Complete Machine Learning & Reinforcement learning 2023

Complete Machine Learning & Reinforcement learning 2023, available at $74.99, has an average rating of 4.4, with 188 lectures, 8 quizzes, based on 175 reviews, and has 1466 subscribers.

You will learn about Achieve the mastery in machine learning from simple linear regression to advanced reinforcement learning projects. Get a deeper intuition about different Machine Learning nomenclatures. Be able to manipulate different algorithms with the power of Mathematics. Write different kinds of algorithms from scratch with Python. 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 . This course is ideal for individuals who are Newbies to Machine Learning. or Any one who wants to boost his skills in Data Science and Machine Learning with Mathematics. or Any people who are not satisfied with their job and who want to become a Data Scientist. It is particularly useful for Newbies to Machine Learning. or Any one who wants to boost his skills in Data Science and Machine Learning with Mathematics. or Any people who are not satisfied with their job and who want to become a Data Scientist.

Enroll now: Complete Machine Learning & Reinforcement learning 2023

Summary

Title: Complete Machine Learning & Reinforcement learning 2023

Price: $74.99

Average Rating: 4.4

Number of Lectures: 188

Number of Quizzes: 8

Number of Published Lectures: 188

Number of Published Quizzes: 7

Number of Curriculum Items: 198

Number of Published Curriculum Objects: 196

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Achieve the mastery in machine learning from simple linear regression to advanced reinforcement learning projects.
Get a deeper intuition about different Machine Learning nomenclatures.
Be able to manipulate different algorithms with the power of Mathematics.
Write different kinds of algorithms from scratch with Python.
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 .

Who Should Attend

Newbies to Machine Learning.
Any one who wants to boost his skills in Data Science and Machine Learning with Mathematics.
Any people who are not satisfied with their job and who want to become a Data Scientist.

Target Audiences

Newbies to Machine Learning.
Any one who wants to boost his skills in Data Science and Machine Learning with Mathematics.
Any people who are not satisfied with their job and who want to become a Data Scientist.

Humans learn from past experience, so why not machine learn as well?

Hello there,

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 datais everywhere, and it needs something to get use of it and unleashits 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 completeMLcourse 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.

We believe that the brainloves to keep the information that it finds funnyand applicable, and that’s what we’re doing here in SkyHub Academy, we give you years of experience from our instructors that have been gathered in just one an interesting dose.

Our course is structured as follows:

An intuition of the algorithm and its applications.
The mathematics that lies under the hood.
Coding with python from scratch.
Assignments to get your hand dirty with machine learning.
Learn more about different Python Data science libraries like Pandas, NumPy & Matplotlib.
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:�

Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Lasso Regression
Ridge Regression
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machines (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Evaluating Models’ Performance
Hierarchical Clustering
K-Means Clustering
Principle Component Analysis (PCA)
Pandas (Python Library for Handling Data)
Matplotlib (Python Library for Visualizing Data)

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Lecture 2: Course Guide

Lecture 3: Machine Learning Analogy

Lecture 4: Supervised Learning

Lecture 5: Unsupervised, Semi-Supervised and Reinforcement Learning

Chapter 2: ……………… Supervised Learning ………………

Lecture 1: Fasten your Belt and Enjoy the Ride!

Chapter 3: —————– Regression —————–

Lecture 1: Welcome to the Regression World!

Chapter 4: Simple Linear Regression

Lecture 1: The Essence of Simple Linear Regression (Housing Data Analysis)

Lecture 2: Mathematics 1: The Hypothesis Function

Lecture 3: Mathematics 2: The Cost Function

Lecture 4: Mathematics 3: The Essence of The Gradient Descent

Lecture 5: Mathematics 4: How GD Works?

Lecture 6: Query 1: What about the Initialization?

Lecture 7: Query 2: How to Adjust the Speed of Algorithm?

Lecture 8: Query 3: What if it Was a Non-Convex Function?

Lecture 9: Polymerization Between Gradient and Hypothesis

Lecture 10: Let's Start Coding!

Lecture 11: Hello Anaconda!

Lecture 12: Hello Jupyter Notebook!

Lecture 13: Python 1: Required Libraries and Importing Data

Lecture 14: What is The Unicode?

Lecture 15: Python 2: Handling Data ( iloc Function )

Lecture 16: Python 3: Handling Data ( Splitting Data into Train and Test Sets )

Lecture 17: Python 4: Defining Main Function

Lecture 18: Python 5: Defining The Gradient Descent Algorithm

Lecture 19: Python 6: Debugging

Lecture 20: Python 7: Scaling Data

Lecture 21: Python 8: Defining Cost Function

Lecture 22: Mathematics 5: SGD (Stochastic Gradient Descent)

Lecture 23: Python 9: Stochastic Gradient Descent

Chapter 5: Multiple Linear Regression

Lecture 1: Welcome to Multiple Linear Regression

Lecture 2: Basic Statistics and P-Value

Lecture 3: R-Squared

Lecture 4: The Essence of Multiple Linear Regression

Lecture 5: Interpreting Coefficients in MLR

Lecture 6: Preparation Steps 1: MLR Analysis (Business Problem Analysis)

Lecture 7: Preparation Steps 2: Checking Linearity

Lecture 8: Preparation Steps 3: Correlation Analysis

Lecture 9: Preparation Steps 4: Single Variable Regressions

Lecture 10: Preparation Steps 5: Multiple Variable Regression

Lecture 11: Choosing Best MLR Model

Lecture 12: The Essence of Dummy Variables

Lecture 13: Applying Multiple Linear Regression Using Excel

Lecture 14: Python 1: MLR (Stock Price Prediction)

Lecture 15: Python 2: MLR (Stock Price Prediction)

Lecture 16: Python 3: MLR Assignment (Human Life Expectancy)

Lecture 17: Python 4: MLR Assignment (Human Life Expectancy)

Chapter 6: Ridge & Lasso Regression

Lecture 1: Python 1: Ridge Regression (Business Problem)

Lecture 2: L1 & L2 Regularization Techniques

Lecture 3: Python 2: Ridge Regression (Business Problem)

Lecture 4: Python 3: Ridge Regression (Business Problem)

Lecture 5: Python 4: Lasso Regression (Business Problem)

Chapter 7: Polynomial Regression

Lecture 1: The Essence of Residual Plots

Lecture 2: Polynomial Regression VS Quadratic Regression

Lecture 3: The Essence of Over-fitting

Lecture 4: Python: Polynomial Regression

Chapter 8: Decision Trees & Random Forests Regression

Lecture 1: The Essence of Decision Trees Regressor

Lecture 2: Python 1: Regression Trees (Petrol Consumption Prediction)

Lecture 3: Python 2: Regression Trees (Business Problem)

Lecture 4: The Essence of Random Forests Regression

Chapter 9: —————– CLASSIFICATION —————–

Lecture 1: Welcome to the Classification World!

Chapter 10: Logistic Regression Classifier

Lecture 1: The Essence of Logistic Regression Classifier

Lecture 2: Mathematics 1: Logistic Regression ( The Hypothesis Function )

Lecture 3: Mathematics 2: Logistic Regression ( Examples On The Hypothesis Function )

Lecture 4: Mathematics 3: Logistic Regression ( The Cost Function )

Lecture 5: Mathematics 4: Logistic Regression ( Estimating the parameters Thetas )

Lecture 6: Python 1: Logistic Regression ( SKlearn generated Data_1 )

Lecture 7: Python 2: Logistic Regression ( SKlearn generated Data_2 )

Lecture 8: Python 3: Logistic Regression ( Spam Filter Problem Simulation )

Lecture 9: Python 4: Logistic Regression (Buying Houses Business Problem )

Lecture 10: Multi-Class Logistic Regression ( One Vs All Algorithm ) !

Lecture 11: Logistic Regression Optimization ( Overfitting Problem )

Lecture 12: Python 5: Multi-Class Logistic Regression ( Hotels Evaluation Business Problem )

Chapter 11: Decision Tree VS Random Forest Classifiers

Lecture 1: The Essence of Decision Trees classifier

Lecture 2: Decision Trees Optimization (Overfitting Problem)

Lecture 3: Mathematics: Decision Trees (The Entropy Algorithm)

Lecture 4: Installing GV

Lecture 5: Python 1: Decision Trees (Website Campaign Business Problem)

Lecture 6: Python/GV 2: Optimizing DT Algorithm Results (Website Campaign Business Problem)

Lecture 7: The Essence of Random Forest Classifier

Lecture 8: Python 3: Random Forest (Website Campaign Business Problem)

Chapter 12: Naive Bayes Classifier

Lecture 1: Mathematics 1: Probability Basics

Instructors

SkyHub Academy
Learn with Passion Learn with Fun
Ahmed Attia
Electrical Engineer

Rating Distribution

1 stars: 3 votes
2 stars: 10 votes
3 stars: 34 votes
4 stars: 50 votes
5 stars: 78 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!

Menu