Machine Learning with Python-Business Applications-AI Robot

Machine Learning with Python|Business Applications|AI Robot, available at $54.99, has an average rating of 3.5, with 65 lectures, 7 quizzes, based on 30 reviews, and has 246 subscribers.

You will learn about build a complete intelligent robot that is able to go out of a maze by its own learning ! Achieve the mastery in machine learning from in the reinforcement learning and the classification tracks. Get a deeper intuition about different Machine Learning nomenclatures. Write different kinds of algorithms from scratch with Python. Learn the python programming language to the advanced levels. Be able to preprocess any kind of Datasets. Solve and Deal with different real-life and businesses problems from the outside world. Deal with different machine learning and data science libraries like: Sikit-Learn, Pandas , NumPy & Matplotlib. Explore the Data science world by handling, prepossessing and visualizing any kind of data set. Make designs with advanced ML algorithms like the Reinforcement Leaning and handle different projects with the Gym library . Design the logistic regression classifier Algorithm with python. Design the decision trees classifier Algorithm with python. Design the random forest classifier Algorithm with python. Design the decision trees classifier Algorithm with python. Design the Naive Bayes Classifier Algorithm with python. Design the Support Vector Machine Classifier Algorithm with python . Design the Kernel Support Vector Machine Classifier Algorithm with python. Design the K-Nearest Neighbor Classifier Algorithm with python. Learn how to Evaluate the different Classification Models Design the Q-Learning Algorithm with python. This course is ideal for individuals who are machine learning students or machine leaning engineers or data science students or data scientists or python programming language students or python programming language developers or R programming language developers and students or artificial intelligence students It is particularly useful for machine learning students or machine leaning engineers or data science students or data scientists or python programming language students or python programming language developers or R programming language developers and students or artificial intelligence students.

Enroll now: Machine Learning with Python|Business Applications|AI Robot

Summary

Title: Machine Learning with Python|Business Applications|AI Robot

Price: $54.99

Average Rating: 3.5

Number of Lectures: 65

Number of Quizzes: 7

Number of Published Lectures: 65

Number of Published Quizzes: 7

Number of Curriculum Items: 77

Number of Published Curriculum Objects: 77

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

build a complete intelligent robot that is able to go out of a maze by its own learning !

Achieve the mastery in machine learning from in the reinforcement learning and the classification tracks.

Get a deeper intuition about different Machine Learning nomenclatures.

Write different kinds of algorithms from scratch with Python.

Learn the python programming language to the advanced levels.

Be able to preprocess any kind of Datasets.

Solve and Deal with different real-life and businesses problems from the outside world.

Deal with different machine learning and data science libraries like: Sikit-Learn, Pandas , NumPy & Matplotlib.

Explore the Data science world by handling, prepossessing and visualizing any kind of data set.

Make designs with advanced ML algorithms like the Reinforcement Leaning and handle different projects with the Gym library .

Design the logistic regression classifier Algorithm with python.

Design the decision trees classifier Algorithm with python.

Design the random forest classifier Algorithm with python.

Design the decision trees classifier Algorithm with python.

Design the Naive Bayes Classifier Algorithm with python.

Design the Support Vector Machine Classifier Algorithm with python .

Design the Kernel Support Vector Machine Classifier Algorithm with python.

Design the K-Nearest Neighbor Classifier Algorithm with python.

Learn how to Evaluate the different Classification Models

Design the Q-Learning Algorithm with python.

Who Should Attend

machine learning students

machine leaning engineers

data science students

data scientists

python programming language students

python programming language developers

R programming language developers and students

artificial intelligence students

Target Audiences

machine learning students

machine leaning engineers

data science students

data scientists

python programming language students

python programming language developers

R programming language developers and students

artificial intelligence students

by the end of this course you will be able to construct your own artificial intelligence software robot !

Hello everyone,

If the word ‘Machine Learning’ baffles your mind and you want to master it, then this Machine Learning course is for you.

If you want to start your career in Machine Learning and make money from it, then this Machine Learning course is for you.

If you want to learn how to manipulate things by learning the Math beforehand and then write a code with python, then this Machine Learning course is for you.

If you get bored of the word ‘this Machine Learning course is for you’, then this Machine Learning course is for you.

Well, machine learning is becoming a widely-used word on everybody’s tongue, and this is reasonable as data is everywhere, and it needs something to get use of it and unleash its hidden secrets, and since humans’ mental skills cannot withstand that amount of data, it comes the need to learn machines to do that for us.

So we introduce to you the complete ML course that you need in order to get your hand on Machine Learning and Data Science, and you’ll not have to go to other resources, as this ML course collects most of the knowledge that you’ll need in your journey.

Our course is structured as follows:

1. An intuition of the algorithm and its applications.

2. The mathematics that lies under the hood.

3. Coding with python from scratch.

4. Assignments to get your hand dirty with machine learning.

5. Learn more about different Python Data science libraries like Pandas, NumPy & Matplotlib.

6. Learn more about different Python Machine learning libraries like SK-Learn & Gym.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We’ll cover the following: 

Logistic Regression

K-Nearest Neighbors (K-NN)

Support Vector Machines (SVM)

Kernel SVM

Naive Bayes

Decision Tree Classification

Random Forest Classification

Evaluating Models’ Performance

Reinforcement learning Q-leaning algorithm

Note: this course is continuously updated ! So new algorithmsand assignments are added in order to cope with the different problems from the outside world and to give you a huge arsenal of algorithms to deal with. Without any other expenses.

And as a bonus, this course includes Python code templates which you can download and use on your own projects.

the best part of this machine leaning course is that is cope with all machine leaning students levels
if you are a very beginner in machine leaning so this machine learning course is for you
and if your machine learning level is intermediate so this machine leaning course is also for you
and if you have an advanced level in machine leaning so this machine leaning is also for you
as we re discussing many machine leaning algorithms that has many machine learning steps to be suitable for all machine leaning students

Course Curriculum

Chapter 1: Reinforcement Learning

Lecture 1: Idea behind the reinforcement learning

Lecture 2: Reinforcement learning essentials

Lecture 3: Temporal Difference Leaning_1

Lecture 4: Temporal Difference Leaning_2

Lecture 5: Q-leaning algorithm_1

Lecture 6: Q-learning algorithm_2

Lecture 7: Exploring the Gym frozen lake environment

Lecture 8: Instilling Anaconda

Lecture 9: overview on jupyter notebook

Lecture 10: Installing the Gym library

Lecture 11: Python for Q-leaning|solving the frozen lake environment_1

Lecture 12: Python for Q-leaning|solving the frozen lake environment_2

Lecture 13: Python for Q-leaning|solving the frozen lake environment_3

Lecture 14: Python for Q-leaning|solving the frozen lake environment_4

Chapter 2: ///////////////// The Classification Algorithms \\\\\\\\\

Lecture 1: Classification Algorithms Resources

Chapter 3: Logistic Regression Classifier

Lecture 1: Idea behind Logistic Regression

Lecture 2: The Hypothesis Function of the Logistic Regression

Lecture 3: Example on the hypothesis function of logistic regression

Lecture 4: Cost function of logistic regression

Lecture 5: Estimating the parameters Thetas of the cost function

Lecture 6: Python for logistic regression | SKlearn generated Data_1

Lecture 7: Python for logistic regression | SKlearn generated Data_2

Lecture 8: Python for logistic regression |Spam Filter Problem Simulation

Lecture 9: Python for logistic regression |Buying Houses Business Problem

Lecture 10: Multi-Class Logistic Regression|One Vs All Algorithm

Lecture 11: Over fitting / under fitting Problem optimization

Lecture 12: python for multi-class logistic regression |Hotels Evaluation Business Problem

Chapter 4: Naive Bayes Classifier

Lecture 1: Basics of Probability

Lecture 2: The Bayes Theorem

Lecture 3: Idea behind Naive Bayes Classifier

Lecture 4: Manual example on the Gaussian Naive Bayes

Lecture 5: Multinomial Naive Bayes for Emails Classification_1

Lecture 6: Multinomial Naive Bayes for Emails Classification_2

Lecture 7: python for Gaussian Naive Bayes|Hiring New Applicants Business Problem

Lecture 8: python for Multinomial Naive Bayes|Email Classification Problem_1

Lecture 9: python for Multinomial Naive Bayes|Email Classification Problem_2

Chapter 5: Decision Trees Classifier

Lecture 1: Idea behind Decision Trees Classifier

Lecture 2: Decision Trees overfitting/ underfitting optimization

Lecture 3: The entropy algorithm for decision trees classifier

Lecture 4: Installing GraphViz software

Lecture 5: Python for decision trees|Website Campaign Business Problem_1

Lecture 6: Python for decision trees|Website Campaign Business Problem_2

Chapter 6: Random Forest Classifier

Lecture 1: Idea behind the random forest classifier

Lecture 2: Python for random forest|Website Campaign Business Problem

Chapter 7: Support Vector Machine Classifier

Lecture 1: Idea behind the support vector machine classifier

Lecture 2: Hypothesis Function of the SVM

Lecture 3: Cost Function Regularization for the SVM

Lecture 4: Python for support vector machine|Bank Credit Cards Business Problem

Lecture 5: Python for Support vector machine|SKlearn Generated Data

Lecture 6: Idea behind the hand-written digits recognition

Lecture 7: Python for support vector machine|Hand-written Digits Recognition

Chapter 8: Kernel Support Vector Machines

Lecture 1: Idea behind kernel support vector machines

Lecture 2: Similarity function of the Kernel SVM ( Kernel Trick )

Lecture 3: Example on the Kernel Trick

Lecture 4: Types of Kernel Functions

Lecture 5: Python for the Gaussian Kernel SVM|Solving Bank Credit Cards Business Problem

Lecture 6: Python for the Gaussian Kernel SVM|optimizing the model results

Lecture 7: Python for Kernel SVM |SKlearn Breast Cancer Dataset

Lecture 8: Python for Kernel SVM |Gaussian – Sigmoid – Polynomial) Kernels

Chapter 9: K-Nearest Neighbor Classifier

Lecture 1: Idea Behind K-Nearest Neighbor

Lecture 2: Manual solved example on K-Nearest Neighbor

Lecture 3: Python for K-Nearest Neighbor|Buying Houses Business Problem

Lecture 4: Python for K-Nearest Neighbor|SKLearn Iris Data set

Chapter 10: Classification Models Evaluation

Lecture 1: analyze the results using the confusion matrix

Lecture 2: the use of the evaluation parameters

Instructors

United Engineering
Learning By Doing

Rating Distribution

1 stars: 2 votes

2 stars: 1 votes

3 stars: 10 votes

4 stars: 9 votes

5 stars: 8 votes

Frequently Asked Questions

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