Machine Learning A-Z Python R

Course Machine Learning A-Z: Hands-On Python & R In Data Science

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

 

Language: English

Platform: Udemy

 

What you´ll learn

  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make powerful analysis
  • Make robust Machine Learning models
  • Create strong added value to your business
  • Use Machine Learning for personal purpose
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem

 

Prerequisites

  • Just some high school mathematics level

 

Description

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:

  • Part 1 – Data Preprocessing
  • Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
  • Part 4 – Clustering: K-Means, Hierarchical Clustering
  • Part 5 – Association Rule Learning: Apriori, Eclat
  • Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
  • Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
  • Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
  • Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
  • Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Moreover, the course is packed with practical exercises which are based on live examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

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

 

Who is the target audience?

  • Anyone interested in Machine Learning
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning tools

 

Course content:

Welcome to the course!

  • Applications of Machine Learning
  • Why Machine Learning is the Future
  • Installing R and R Studio (MAC & Windows)
  • Installing Python and Anaconda (MAC & Windows)
Part 1: Data Preprocessing
  • Welcome to Part 1 – Data Preprocessing
  • Get the dataset
  • Importing the Libraries
  • Importing the Dataset
  • For Python learners, summary of Object-oriented programming: classes & objects
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling
  • And here is our Data Preprocessing Template!
  • Data Preprocessing
Part 2: Regression
  • Welcome to Part 2 – Regression
Simple Linear Regression
  • How to get the dataset
  • Dataset Business Problem Description
  • Simple Linear Regression Intuition – Step 1
  • Simple Linear Regression Intuition – Step 2
  • Simple Linear Regression in Python – Step 1
  • Simple Linear Regression in Python – Step 2
  • Simple Linear Regression in Python – Step 3
  • Simple Linear Regression in Python – Step 4
  • Simple Linear Regression in R – Step 1
  • Simple Linear Regression in R – Step 2
  • Simple Linear Regression in R – Step 3
  • Simple Linear Regression in R – Step 4
  • Simple Linear Regression
Multiple Linear Regression
  • How to get the dataset
  • Dataset Business Problem Description
  • Multiple Linear Regression Intuition – Step 1
  • Multiple Linear Regression Intuition – Step 2
  • Multiple Linear Regression Intuition – Step 3
  • Multiple Linear Regression Intuition – Step 4
  • Multiple Linear Regression Intuition – Step 5
  • Multiple Linear Regression in Python – Step 1
  • Multiple Linear Regression in Python – Step 2
  • Multiple Linear Regression in Python – Step 3
  • Multiple Linear Regression in Python – Backward Elimination – Preparation
  • Multiple Linear Regression in Python – Backward Elimination – HOMEWORK !
  • Multiple Linear Regression in Python – Backward Elimination – Homework Solution
  • Multiple Linear Regression in R – Step 1
  • Multiple Linear Regression in R – Step 2
  • Multiple Linear Regression in R – Step 3
  • Multiple Linear Regression in R – Backward Elimination – HOMEWORK !
  • Multiple Linear Regression in R – Backward Elimination – Homework Solution
  • Multiple Linear Regression
Polynomial Regression
  • Polynomial Regression Intuition
  • How to get the dataset
  • Polynomial Regression in Python – Step 1
  • Polynomial Regression in Python – Step 2
  • Polynomial Regression in Python – Step 3
  • Polynomial Regression in Python – Step 4
  • Python Regression Template
  • Polynomial Regression in R – Step 1
  • Polynomial Regression in R – Step 2
  • Polynomial Regression in R – Step 3
  • Polynomial Regression in R – Step 4
  • R Regression Template
Support Vector Regression (SVR)
  • How to get the dataset
  • SVR in Python
  • SVR in R
Decision Tree Regression
  • Decision Tree Regression Intuition
  • How to get the dataset
  • Decision Tree Regression in Python
  • Decision Tree Regression in R
Random Forest Regression
  • Random Forest Regression Intuition
  • How to get the dataset
  • Random Forest Regression in Python
  • Random Forest Regression in R
Evaluating Regression Models Performance
  • R-Squared Intuition
  • Adjusted R-Squared Intuition
  • Evaluating Regression Models Performance – Homework’s Final Part
  • Interpreting Linear Regression Coefficients
  • Conclusion of Part 2 – Regression

About the instructors

Kirill Eremenko

My name is Kirill Eremenko and I am super-psyched that you are reading this!

I teach courses in two distinct Business areas on Udemy: Data Science and Forex Trading. I want you to be confident that I can deliver the best training there is, so below is some of my background in both these fields.

Data Science

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

Forex Trading

Since 2007 I have been actively involved in the Forex market as a trader as well as running programming courses in MQL4. Forex trading is something I really enjoy, because the Forex market can give you financial, and more importantly – personal freedom.

In my other life I am a Data Scientist – I study numbers to analyze patterns in business processes and human behaviour… Sound familiar? Yep! Coincidentally, I am a big fan of Algorithmic Trading 🙂 EAs, Forex Robots, Indicators, Scripts, MQL4, even java programming for Forex – Love It All!

Summary

To sum up, I am absolutely and utterly passionate about both Data Science and Forex Trading and I am looking forward to sharing my passion and knowledge with you!

 

Hadelin de Ponteves

Hi. My name is Hadelin de Ponteves. Always eager to learn, I invested a lot of my time in learning and teaching, covering a wide range of different scientific topics.

Today I am passionate about data science, artificial intelligence and deep learning. I will do my very best to convey my passion for data science to you. I have gained diverse experience in this field. I have an engineering master’s degree with a specialisation in data science. I spent one year doing research in machine learning, working on innovative and exciting projects. Then a work experience at Google where I implemented some machine learning models for business analytics.

Eventually, I realised I spent most of my time doing analysis and I gradually needed to feed my creativity so I became an entrepreneur. My courses will combine the two dimensions of analysis and creativity, allowing you to learn all the analytic skills required in data science, by applying it on creative ideas.

Looking forward to working together!

Hello, je m’appelle Hadelin de Ponteves et je suis un data scientist passionné.

Etant particulièrement sensible au domaine de l’éducation, je suis déterminé à y apporter de grandes contributions. J’ai déjà investi beaucoup de mon temps dans la sphère de l’éducation, à étudier et enseigner divers sujets scientifiques.

Aujourd’hui, je suis passionné de data sciences, d’intelligence artificielle et de deep learning. Et je ferai de mon mieux pour vous transmettre mes passions. Car c’est en étant passionné que l’on réussit le mieux dans un domaine, et que l’on est le plus heureux dans notre travail au quotidien.

J’ai acquis beaucoup d’expérience en data sciences. J’ai effectué mes études à l’école Centrale Paris, où j’ai suivi le parcours Data Sciences, en parallèle d’un master de recherche en machine learning à l’Ecole Normale Supérieure. Ma page étudiante s’est enchaînée avec une expérience chez Google où j’ai fait des data sciences pour résoudre des problèmes business. Puis j’ai réalisé que je passais la plupart de mon temps à analyser et je développais petit à petit un besoin de créer. Donc pour nourrir ma créativité, je suis devenu un entrepreneur.

Et justement, mes cours vont tous combiner ces deux dimensions d’analyse et de créativité, grâce auxquelles vous intégrerez toutes les compétences à avoir en data sciences, en les appliquant à des idées créatives.

J’ai hâte de vous retrouver dans mes cours et de partager mes passions avec vous!

 

SuperDataScience Team

Hi there,

We are the SuperDataScience team. You will find us in the Data Science courses taught by Kirill Eremenko – we are here to help you out with any questions and make sure your journey through the courses is always smooth sailing!

The best way to get in touch is to post a discussion in the Q&A of the course you are taking. In most cases we will respond within 24 hours.

We’re passionate about helping you enjoy the courses!

See you in class,

Sincerely,

The Real People at SuperDataScience

 

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