Complete Machine Learning & Reinforcement learning 2023 from Udemy

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 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.

We believe that the brain loves to keep the information that it finds funny and 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 algorithms and 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.

What's inside

Learning objectives

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 .

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 .

Syllabus

Introduction

Course Introduction

Course Guide

Machine Learning Analogy

Supervised Learning

Unsupervised, Semi-Supervised and Reinforcement Learning

.................. Supervised Learning ..................

Fasten your Belt and Enjoy the Ride!

----------------- Regression -----------------

Welcome to the Regression World!

Simple Linear Regression

The Essence of Simple Linear Regression (Housing Data Analysis)

Mathematics 1: The Hypothesis Function

Mathematics 2: The Cost Function

Mathematics 3: The Essence of The Gradient Descent

Mathematics 4: How GD Works?

Start where you're. Use what you've. Do what you can!

Query 1: What about the Initialization?

Query 2: How to Adjust the Speed of Algorithm?

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

Polymerization Between Gradient and Hypothesis

Don't watch the clock. Do what it does. Keep going!

Let's Start Coding!

Hello Anaconda!

Hello Jupyter Notebook!

Python 1: Required Libraries and Importing Data

What is The Unicode?

Python 2: Handling Data ( iloc Function )

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

Python 4: Defining Main Function

Python 5: Defining The Gradient Descent Algorithm

Python 6: Debugging

Python 7: Scaling Data

Python 8: Defining Cost Function

Mathematics 5: SGD (Stochastic Gradient Descent)

Python 9: Stochastic Gradient Descent

Multiple Linear Regression

Welcome to Multiple Linear Regression

Basic Statistics and P-Value

R-Squared

The Essence of Multiple Linear Regression

Easy? No. Worth it? Absolutely.

Interpreting Coefficients in MLR

Preparation Steps 1: MLR Analysis (Business Problem Analysis)

Preparation Steps 2: Checking Linearity

Preparation Steps 3: Correlation Analysis

Success requires Effort.

Preparation Steps 4: Single Variable Regressions

Preparation Steps 5: Multiple Variable Regression

Choosing Best MLR Model

The Essence of Dummy Variables

Don't stop when you're tired. Stop when you're done!

Applying Multiple Linear Regression Using Excel

Python 1: MLR (Stock Price Prediction)

Python 2: MLR (Stock Price Prediction)

Python 3: MLR Assignment (Human Life Expectancy)

Python 4: MLR Assignment (Human Life Expectancy)

Life Expectancy Assignment (Kaggle Problem)

Ridge & Lasso Regression

Python 1: Ridge Regression (Business Problem)

L1 & L2 Regularization Techniques

Python 2: Ridge Regression (Business Problem)

Python 3: Ridge Regression (Business Problem)

Python 4: Lasso Regression (Business Problem)

Polynomial Regression

The Essence of Residual Plots

Polynomial Regression VS Quadratic Regression

The Essence of Over-fitting

Python: Polynomial Regression

Decision Trees & Random Forests Regression

The Essence of Decision Trees Regressor

Python 1: Regression Trees (Petrol Consumption Prediction)

Python 2: Regression Trees (Business Problem)

The Essence of Random Forests Regression

----------------- CLASSIFICATION -----------------

Welcome to the Classification World!

Logistic Regression Classifier

The Essence of Logistic Regression Classifier

Mathematics 1: Logistic Regression ( The Hypothesis Function )

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

Mathematics 3: Logistic Regression ( The Cost Function )

Mathematics 4: Logistic Regression ( Estimating the parameters Thetas )

Python 1: Logistic Regression ( SKlearn generated Data_1 )

visualization code source
Gaël Varoquaux, Modified for documentation by Jaques Grobler, License: BSD 3 clause

Python 3: Logistic Regression ( Spam Filter Problem Simulation )

Python 4: Logistic Regression (Buying Houses Business Problem )

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

Logistic Regression Optimization ( Overfitting Problem )

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

Decision Tree VS Random Forest Classifiers

The Essence of Decision Trees classifier

Decision Trees Optimization (Overfitting Problem)

Mathematics: Decision Trees (The Entropy Algorithm)

Installing GV

Python 1: Decision Trees (Website Campaign Business Problem)

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

The Essence of Random Forest Classifier

Python 3: Random Forest (Website Campaign Business Problem)

Naive Bayes Classifier

Mathematics 1: Probability Basics

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides an intuitive understanding of algorithms and their mathematical foundation with hands-on coding in Python

Covers a comprehensive range of topics from simple linear regression to advanced reinforcement learning projects

Emphasizes practical application by providing assignments and code templates to solve real-life problems

Designed for individuals with no prior machine learning knowledge, making it accessible to beginners

Provides an opportunity to explore various machine learning and data science libraries such as Pandas, NumPy, and Matplotlib

Taught by experienced instructors with a proven track record in the field of machine learning

Reviews summary

Difficult to understand.

Learners say this course is difficult to understand because the instructors have strong accents and the captions don't work properly.

Captions don't work properly.

"The captions which I assume are auto-generated don’t work and create words such as ‘badass’ and other nonsense words from the instructors’ words."

Instructors have difficult accents.

"The instructors' accents are very thick and make the course very difficult to understand."

"I found it extremely difficult to get into tune with the bad English. It's almost like the course is being delivered in a foreign language!"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete Machine Learning & Reinforcement learning 2023 with these activities:

Form a study group with your classmates

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Forming a study group with your classmates will provide you with a supportive environment to discuss concepts and learn from each other.

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Reach out to your classmates and find those who are interested in forming a study group.
Set regular meeting times and decide on a study schedule.
Prepare for each meeting by reviewing the assigned material and completing any assigned problems.

Review basic Math knowledge

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Reviewing basic math concepts will strengthen your foundation for the more advanced topics covered in this course.

Browse courses on Math

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Review algebra concepts such as solving equations, inequalities, and systems of equations.
Practice basic calculus concepts such as derivatives and integrals.
Review trigonometry concepts such as sine, cosine, and tangent.

Read 'Machine Learning: A Probabilistic Perspective'

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Reading 'Machine Learning: A Probabilistic Perspective' will provide you with a comprehensive understanding of the probabilistic foundations of machine learning, which is essential for understanding more advanced machine learning algorithms.

View Probabilistic Machine Learning: Advanced Topics... on Amazon

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Acquire a copy of 'Machine Learning: A Probabilistic Perspective'.
Read the book thoroughly, taking notes and highlighting important concepts.
Solve the exercises at the end of each chapter to reinforce your understanding.

Five other activities

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Follow tutorials on Python data manipulation

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Following tutorials on Python data manipulation will provide you with hands-on experience and help you develop the skills necessary for the course.

Browse courses on Python

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Find tutorials on data manipulation in Python using libraries such as Pandas and NumPy.
Follow the tutorials step-by-step and practice the concepts.
Experiment with different data manipulation techniques and explore the capabilities of the libraries.

Attend a machine learning workshop

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Attending a machine learning workshop will provide you with an opportunity to learn from experts, network with other professionals, and stay up-to-date on the latest trends in the field.

Browse courses on Machine Learning

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Find a machine learning workshop that aligns with your interests.
Register for the workshop and attend the sessions.
Participate in discussions and ask questions to enhance your understanding.

Solve coding problems on LeetCode

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Solving coding problems on LeetCode will challenge you and help you develop your problem-solving and coding skills, which are essential for success in machine learning.

Browse courses on Coding

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Create an account on LeetCode.
Start solving easy problems and gradually move on to more difficult ones.
Analyze the solutions to the problems and learn from different approaches.

Write a blog post on a machine learning concept

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Writing a blog post on a machine learning concept will help you solidify your understanding of the concept and improve your communication skills.

Browse courses on Machine Learning

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Choose a machine learning concept that you are familiar with.
Research the concept and gather information from reliable sources.
Write a clear and concise blog post explaining the concept and providing examples.

Build a machine learning model to predict customer churn

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Building a machine learning model to predict customer churn will provide you with hands-on experience applying machine learning techniques to a real-world problem.

Browse courses on Machine Learning

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Gather data on customer churn.
Preprocess the data and prepare it for modeling.
Train and evaluate different machine learning models.
Deploy the best model and monitor its performance.

Career center

Learners who complete Complete Machine Learning & Reinforcement learning 2023 will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, and implement machine learning models. This course dives deep into the mathematics, coding, algorithms, and applications of machine learning, which will all be helpful skills for a Machine Learning Engineer to have.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data. This course would help build a foundation for this role by teaching the mathematics and coding that is necessary to manipulate data from its raw form into something useful. A data scientist can use this information to solve complex business problems.

See salaries and explore the career path for Data Scientist

Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and test artificial intelligence systems. This course covers machine learning algorithms that are needed in AI, and could be helpful for those wishing to enter this field.

See salaries and explore the career path for Artificial Intelligence Engineer

Data Analyst

Data Analysts uncover meaningful insights from data for businesses to make data-driven decisions. This course can provide foundational knowledge of machine learning that can help Data Analysts take the next step in their career and advance to more senior roles.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts analyze business processes and make recommendations for improvements. This course could be helpful for them to gain deeper insights into the data they are working with.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines. This course may be useful for Data Engineers who want to learn more about machine learning algorithms.

See salaries and explore the career path for Data Engineer

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course may be useful for Operations Research Analysts who want to learn more about machine learning algorithms.

See salaries and explore the career path for Operations Research Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. This course may be useful for Product Managers who want to learn more about machine learning algorithms.

See salaries and explore the career path for Product Manager

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course may be useful for Quantitative Analysts who want to learn more about machine learning algorithms.

See salaries and explore the career path for Quantitative Analyst

Software Engineer

Software Engineers design, develop, test, and maintain software systems. This course may be helpful for Software Engineers who wish to specialize in machine learning or data science.

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Financial Analyst

Financial Analysts evaluate and make recommendations on investments. This course may be helpful for Financial Analysts who want to learn more about machine learning algorithms.

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Actuary

Actuaries analyze and manage risk for insurance companies and other financial institutions. This course may be useful for Actuaries who want to gain some basic knowledge of machine learning algorithms.

See salaries and explore the career path for Actuary

Market Researcher

Market Researchers conduct research on consumer behavior and market trends. This course may be useful for Market Researchers who want to learn more about machine learning algorithms.

See salaries and explore the career path for Market Researcher

Statistician

Statisticians collect, analyze, interpret, and present data. This course may be useful for Statisticians who want to expand their knowledge into machine learning algorithms.

See salaries and explore the career path for Statistician

Technical Writer

Technical Writers create documentation for software and other technical products. This course may be useful for Technical Writers who want to learn more about machine learning concepts and algorithms.

See salaries and explore the career path for Technical Writer