Forest
  • K-means
  • HMM
  • Eigenfaces and Eigenvalues
  • SVM (Support Vector Machine)
  • XGBOOST
  • Pandas
  • Numpy
  • matplotlib
  • IF-IDF
  • Tensorflow
  • Scikit-Learn
  • Cyber security
  • Google Colab
  • Data Pre-processing.
  • Analysing Data.
  • Data standardization.
  • Splitting Data into Training Set and Test Set.
  • One-hot Encoding.
  • Understanding Machine Learning Algorithm.
  • Training Neural Network.
  • Model building.
  • Analysing Results.
  • Model compilation.
  • A Comparison Of Categorical And Binary Problem.
  • Make a Prediction.
  • Testing Accuracy.
  • Confusion Matrix.
  • Keras.

Who Should Attend

  • Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning in Cyber Security
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Anyone passionate about Artificial Intelligence.
  • Data Scientists who want to take their AI Skills to the next level.
  • AI experts who want to expand on the field of applications.
  • Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.

Target Audiences

  • Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning in Cyber Security
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Anyone passionate about Artificial Intelligence.
  • Data Scientists who want to take their AI Skills to the next level.
  • AI experts who want to expand on the field of applications.
  • Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.

*** AS SEEN ON KICKSTARTER ***

Learn key AI concepts and intuition training to get you quickly up to speed with all things AI. Covering:

  • How to start building AIwith no previous coding experience using Python.

  • How to solve AI problemsin cyber security field.

Here is what you will get with this course:

1. Complete beginner to expert AI skills – Learn to code self-improving AI for a range of purposes. In fact, I will code together with you. Every tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.

2. Coding step–Plus, you’ll get a template which shows all the steps and all detailed explanations on each step.

3. Intuition Tutorials – Where most courses simply bombard you with dense theory and set you on your way, you will develop a deep understanding for not only what you’re doing, but why you’re doing it. That’s why I don’t throw complex theories at you, but focus on building up your intuition in coding AI making for infinitely better results down the line.

4. Real-world solutions – You’ll achieve your goal in not only 1 project but in more than 10. Each module is comprised of varying structures and difficulties, meaning you’ll be skilled enough to build AI adaptable to any projects in real life, rather than just passing a glorified memory “test and forget” like most other courses. Practice truly does make perfect.

5. In-course support – I fully committed to making this the most accessible and results-driven AI course on the planet. This requires me to be there when you need my help. That’s why I will support you in your journey, meaning you’ll get a response from me within 72 hours maximum.

Course Curriculum

Chapter 1: Introduction to Artificial Intelligence and Cybersecurity (Updated in 2024)

Lecture 1: Course structure

Lecture 2: Important note about tools in this course

Lecture 3: How to make the most out of this course

Lecture 4: UPDATED CONTENT

Lecture 5: Basic concepts of machine learning

Lecture 6: Introduction to cybersecurity and common cyber threats

Lecture 7: What is the Role of AI in cybersecurity

Chapter 2: Fundamentals of Machine Learning for Cybersecurity (UPDATED 2024)

Lecture 1: Basics of supervised, unsupervised, and reinforcement learning

Lecture 2: What is scikit-learn?

Lecture 3: what is pandas?

Lecture 4: Implementation of basic machine learning in cyber security

Lecture 5: what is standardization ?

Lecture 6: Implementation of standardization

Lecture 7: what is principal component analysis (pca)?

Lecture 8: Implementation of principal component analysis

Lecture 9: What is markov chains?

Lecture 10: Implementation of markov chains

Lecture 11: what is clustering

Lecture 12: what is plotly

Lecture 13: Implementation of clustering

Lecture 14: What is XGBOOST classifier

Lecture 15: Implementation of XGBOOST classifier

Lecture 16: what is scipy?

Lecture 17: what is matplotlib?

Lecture 18: What is isolation forest?

Lecture 19: Implementation of Isolation forest?

Lecture 20: What is K-nearest Neighbors

Lecture 21: what is hashing vectorizer

Lecture 22: what is tf-idf?

Lecture 23: Implementation of hashing vectorizer and tf-idf with scikit-learn

Chapter 3: Introduction to phishing attack detectors (updated 2024)

Lecture 1: what is logistic regression?

Lecture 2: what is decision tree?

Lecture 3: what is phishing attack

Lecture 4: What is spam detection

Lecture 5: What is perceptrons?

Lecture 6: Detecting spams using perceptrons

Lecture 7: What is SVM

Lecture 8: Implementation of Phishing detection with logistic regression

Chapter 4: Malware threat detection using machine learning method (updated 2024)

Lecture 1: What is malware

Lecture 2: what is malware static and dymanic analysis

Lecture 3: what is obfuscated JavaScript?

Lecture 4: What is N-gram

Lecture 5: what is PE Header??

Lecture 6: what is Markov process

Lecture 7: What is HMMs

Lecture 8: Implementation of N-grams

Lecture 9: what is metamorphic malware?

Lecture 10: What is K-means

Lecture 11: Implementation of malware detection using K-means

Lecture 12: Implementation of Malware detection using decision tree

Chapter 5: Automatic Intrusion Detection (updated 2024)

Lecture 1: what is automatic instrusion detection??

Lecture 2: what is spam email and spam filtering?

Lecture 3: What is phishing URL?

Lecture 4: What is network?

Lecture 5: How to classify network?

Lecture 6: what is Network behavior anomaly detection?

Lecture 7: what is Credit card fraud detection?

Lecture 8: what is tf-idf

Lecture 9: What is confusion matrix

Lecture 10: what is Counterfeit bank note detection?

Lecture 11: what is Ad blocking?

Lecture 12: what is Wireless indoor localization?

Lecture 13: What is botnet?

Lecture 14: How to detect botnet

Lecture 15: What is Gaussian Naive Bayes

Lecture 16: implementation of DDos attacks

Lecture 17: Implementation of botnet detection

Lecture 18: Implementation of Counterfeit bank note detection

Lecture 19: Implementation of ad-blocking

Lecture 20: Implementation of phishing URL

Lecture 21: Implementation of spam detection

Chapter 6: Securing and Attacking Data with Machine Learning (updated 2024)

Lecture 1: What is password security?

Lecture 2: what is XG-Boost

Lecture 3: what is artificial neural network

Lecture 4: what are Variance, covariance, and the covariance matrix?

Lecture 5: What are Eigenvectors and Eigenvalues

Lecture 6: What is MLPClassifier?

Lecture 7: What is XGBClassifier?

Lecture 8: what is PUFs

Lecture 9: what is Challenge-Response Pairs (CRPs)?

Lecture 10: Implementation of assessing password security

Lecture 11: Implementation of keystroke detection

Lecture 12: Implementation of facial recognition

Chapter 7: (OLD CONTENT) Introduction

Lecture 1: Course structure

Lecture 2: How To Make The Most Out Of This Course

Lecture 3: Who is this course for????

Lecture 4: How does the course work?

Lecture 5: Type of Machine learning

Instructors

Rating Distribution

  • 1 stars: 31 votes
  • 2 stars: 15 votes
  • 3 stars: 41 votes
  • 4 stars: 86 votes
  • 5 stars: 163 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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