Wednesday 15 November 2017

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.
BEST SELLER
4.5 (22,679 ratings)
142,824 students enrolled
Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team
Last updated 10/2017
 English
 English [Auto-generated]
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Machine Learning A-Z™: Hands-On Python & R In Data Science
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Includes:
  • 40.5 hours on-demand video
  • 19 Articles
  • 2 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I 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
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 which 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 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.
Compare to Other Python Courses
Curriculum For This Course
Expand All
 
276 Lectures
40:47:04
–
Welcome to the course!
23:52
Applications of Machine Learning
Preview03:22
Why Machine Learning is the Future
06:37
Installing R and R Studio (MAC & Windows)
Preview05:40
Update: Recommended Anaconda Version
00:12
Installing Python and Anaconda (MAC & Windows)
07:31
BONUS: Meet your instructors
00:29
+
-------------------- Part 1: Data Preprocessing --------------------
10 Lectures01:42:47
+
-------------------- Part 2: Regression --------------------
1 Lecture00:22
+
Simple Linear Regression
12 Lectures01:25:06
+
Multiple Linear Regression
18 Lectures02:21:46
+
Polynomial Regression
12 Lectures02:09:06
+
Support Vector Regression (SVR)
3 Lectures34:59
+
Decision Tree Regression
4 Lectures49:03
+
Random Forest Regression
4 Lectures44:28
+
Evaluating Regression Models Performance
5 Lectures35:04
+
-------------------- Part 3: Classification --------------------
1 Lecture00:21
+
Logistic Regression
14 Lectures01:41:02
+
K-Nearest Neighbors (K-NN)
4 Lectures38:06
+
Support Vector Machine (SVM)
4 Lectures37:40
+
Kernel SVM
7 Lectures01:04:58
+
Naive Bayes
7 Lectures01:17:38
+
Decision Tree Classification
4 Lectures43:47
+
Random Forest Classification
4 Lectures47:36
+
Evaluating Classification Models Performance
6 Lectures34:52
+
-------------------- Part 4: Clustering --------------------
1 Lecture00:21
+
K-Means Clustering
6 Lectures01:06:56
+
Hierarchical Clustering
15 Lectures01:15:43
+
-------------------- Part 5: Association Rule Learning --------------------
1 Lecture00:11
+
Apriori
8 Lectures01:59:47
+
Eclat
3 Lectures19:32
+
-------------------- Part 6: Reinforcement Learning --------------------
1 Lecture00:26
+
Upper Confidence Bound (UCB)
11 Lectures02:19:49
+
Thompson Sampling
7 Lectures01:16:38
+
-------------------- Part 7: Natural Language Processing --------------------
24 Lectures02:55:53
+
-------------------- Part 8: Deep Learning --------------------
2 Lectures12:58
+
Artificial Neural Networks
24 Lectures03:31:51
+
Convolutional Neural Networks
21 Lectures03:01:43
+
-------------------- Part 9: Dimensionality Reduction --------------------
1 Lecture00:35
+
Principal Component Analysis (PCA)
7 Lectures01:10:07
+
Linear Discriminant Analysis (LDA)
3 Lectures41:27
+
Kernel PCA
3 Lectures38:15
+
-------------------- Part 10: Model Selection & Boosting --------------------
1 Lecture00:30
+
Model Selection
6 Lectures01:16:44
+
XGBoost
4 Lectures43:45
+
Bonus Lectures
1 Lecture02:00
Frequently Bought Together
US$20TotalUS$400
About the Instructors
Kirill Eremenko
Data Scientist & Forex Systems Expert
4.5Average Rating
70264Reviews
283026Students
33Courses
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
AI Entrepreneur
4.5Average Rating
31041Reviews
172400Students
9Courses
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 Machine Learning, Deep Learning and Artificial Intelligence. I will do my very best to convey my passion for AI 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 them on creative ideas. 
Looking forward to working together!

Hello, je m'appelle Hadelin de Ponteves et je suis entrepreneur en Intelligence Artificielle. 
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 cette sphère, à étudier et enseigner divers sujets scientifiques. 
Aujourd'hui, je suis passionné de Machine Learning, Deep Learning et Intelligence Artificielle. 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
Helping Data Scientists Succeed
4.5Average Rating
63241Reviews
256207Students
22Courses
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
Student Feedback
4.5
Average Rating
Reviews
Good. They could give us some source of reference material to read and understand some terms used in the course
S
Smitha
6 days ago
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i am able to learn the course without having any knowledge on python. the course structure that is offered in our college is actually 50% of what you told and i had this such great course only for 10$. This really is amazing course.
BV
Bhavesh veeramachaneni
a month ago
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Very good course to start with machine learning. As a room for improvement, it would be interesting to see 3D plots. There are some repetition in the lectures, but you can skip that.
WA
Wael Almadhoun
a month ago
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Very good course. Covered all the basics of most of the machine learning algorithms. However taking away a 0.5 rating for few reasons: SVR wasn't satisfying because there was no intuition, the Kmeans section in python and R was very hurried, only scratched the surface with XGBoost - was expecting more from this, wish there were more content on feature extraction techniques. All in all this is a course for beginners so that in mind, a very good course to help you get started in machine learning.
KG
Karan Gadiya
2 months ago
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Very Good Course providing structured step by step insight to Machine Learning. Beauty about it is it's relation to real life examples. In addition, what helps is practical implementation using Python and R.
Delighted to take the course and venture deep into the word of Machine Learning
RI
Ramkumar Iyer
2 months ago
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Many thanks for this interesting and eye opening course. I have been coding in ML over years following Geoffrey Hinton and developed applications in C++, Java and C#.
However, this is the first time, I felt so confident that R is more than enough.
Many thanks for the intuitive way of teaching. it was full of fun and encouragement. Specially, making the XGBoost the last lecture was good idea. It is always nicer to go through the basics and then meet the greatest at the end of the tunnel.
Well done
MM
M A Mohamed
2 months ago
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This is an amazing course for those who are interested in Machine Learning, tutors are brilliant and more important pass the message concisely, super recommend it, even if you are new to coding, just like I was!
VC
Vanessa Correia
14 days ago
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I think it's the most complete course in Machine Learning. Great explanations, great examples, great delivery!
Draghici Stefan
2 months ago
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A good practical knowledge of machine learning is something I needed.
Maybe more exercises would help. However there was so much material it was hard to get through it all anyway
Maybe an accompanying maths course to gain intuitive knowledge of concepts e.g eigen values and vectors of co-variance matrix for PCA and how this code is formed. More maths explanation from a sideline site maybe.
CM
Cathal Murray
2 months ago
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One of the best investments I've ever made
MM
Milos Montes
3 months ago
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