HOT
blank

The Complete Course: Artificial Intelligence From Scratch

Development

Rating2.9
Enroll Now

The Complete Course: Artificial Intelligence From Scratch, available at $39.99, has an average rating of 2.85, with 150 lectures, 10 quizzes, based on 51 reviews, and has 3608 subscribers.

You will learn about Learn the basic of Artificial Intelligence from scratch. Learn how Neural Networks work. Program Multilayer Perceptron Network from scratch in python. You'll know how recurrent neural networks work. You'll learn how to create LSTM networks using python and Keras You'll know how to forecast Google stock price with high accuracy Use k Nearest Neighbor classification method to classify datasets. Classify datasets by using Support Vector Machine method Understand main concept behind Support Vector Machine method. Classify Handwritten Images by Logistic classification method You'll know how Linear Regression work. You'll know how Multi Linear Regression work using sklearn and Python. Program Logistic Regression from scratch in python. Build Model to Predict CO2 and Global Temperature by Polynomial Regression. You'll know the ideas behind Genetic Algorithm. You'll know the ideas behind Particle Swarm Optimization Method. You'll know how to find optimum point for complicated Trigonometric functions. You'll learn how to solve well known problems like Travelling Salesman Problem (TSP). This course is ideal for individuals who are Anyone who wants to make the right choice when starting to learn Artificial Intelligence. or Learners who want to work in data science and big data field or students who want to learn machine learning or Data analyser, Researcher, Engineers and Post Graduate Students It is particularly useful for Anyone who wants to make the right choice when starting to learn Artificial Intelligence. or Learners who want to work in data science and big data field or students who want to learn machine learning or Data analyser, Researcher, Engineers and Post Graduate Students.

Enroll now: The Complete Course: Artificial Intelligence From Scratch

Summary

Title: The Complete Course: Artificial Intelligence From Scratch

Price: $39.99

Average Rating: 2.85

Number of Lectures: 150

Number of Quizzes: 10

Number of Published Lectures: 150

Number of Published Quizzes: 10

Number of Curriculum Items: 169

Number of Published Curriculum Objects: 169

Original Price: £199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn the basic of Artificial Intelligence from scratch.
  • Learn how Neural Networks work.
  • Program Multilayer Perceptron Network from scratch in python.
  • You'll know how recurrent neural networks work.
  • You'll learn how to create LSTM networks using python and Keras
  • You'll know how to forecast Google stock price with high accuracy
  • Use k Nearest Neighbor classification method to classify datasets.
  • Classify datasets by using Support Vector Machine method
  • Understand main concept behind Support Vector Machine method.
  • Classify Handwritten Images by Logistic classification method
  • You'll know how Linear Regression work.
  • You'll know how Multi Linear Regression work using sklearn and Python.
  • Program Logistic Regression from scratch in python.
  • Build Model to Predict CO2 and Global Temperature by Polynomial Regression.
  • You'll know the ideas behind Genetic Algorithm.
  • You'll know the ideas behind Particle Swarm Optimization Method.
  • You'll know how to find optimum point for complicated Trigonometric functions.
  • You'll learn how to solve well known problems like Travelling Salesman Problem (TSP).

Who Should Attend

  • Anyone who wants to make the right choice when starting to learn Artificial Intelligence.
  • Learners who want to work in data science and big data field
  • students who want to learn machine learning
  • Data analyser, Researcher, Engineers and Post Graduate Students

Target Audiences

  • Anyone who wants to make the right choice when starting to learn Artificial Intelligence.
  • Learners who want to work in data science and big data field
  • students who want to learn machine learning
  • Data analyser, Researcher, Engineers and Post Graduate Students

Do you like to learn how to forecast economic time series like stock price or indexes with high accuracy?

Do you like to know how to predict weather data like temperature and wind speed with a few lines of codes?

Do you like to classify Handwritten digits more accurately ?

If you say Yesso read more …

In computer science, Artificial intelligence(AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In this you are going to learn essential concepts of AI using Python:

Neural Networks

Classification Methods

Regression Analysis

Optimization Methods

_____________________________________________________________________________________________________________________

in the First, Second,Thirdsections you will learn Neural Networks

You will learn how to make Recurrent Neural Networks using Keras and LSTMs:

  • you’ll learn how to use python andKeras to forecast google stock price . 

  • you’ll know how to use python andKeras to predict NASDAQ Indexprecisely.

  • you’ll learn how to use python andKeras to forecast New York temperature with low error.

  • you’ll know how to use python andKeras to predict New York Wind speedaccurately.

In the next section you learn how to use python and sklearn MLPclassifierto forecast output of different datasets like

  • Logic Gates

  • Vehicles Datasets

  • Generated Datasets

In the third section you can forecast output of different datasets using Keras library like

  • Random datasets

  • Forecast International Airline passengers

  • Los Angeles temperature forecasting

_____________________________________________________________________________________________________________________

Next you will learn how to classify well known datasets into with high accuracy using k-Nearest Neighbors, Bayes, Support Vector Machine and Logistic Regression.

In the 4thsection you learn how to use python and k-Nearest Neighbors to estimate output of your system. In this section you can classify:

  • Python Dataset

  • IRIS Flowers

  • Make your own k Nearest Neighbors Algorithm

In the 5thsection you learn how to use Bayes and python to classify output of your system with nonlinear structure .In this section you can classify:

  • IRIS Flowers

  • Pima Indians Diabetes Database

  • Make your own Naive Bayes  Algorithm

You can also learn how to classify datasets by by Support Vector Machines to find the correct class for data and reduce error. Next you go further  You will learn how to classify output of model by using Logistic Regression

In the 6thsection you learn how to use python to estimate output of your system. In this section you can estimate output of:

  • Random dataset

  • IRIS Flowers

  • Handwritten Digits

In the 7th section you learn how to use python to classify output of your system with nonlinear structure .In this section you can estimate output of:

  • Blobs

  • IRIS Flowers

  • Handwritten Digits

_____________________________________________________________________________________________________________________

After it we are going to learn regression methods like Linear, Multi-Linear and Polynomial  Regression.

In the 8th section you learn how to use Linear Regression and python to estimate output of your system. In this section you can estimate output of:

  • Random Number

  • Diabetes

  • Boston House Price

  • Built in Dataset

In the 9thsection you learn how to use python and Multi Linear Regression to estimate output of your system with multivariable inputs.In this section you can estimate output of:

  • Global Temprature

  • Total Sales of Advertising Campaign

  • Built in Dataset

In the 10thsection you learn how to use python Polynomial Regression to estimate output of your system. In this section you can estimate output of:

  • Nonlinear Sine Function

  • Python Dataset

  • Temperature and CO2

_____________________________________________________________________________________________________________________

Finally I want to learn you theory behind bio inspired algorithms like Genetic Algorithm  and Particle Swarm Optimization Method. You’ll learn basic genetic operators like mutation crossover and selection and how they are work. You’ll learn basic concepts of Particle Swarm and how they are work.

In the 11th section you will learn how to use python and deap library to solve optimization problem and find Min/Max points for your desired functions using Genetic Algorithm.

  • you’ll learn theory of Genetic Algorithm Optimization Method

  • you’ll know how to use python anddeap to optimize simple functionprecisely.

  • you’ll learn how to use python anddeap to find optimum point of complicated Trigonometric function.

  • you’ll know how to use python anddeap to solve  Travelling Salesman Problem (TSP)accurately.

In the 12thsection we go further you will learn how to use python and deap library to solve optimization problem using Particle Swarm Optimization

  • you’ll learn theory of Particle Swarm Optimization Method

  • you’ll know how to use python anddeap to optimize simple functionprecisely.

  • you’ll learn how to use python anddeap to find optimum point of complicated Trigonometric function.

  • you’ll know how to use python anddeap to solve  Rastrigin standard functionaccurately.

___________________________________________________________________________

Important information before you enroll:

  • In case you find the course useless for your career, don’t forget you are covered by a 30 day money back guarantee, full refund, no questions asked!

  • Once enrolled, you have unlimited, lifetime access to the course!

  • You will have instant and free access to any updates I’ll add to the course.

  • You will give you my full support regarding any issues or suggestions related to the course.

  • Check out the curriculum and FREE PREVIEW lecturesfor a quick insight.

___________________________________________________________________________

Music from Jukedeck – create your own at jukedeck com

___________________________________________________________________________

It’s time to take Action!

Click the “Take This Course” button at the top right now!

...Don’t waste time! Every second of every day is valuable…

I can’t wait to see you in the course!

Best Regrads,

Sobhan

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Learn LSTM Neural Networks

Lecture 1: Recurrent neural networks and LSTMs theory

Lecture 2: Required Softwares and Libraries

Lecture 3: Predict Google stock price using LSTMs – Part1

Lecture 4: Predict Google stock price using LSTMs – Part2

Lecture 5: Predict Google stock price using LSTMs – Part3

Lecture 6: Predict Google stock price using LSTMs – Part4

Lecture 7: Predict Google stock price using LSTMs – Part5

Lecture 8: Source Code

Lecture 9: Forecast NASDAQ Index using LSTMs and Keras library – Part 1

Lecture 10: Forecast NASDAQ Index using LSTMs and Keras library – Part 2

Lecture 11: Forecast NASDAQ Index using LSTMs and Keras library – Part 3

Lecture 12: Forecast NASDAQ Index using LSTMs and Keras library – Part 4

Lecture 13: Forecast NASDAQ Index using LSTMs and Keras library – Part 5

Lecture 14: Source Code

Lecture 15: Predict New York annual temperature using LSTMs – Part 1

Lecture 16: Predict New York annual temperature using LSTMs – Part 2

Lecture 17: Predict New York annual temperature using LSTMs – Part 3

Lecture 18: Predict New York annual temperature using LSTMs – Part 4

Lecture 19: Predict New York annual temperature using LSTMs – Part 5

Lecture 20: Source Code

Lecture 21: Forecast New York wind speed using LSTMs and Keras library – Part 1

Lecture 22: Forecast New York wind speed using LSTMs and Keras library – Part 2

Lecture 23: Forecast New York wind speed using LSTMs and Keras library – Part 3

Lecture 24: Forecast New York wind speed using LSTMs and Keras library – Part 4

Lecture 25: Forecast New York wind speed using LSTMs and Keras library – Part 5

Lecture 26: Source Code

Chapter 3: Learn Multi Layer Perceptron Neural Networks

Lecture 1: Theory of MLP Neural Networks

Lecture 2: Required Softwares and Libraries

Lecture 3: Make MLP neural network to create Logic Gates

Lecture 4: Source Code

Lecture 5: Using MLP to Detect Vehicles Precisely Part 1

Lecture 6: Using MLP to Detect Vehicles Precisely Part 2

Lecture 7: Source Code

Lecture 8: Classify random data using Multilayer Perceptron Part 1

Lecture 9: Classify random data using Multilayer Perceptron Part 2

Lecture 10: Source Code

Lecture 11: Using Keras to forecast 1000 data with 100 features in a few seconds Part 1

Lecture 12: Using Keras to forecast 1000 data with 100 features in a few seconds Part 2

Lecture 13: Source Code

Lecture 14: Forecasting international airline passengers using keras Part1

Lecture 15: Forecasting international airline passengers using keras Part2

Lecture 16: Source Code

Lecture 17: Los Angeles Temperature Forecasting Part 1

Lecture 18: Los Angeles Temperature Forecasting Part 2

Lecture 19: Los Angeles Temperature Forecasting Part 3

Lecture 20: Source Code

Chapter 4: k Nearest Neighbors Classification Method

Lecture 1: Theory of k Nearest Neighbors Classification Method

Lecture 2: Required Softwares and Libraries

Lecture 3: Use k Nearest Neighbors Classification Method to classify random dataset Part 1

Lecture 4: Use k Nearest Neighbors Classification Method to classify random dataset Part 2

Lecture 5: Source Code

Lecture 6: Learn How to Use k Nearest Neighbors Classification for IRIS Dataset

Lecture 7: Source Code

Lecture 8: Write k Nearest Neighbors Classification Method by yourself Part 1

Lecture 9: Write k Nearest Neighbors Classification Method by yourself Part 2

Lecture 10: Source Code

Chapter 5: Naive Bayes Classification Method

Lecture 1: Theory of Naive Bayes Classification Method

Lecture 2: Use the power of Naive Bayes to Classify IRIS Dataset Part 1

Lecture 3: Use the power of Naive Bayes to Classify IRIS Dataset Part 2

Lecture 4: Source Code

Lecture 5: Learn how to Use Naive Bayes to Classify Diabetes dataset

Lecture 6: Source Code

Lecture 7: Write Naive Bayes Classification Method by Yourself Part 1

Lecture 8: Write Naive Bayes Classification Method by Yourself Part 2

Lecture 9: Source Code

Chapter 6: Support Vector Machine Classification Method

Lecture 1: Theory of Support Vector Machine Classification Method

Lecture 2: Support Vector Machine Classification Method for two classes dataset

Lecture 3: Source Code

Lecture 4: Use the Power of Support Vector Machine Method for IRIS dataset Part 1

Lecture 5: Use the Power of Support Vector Machine Method for IRIS dataset Part 2

Lecture 6: Source Code

Lecture 7: Use Support Vector Machine for Hand Written Images Classification Part 1

Lecture 8: Use Support Vector Machine for Hand Written Images Classification Part 2

Lecture 9: Source Code

Chapter 7: Logistic Regression Classification Method

Lecture 1: Logistic Regression Classification Method

Lecture 2: Use Logistic Regression Model for Blobs Data sets Classification Part-1

Lecture 3: Use Logistic Regression Model for Blobs Data sets Classification Part-2

Lecture 4: Source Code

Lecture 5: Learn How to Use Logistic Regression Classifier for IRIS Flowers Classification

Lecture 6: Source Code

Lecture 7: Classify Handwritten Digits Using Logistic Regression

Lecture 8: Source Code

Instructors

  • Sobhan N.
    Sobhan N.
    AI Developer|Electrical Engineer (PhD)|21,000+ Students

Rating Distribution

  • 1 stars: 9 votes
  • 2 stars: 3 votes
  • 3 stars: 11 votes
  • 4 stars: 12 votes
  • 5 stars: 16 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!

You may also like

Top 10 Ethical AI Courses to Learn in November 2024

Top 10 Game Development Courses to Learn in November 2024

Top 10 Software Testing Courses to Learn in November 2024