Free download Udemy Machine Learning A-Z™: Hands-On Python
& R In Data Science created by Kirill
Eremenko, Hadelin de
Ponteves, SuperDataScience
Support and Ligency Team.
Download today and learn to create Machine Learning Algorithms in Python and R
from two Data Science experts. Code templates included.
This course includes:
- 44 hours on-demand video
- 73 articles
- 38 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
You can enroll the course clicking here
What you will 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
Course content
45 sections • 320
lectures • 44h 29m total length
- Welcome to the course! Here we will help you get started in the best conditions.
- ---------------------------- Part 1: Data Preprocessing -------------------------------
- Data Preprocessing in Python
- Data Preprocessing in R
- ------------------------------ Part 2: Regression ---------------------------------------
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Decision Tree Regression
- Random Forest Regression
- Evaluating Regression Models Performance
- Regression Model Selection in Python
- Regression Model Selection in R
- ------------------------------ Part 3: Classification -------------------------------------
- Logistic Regression
- K-Nearest Neighbors (K-NN)
- Support Vector Machine (SVM)
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
- Classification Model Selection in Python
- Evaluating Classification Models Performance
- ------------------------------ Part 4: Clustering -----------------------------------------
- K-Means Clustering
- Hierarchical Clustering
- -------------------------- Part 5: Association Rule Learning ---------------------------
- Apriori
- Eclat
- --------------------------- Part 6: Reinforcement Learning -----------------------------
- Upper Confidence Bound (UCB)
- Thompson Sampling
- ------------------------- Part 7: Natural Language Processing --------------------------
- ------------------------------ Part 8: Deep Learning -------------------------------------
- Artificial Neural Networks
- Convolutional Neural Networks
- -------------------------- Part 9: Dimensionality Reduction ----------------------------
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Kernel PCA
- -------------------------- Part 10: Model Selection & Boosting -------------------------
- Model Selection
- XGBoost
- Bonus Lectures
Requirements
- 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 that
are based on real-life 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 this course is for:
- 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.
Instructors
Kirill
Eremenko - Data Scientist
My name is Kirill Eremenko and I am super-psyched that you
are reading this!
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.
To sum up, I am absolutely and utterly passionate about Data
Science and I am looking forward to sharing my passion and knowledge with you!
Hadelin
de Ponteves - AI Entrepreneur
Hadelin is the co-founder and CEO at BlueLife AI, which leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by innovating, automating processes and maximizing efficiency. Hadelin is also an online entrepreneur who has created top-rated educational e-courses to the world on topics such as Machine Learning, Artificial Intelligence and Blockchain.
Summary in numbers:
- 2M+ online courses sold.
- 1.3M students.
- 28 online courses created.
- 4.5/5 instructor average rating.
- 2 books written.
- Instagram: @hadelin2p
SuperDataScience
Support-Answering All Your Questions
Hi there,
We are the SuperDataScience Support 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
Ligency
Team - Helping Data Scientists Succeed
Hi there,
We are the Ligency PR and Marketing team. You will be
hearing from us when new courses are released, when we publish new podcasts,
blogs, share cheatsheets and more!
We are here to help you stay on the cutting edge of Data
Science and Technology.
See you in class,
Sincerely,
The Real People at Ligency
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