The Complete Course: Artificial Intelligence From Scratch
3.0 (27 ratings)
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The Complete Course: Artificial Intelligence From Scratch

Learn the Essential Concepts of the AI like Neural Networks, Classification, Regression and Optimization Using Python.
3.0 (27 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,314 students enrolled
Created by Sobhan N.
Last updated 12/2018
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 14 hours on-demand video
  • 43 articles
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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What you'll 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).
Course content
Expand all 150 lectures 14:17:21
+ Learn LSTM Neural Networks
26 lectures 02:00:53
Required Softwares and Libraries
00:25

Learn how to Predict Google stock price using LSTMs 

Predict Google stock price using LSTMs - Part1
05:10
Predict Google stock price using LSTMs - Part2
05:20
Predict Google stock price using LSTMs - Part3
06:30
Predict Google stock price using LSTMs - Part4
06:00
Predict Google stock price using LSTMs - Part5
05:53
Source Code
00:25

Learn How to Forecast NASDAQ Index using LSTMs and Keras library accurately 

Forecast NASDAQ Index using LSTMs and Keras library - Part 1
07:09
Forecast NASDAQ Index using LSTMs and Keras library - Part 2
03:46
Forecast NASDAQ Index using LSTMs and Keras library - Part 3
04:23
Forecast NASDAQ Index using LSTMs and Keras library - Part 4
05:32
Forecast NASDAQ Index using LSTMs and Keras library - Part 5
06:03
Source Code
00:25

Learn how to Predict New York annual temperature using LSTMs 

Predict New York annual temperature using LSTMs - Part 1
06:10
Predict New York annual temperature using LSTMs - Part 2
06:03
Predict New York annual temperature using LSTMs - Part 3
05:14
Predict New York annual temperature using LSTMs - Part 4
06:03
Predict New York annual temperature using LSTMs - Part 5
06:29
Source Code
00:25

In this lecture you will learn How to Forecast New York wind speed using LSTMs and Keras library 

Forecast New York wind speed using LSTMs and Keras library - Part 1
04:37
Forecast New York wind speed using LSTMs and Keras library - Part 2
04:53
Forecast New York wind speed using LSTMs and Keras library - Part 3
05:10
Forecast New York wind speed using LSTMs and Keras library - Part 4
04:56
Forecast New York wind speed using LSTMs and Keras library - Part 5
07:02
Source Code
00:22
Recurrent Neural Networks and LSTMs Quiz
3 questions
Forecast Apple stock price using LSTMs
Recurrent Neural Networks and LSTMs Assignment
1 question
+ Learn Multi Layer Perceptron Neural Networks
20 lectures 02:24:51

In this lecture you will learn Multi Layer Perceptron Neural Networks Theory

Theory of MLP Neural Networks
06:28
Required Softwares and Libraries
00:25

In this Lecture You Will learn How to Make MLP neural network to create Logic Gates

Make MLP neural network to create Logic Gates
16:25
Source Code
00:10

In this we make program to detect vehicle types correctly.

Using MLP to Detect Vehicles Precisely Part 1
12:07
Using MLP to Detect Vehicles Precisely Part 2
10:56
Source Code
00:15

In this Lecture You will learn how to Classify random data using Multilayer Perceptron 

Classify random data using Multilayer Perceptron Part 1
09:55
Classify random data using Multilayer Perceptron Part 2
10:06
Source Code
00:13

In this Lecture you are going to learn how to Use Keras to forecast 1000 data with 100 features in a few seconds 

Using Keras to forecast 1000 data with 100 features in a few seconds Part 1
09:44
Using Keras to forecast 1000 data with 100 features in a few seconds Part 2
11:47
Source Code
00:13

using keras to Forecast international airline passengers correctly 

Forecasting international airline passengers using keras Part1
13:32
Forecasting international airline passengers using keras Part2
10:54
Source Code
00:22

In this lecture you will learn how to use MLP for Los Angeles Temperature Forecasting 

Los Angeles Temperature Forecasting Part 1
09:14
Los Angeles Temperature Forecasting Part 2
14:01
Los Angeles Temperature Forecasting Part 3
07:04
Source Code
00:58
Multilayer Perceptron Neural Networks Quiz
3 questions
In this assignment we want to forecast New York temperature using Multilayer Perceptron Neural Networks.
Multilayer Perceptron Neural Networks assignment
1 question
+ k Nearest Neighbors Classification Method
10 lectures 59:19
Theory of k Nearest Neighbors Classification Method
04:38
Required Softwares and Libraries
00:08

In this lecture we Use the power k Nearest Neighbors Classification Method to classify random dataset accurately. 

Use k Nearest Neighbors Classification Method to classify random dataset Part 1
10:58
Use k Nearest Neighbors Classification Method to classify random dataset Part 2
10:50
Source Code
00:12

In this lecture you will Learn How to Use k Nearest Neighbors Classification for IRIS Dataset. The IRIS dataset is the most famous benchmark for classification problems. 

Learn How to Use k Nearest Neighbors Classification for IRIS Dataset
14:38
Source Code
00:20

Learn how to Write k Nearest Neighbors Classification Method by yourself. 

Write k Nearest Neighbors Classification Method by yourself Part 1
09:14
Write k Nearest Neighbors Classification Method by yourself Part 2
08:03
Source Code
00:16
k Nearest Neighbors Classification Method Qiuz
3 questions
Use k Nearest Neighbors Classification Method to classify random dataset
k Nearest Neighbors Classification Method Assignment
1 question
+ Naive Bayes Classification Method
9 lectures 55:35
Theory of Naive Bayes Classification Method
04:35

Learn how to Use Naive Bayes to Classify IRIS Dataset accurately. 

Use the power of Naive Bayes to Classify IRIS Dataset Part 1
09:27
Use the power of Naive Bayes to Classify IRIS Dataset Part 2
06:04
Source Code
00:12

In this lecture you are going to Learn how to Use Naive Bayes to Classify Diabetes dataset. You also have access to dataset and source code in the next lecture. 

Learn how to Use Naive Bayes to Classify Diabetes dataset
11:44
Source Code
00:39

In this lecture you will learn how to Write Naive Bayes Classification Method by Yourself. Then you are able to predict gender of human. 

Write Naive Bayes Classification Method by Yourself Part 1
10:29
Write Naive Bayes Classification Method by Yourself Part 2
12:13
Source Code
00:11
Naive Bayes Classification Method Quiz
3 questions
Use Naive Bayes Classification Method for diabetes dataset
Naive Bayes Classification Method Assignment
1 question
+ Support Vector Machine Classification Method
9 lectures 56:02
Theory of Support Vector Machine Classification Method
04:28

IN this lecture you will learn how to use Support Vector Machine Classification Method to classify two classes dataset 

Support Vector Machine Classification Method for two classes dataset
14:45
Source Code
00:08

Use the Power of Support Vector Machine Method for IRIS dataset classification based on 4 features of this flower. 

Use the Power of Support Vector Machine Method for IRIS dataset Part 1
09:54
Use the Power of Support Vector Machine Method for IRIS dataset Part 2
05:59
Source Code
00:12
Use Support Vector Machine for Hand Written Images Classification Part 1
09:53
Use Support Vector Machine for Hand Written Images Classification Part 2
10:24
Source Code
00:18
+ Logistic Regression Classification Method
8 lectures 57:37
Logistic Regression Classification Method
08:17

Learn how to Use Logistic Regression Model for Blobs Data sets Classification with a few lines of python codes.

Use Logistic Regression Model for Blobs Data sets Classification Part-1
08:31
Use Logistic Regression Model for Blobs Data sets Classification Part-2
08:21
Source Code
00:25

Learn How to Use Logistic Regression Classifier for IRIS Flowers Classification with using 4 features of this flower 

Learn How to Use Logistic Regression Classifier for IRIS Flowers Classification
15:25
Source Code
00:25

In this lecture you will learn how to Classify Handwritten Digits Using Logistic Regression. 

Classify Handwritten Digits Using Logistic Regression
15:44
Source Code
00:29
Logistic Regression Analysis Quiz
3 questions
Classify blob data set using logistic regression analysis
Logistic Regression Analysis Assignment
1 question
+ Linear Regression Analysis
13 lectures 01:11:56
Linear Regression Theory
05:24
Required Softwares and Libraries
00:08
Use Linear Regression to Create Model for Random Numbers Part-1
08:50
Use Linear Regression to Create Model for Random Numbers Part-2
09:38
Source Code
00:12
Learn How to Create Linear Regression Model to Predict Diabetes Part-1
08:13
Learn How to Create Linear Regression Model to Predict Diabetes Part-2
08:17
Source Code
00:13
Linear Regression Model for Boston Houses Data set Part-1
09:34
Linear Regression Model for Boston Houses Data set Part-2
08:32
Source Code
00:11
Linear Regression Model for Built-in Data set
12:33
Source Code
00:09
Linear Regression Analysis Quiz
3 questions
+ Multi Linear Regression
9 lectures 59:48
Multi Linear Regression Theory
07:15
Model Global Temperature Using Multilinear Regression Method Part-1
10:33
Model Global Temperature Using Multilinear Regression Method Part-2
08:48
Source Code
00:16
Make Best Advertising Campaign Using Multilinear Regression Model Part-1
13:25
Make Best Advertising Campaign Using Multilinear Regression Model Part-2
05:25
Source Code
00:16
Multi Linear Regression Model for built in dataset
13:35
Source Code
00:14
Multi Linear Regression Analysis Quiz
3 questions
Make multi linear regression model for blob data set .
Multi Linear Regression Assignment
1 question
+ Polynomial Regression Analysis
10 lectures 01:09:03
Polynomial Regression Analysis Theory
04:45
Polynomial Regression Model for Sine Function Part-1
12:22
Polynomial Regression Model for Sine Function Part-2
11:00
Source Code
00:30
Learn How to Use Polynomial Regression Model for Built-in Dataset Part-1
07:20
Learn How to Use Polynomial Regression Model for Built-in Dataset Part-2
07:01
Source Code
00:22
Find the Relation between CO2 and Temperature by Polynomial Regression Part-1
11:48
Find the Relation between CO2 and Temperature by Polynomial Regression Part-2
13:15
Source Code
00:39
Polynomial Regression Analysis Quiz
2 questions
Make polynomial regression model for Cosine function.
Polynomial Regression Analysis Assignment
1 question
Requirements
  • All you need is a decent PC/Laptop (2GHz CPU, 4GB RAM). You will get the rest from me.
  • You must know basic python programming.
  • Install Sublime and required library for python.
  • You should have a great desire to learn artificial intelligence and do it in a hands-on fashion.
Description

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 Yes so 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,Third sections 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 and Keras to forecast google stock price .  


  • you'll know how to use python and Keras to predict NASDAQ Index precisely.


  • you'll learn how to use python and Keras to forecast New York temperature with low error. 


  • you'll know how to use python and Keras to predict New York Wind speed accurately.


In the next section you learn how to use python and sklearn MLPclassifier to 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 4th section 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 5th section 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 6th section 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 9th section 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 10th section 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 and deap to optimize simple function precisely.


  • you'll learn how to use python and deap to find optimum point of complicated Trigonometric function


  • you'll know how to use python and deap to solve  Travelling Salesman Problem (TSP) accurately.


In the 12th section 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 and deap to optimize simple function precisely.


  • you'll learn how to use python and deap to find optimum point of complicated Trigonometric function


  • you'll know how to use python and deap to solve  Rastrigin standard function accurately.

___________________________________________________________________________

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 lectures for 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

Who this course is for:
  • 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