Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

The Complete Course: Artificial Intelligence From Scratch

Name: The Complete Course: Artificial Intelligence From Scratch
Rating: 3.5 (55 reviews)

Learn the Essential Concepts of the AI like Neural Networks, Classification, Regression and Optimization Using Python.

Created bySobhan N.

Last updated 12/2018

English

English [Auto],

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

12 sections • 150 lectures • 14h 17m total length

Introduction9:20

Recurrent neural networks and LSTMs theory6:26
Explore recurrent neural networks and long short-term memory units, featuring gated memory to process sequences, address backpropagation through time challenges, and apply to speech, handwriting, and time series tasks.
Required Softwares and Libraries0:25
Predict Google stock price using LSTMs - Part15:10
Learn how to Predict Google stock price using LSTMs
Predict Google stock price using LSTMs - Part25:20
Predict Google stock price using LSTMs - Part36:30
Combine high and low columns into input X, and define y as the close price. Transpose and scale X, then reshape it to three dimensions for the LSTM model.
Predict Google stock price using LSTMs - Part46:00
Predict Google stock price using LSTMs - Part55:53
Source Code0:25
Forecast NASDAQ Index using LSTMs and Keras library - Part 17:09
Learn How to Forecast NASDAQ Index using LSTMs and Keras library accurately
Forecast NASDAQ Index using LSTMs and Keras library - Part 23:46
Forecast NASDAQ Index using LSTMs and Keras library - Part 34:23
Forecast NASDAQ Index using LSTMs and Keras library - Part 45:32
Forecast NASDAQ Index using LSTMs and Keras library - Part 56:03
Forecast NASDAQ with an LSTM in Keras, predicting from X test and comparing to y test with plots; adjust number of units to improve accuracy and reduce mean absolute error.
Source Code0:25
Predict New York annual temperature using LSTMs - Part 16:10
Learn how to Predict New York annual temperature using LSTMs
Predict New York annual temperature using LSTMs - Part 26:03
Predict New York annual temperature using LSTMs - Part 35:14
Predict New York annual temperature using LSTMs - Part 46:03
Split data into train and test sets with 30 percent, then build a lstm model with a 1x2 input, tanh and hard sigmoid activations, and mean squared error loss.
Predict New York annual temperature using LSTMs - Part 56:29
Forecast New York City temperature with an LSTM neural network, compare predictions to test data via plots, and show that increasing input layer units to 50 improves mean absolute error.
Source Code0:25
Forecast New York wind speed using LSTMs and Keras library - Part 14:37
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 24:53
Forecast New York wind speed using LSTMs and Keras library - Part 35:10
Transpose data with delays for the output, standardize X and Y to minus 1 and plus 1 with a min-max scalar, and prepare a 3D input for LSTM using Keras.
Forecast New York wind speed using LSTMs and Keras library - Part 44:56
Forecast New York wind speed using LSTMs and Keras library - Part 57:02
Train an LSTM wind speed model across 10 airports, reducing mean absolute error and comparing predicted to real data with plots.
Source Code0:22
Recurrent Neural Networks and LSTMs Quiz
Recurrent Neural Networks and LSTMs Assignment

Theory of MLP Neural Networks6:28
In this lecture you will learn Multi Layer Perceptron Neural Networks Theory
Required Softwares and Libraries0:25
Make MLP neural network to create Logic Gates16:25
In this Lecture You Will learn How to Make MLP neural network to create Logic Gates
Source Code0:10
Using MLP to Detect Vehicles Precisely Part 112:07
In this we make program to detect vehicle types correctly.
Using MLP to Detect Vehicles Precisely Part 210:56
Source Code0:15
Classify random data using Multilayer Perceptron Part 19:55
In this Lecture You will learn how to Classify random data using Multilayer Perceptron
Classify random data using Multilayer Perceptron Part 210:06
Source Code0:13
Using Keras to forecast 1000 data with 100 features in a few seconds Part 19:44
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 211:47
Source Code0:13
Forecasting international airline passengers using keras Part113:32
using keras to Forecast international airline passengers correctly
Forecasting international airline passengers using keras Part210:54
Build Keras sequential model to forecast airline passengers, normalize data to 0–1, configure a 3-input network with 32-unit hidden layer and sigmoid output, train with RMSProp and mean squared error.
Source Code0:22
Los Angeles Temperature Forecasting Part 19:14
In this lecture you will learn how to use MLP for Los Angeles Temperature Forecasting
Los Angeles Temperature Forecasting Part 214:01
Visualize Los Angeles temperature data with a 3D plot, then build and train a 3-layer MLP using max and min temperatures to predict average temperature, with normalization and train-test split.
Los Angeles Temperature Forecasting Part 37:04
Explore training a multi-layer neural network to forecast Los Angeles average temperature, test on unseen data, and visualize results to demonstrate accuracy and scalability to any city.
Source Code0:58
Multilayer Perceptron Neural Networks Quiz
Multilayer Perceptron Neural Networks assignment

Theory of k Nearest Neighbors Classification Method4:38
Required Softwares and Libraries0:08
Use k Nearest Neighbors Classification Method to classify random dataset Part 110:58
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 210:50
Source Code0:12
Learn How to Use k Nearest Neighbors Classification for IRIS Dataset14:38
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.
Source Code0:20
Write k Nearest Neighbors Classification Method by yourself Part 19:14
Learn how to Write k Nearest Neighbors Classification Method by yourself.
Write k Nearest Neighbors Classification Method by yourself Part 28:03
Source Code0:16
k Nearest Neighbors Classification Method Qiuz
k Nearest Neighbors Classification Method Assignment

Theory of Naive Bayes Classification Method4:35
Use the power of Naive Bayes to Classify IRIS Dataset Part 19:27
Learn how to Use Naive Bayes to Classify IRIS Dataset accurately.
Use the power of Naive Bayes to Classify IRIS Dataset Part 26:04
Classify the iris dataset with a Gaussian Naive Bayes classifier, evaluate performance using confusion matrix and classification report, and visualize decision regions to reveal misclassifications.
Source Code0:12
Learn how to Use Naive Bayes to Classify Diabetes dataset11:44
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.
Source Code0:39
Write Naive Bayes Classification Method by Yourself Part 110:29
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 212:13
This lecture demonstrates calculating gaussian probabilities for naive bayes, estimating mean and variance for hate, weight, and food size, and using p(x|y) to classify gender.
Source Code0:11
Naive Bayes Classification Method Quiz
Naive Bayes Classification Method Assignment

Theory of Support Vector Machine Classification Method4:28
Understand support vector machines, a supervised learning method for classification and regression. Learn how SVM maps data to high-dimensional spaces using kernels, finds maximal margin hyperplanes to separate classes.
Support Vector Machine Classification Method for two classes dataset14:45
IN this lecture you will learn how to use Support Vector Machine Classification Method to classify two classes dataset
Source Code0:08
Use the Power of Support Vector Machine Method for IRIS dataset Part 19:54
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 25:59
Source Code0:12
Use Support Vector Machine for Hand Written Images Classification Part 19:53
Use Support Vector Machine for Hand Written Images Classification Part 210:24
Source Code0:18

Logistic Regression Classification Method8:17
Use Logistic Regression Model for Blobs Data sets Classification Part-18:31
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-28:21
Source Code0:25
Learn How to Use Logistic Regression Classifier for IRIS Flowers Classification15:25
Learn How to Use Logistic Regression Classifier for IRIS Flowers Classification with using 4 features of this flower
Source Code0:25
Classify Handwritten Digits Using Logistic Regression15:44
In this lecture you will learn how to Classify Handwritten Digits Using Logistic Regression.
Source Code0:29
Logistic Regression Analysis Quiz
Logistic Regression Analysis Assignment

Linear Regression Theory5:24
Explore the theory of linear regression, linking a dependent variable y with one or more independent variables x using linear predictor functions, including simple linear regression and ordinary least squares.
Required Softwares and Libraries0:08
Use Linear Regression to Create Model for Random Numbers Part-18:50
Use Linear Regression to Create Model for Random Numbers Part-29:38
Source Code0:12
Learn How to Create Linear Regression Model to Predict Diabetes Part-18:13
Learn How to Create Linear Regression Model to Predict Diabetes Part-28:17
Source Code0:13
Linear Regression Model for Boston Houses Data set Part-19:34
Linear Regression Model for Boston Houses Data set Part-28:32
Build and evaluate a linear regression model for the Boston houses data set, training with X_train and y_train, predicting with X_test, and assessing with R2 and MSE.
Source Code0:11
Linear Regression Model for Built-in Data set12:33
Source Code0:09
Linear Regression Analysis Quiz

Multi Linear Regression Theory7:15
Model Global Temperature Using Multilinear Regression Method Part-110:33
Model Global Temperature Using Multilinear Regression Method Part-28:48
Source Code0:16
Make Best Advertising Campaign Using Multilinear Regression Model Part-113:25
Build a practical multilinear regression model to forecast sales from TV, radio, and newspaper advertising data, perform a train-test split, and evaluate coefficients, R-squared, and MSE to guide advertising priorities.
Make Best Advertising Campaign Using Multilinear Regression Model Part-25:25
Source Code0:16
Multi Linear Regression Model for built in dataset13:35
Build a multi linear regression model on a generated dataset with 300 samples and five features, add noise, then train-test split and evaluate with R2 and MSE.
Source Code0:14
Multi Linear Regression Analysis Quiz
Multi Linear Regression Assignment

Polynomial Regression Analysis Theory4:45
Explore polynomial regression theory, modeling nonlinear relationships between x and y with polynomial terms, linear in parameters, using design matrices and least squares to estimate coefficients.
Polynomial Regression Model for Sine Function Part-112:22
Polynomial Regression Model for Sine Function Part-211:00
Learn polynomial regression of a sine function, comparing second and third order fits, evaluating with r squared and plotting results to show accuracy improvements up to near 99 percent.
Source Code0:30
Learn How to Use Polynomial Regression Model for Built-in Dataset Part-17:20
Learn How to Use Polynomial Regression Model for Built-in Dataset Part-27:01
Source Code0:22
Find the Relation between CO2 and Temperature by Polynomial Regression Part-111:48
Explore the relationship between global temperature and atmospheric CO2 using polynomial regression on a 1880–2012 dataset, visualize CO2 and temperature trends, and build a one-dimensional input-output model.
Find the Relation between CO2 and Temperature by Polynomial Regression Part-213:15
Source Code0:39
Polynomial Regression Analysis Quiz
Polynomial Regression Analysis Assignment

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

The Complete Course: Artificial Intelligence From Scratch

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 9min

Learn LSTM Neural Networks26 lectures • 2hr 1min

Learn Multi Layer Perceptron Neural Networks20 lectures • 2hr 25min

k Nearest Neighbors Classification Method10 lectures • 59min

Naive Bayes Classification Method9 lectures • 56min

Support Vector Machine Classification Method9 lectures • 56min

Logistic Regression Classification Method8 lectures • 58min

Linear Regression Analysis13 lectures • 1hr 12min

Multi Linear Regression9 lectures • 1hr

Polynomial Regression Analysis10 lectures • 1hr 9min

Requirements

Description

Who this course is for: