As practitioner of SNA, I am trying to bring many relevant topics under one umbrella in following topics so that it can be uses in advance machine learning areas.
1. The content (80% hands on and 20% theory) will prepare you to work independently on SNA projects
2. Learn - Basic, Intermediate and Advance concepts
3. Graph’s foundations (20 techniques)
4. Graph’s use cases (6 use cases)
5. Link Analysis (how Google search the best link/page for you)
6. Page Ranks
7. Hyperlink-Induced Topic Search (HITS; also known as hubs and authorities)
8. Node embedding
9. Recommendations using SNA (theory)
10. Management and monitoring of complex networks (theory)
11. How to use SNA for Data Analytics (theory)

Python, Java, PyCharm, Android Studio and MNIST. Learn to code and build apps! Use machine learning models in hands-on projects.
A wildly successful Kickstarter funded this course
Explore machine learning concepts. Learn how to use TensorFlow 1.4.1 to build, train, and test
machine learning models.
We explore Python 3.6.2 and Java 8 languages, and how to use PyCharm 2017.2.3 and Android Studio 3 to build apps.
A machine learning framework for everyone
If you want to build
sophisticated and intelligent mobile apps
or simply want to know more about how machine learning works in a mobile environment, this course is for you.
Be one of the first
There are next to
no
courses on big platforms that focus on
mobile
machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment.
We provide
clear, concise explanations
at each step along the way so that viewers can not only replicate, but also
understand and
expand
upon what I teach. Other courses don’t do a great job of explaining exactly what is going on at each step in the process and why we choose to build models the way we do.
No prior knowledge is required
We will teach you all you need to know about the languages, software and technologies we use. If you have lots of experience building machine learning apps, you may find this course a little slow because it’s designed for beginners.
Jump into a field that has more
demand
than supply
Machine learning changes everything. It’s bringing us self-driving cars, facial recognition and artificial intelligence. And the best part is: anyone can create such innovations.
"This course is GREAT! This is what I want!" --
Rated 5 Stars by Mammoth Interactive Students
Enroll Now While On Sale

In this course, you are going to learn all types of
Supervised Machine Learning Models
implemented in
Python
. The
Math
behind every model is very important. Without it, you can never become a Good Data Scientist. That is the reason, I have covered the Math behind every model in the intuition part of each Model.
Implementation in Python is done in such a way so that not only you learn how to implement a specific Model in Python but you learn how to build real times templates and find the accuracy rate of Models so that you can easily test different models on a specific problem, find the accuracy rates and then choose the one which give you the highest accuracy rate.
I am looking forward to see you in the course..
Best

This course has been prepared for professionals aspiring to learn the basics of R and Python and develop applications involving machine learning techniques such as recommendation, classification, regression and clustering.
Through this course, you will learn to solve data-driven problems and implement your solutions using the powerful yet simple programming language like R and Python and its packages.
After completing this course, you will gain a broad picture of the machine learning environment and the best practices for machine learning techniques.

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.Machine learning has proven to be a fruitful area of research, spawning a number of different problems and algorithms for their solution. This algorithm vary in their goals,in the available training data, and in the learning strategies. this course will help to gain advance technique in machine learning.

If you're excited to explore data science & machine learning but anxious about learning complex programming languages or intimidated by terms like
"naive bayes"
,
"logistic regression"
,
"KNN"
and
"decision trees"
,
you're in the right place
.
This course is
PART 1
of a
4-PART SERIES
designed to help you build a strong, foundational understanding of machine learning:
PART 1: QA & Data Profiling
PART 2: Classification
PART 3: Regression & Forecasting
PART 4: Unsupervised Learning
This course makes data science approachable to everyday people, and is designed to
demystify powerful machine learning tools & techniques
without trying to teach you a coding language at the same time.
Instead, we'll use familiar, user-friendly tools like
Microsoft Excel
to break down complex topics and help you understand exactly HOW and WHY machine learning works before you dive into programming languages like Python or R. Unlike most data science and machine learning courses,
you won't write a SINGLE LINE of code
.
COURSE OUTLINE:
In this Part 1 course, we’ll introduce the machine learning landscape and workflow, and review critical QA tips for cleaning and preparing raw data for analysis, including variable types, empty values, range & count calculations, table structures, and more.
We’ll cover
univariate analysis
with frequency tables, histograms, kernel densities, and profiling metrics, then dive into
multivariate profiling tools
like heat maps, violin & box plots, scatter plots, and correlation:
Section 1: Machine Learning Intro & Landscape
Machine learning process, definition, and landscape
Section 2: Preliminary Data QA
Variable types, empty values, range & count calculations, left/right censoring, etc.
Section 3: Univariate Profiling
Histograms, frequency tables, mean, median, mode, variance, skewness, etc.
Section 4: Multivariate Profiling
Violin & box plots, kernel densities, heat maps, correlation, etc.
Throughout the course we’ll introduce
real-world scenarios
designed to help solidify key concepts and tie them back to actual business intelligence case studies. You’ll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and much more.
If you’re ready to build the foundation for a successful career in data science,
this is the course for you
.
__________
Join today and get
immediate, lifetime access
to the following:
High-quality, on-demand video
Machine Learning: Data Profiling ebook
Downloadable Excel project file
Expert Q&A forum
30-day money-back guarantee
Happy learning!
-Josh
M.
(Lead Machine Learning Instructor,
Maven Analytics
)
__________
Looking for our full business intelligence stack?
Search for
"
Maven Analytics
"
to browse our full course library, including
Excel, Power BI, MySQL
, and
Tableau
courses!
See why our courses are among the TOP-RATED on Udemy:
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Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!
This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With
over 100 HD video lectures
and
detailed code notebooks for every lecture
this is one of the most comprehensive course for data science and machine learning on Udemy!
We'll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning:
Programming with R
Advanced R Features
Using R Data Frames to solve complex tasks
Use R to handle Excel Files
Web scraping with R
Connect R to SQL
Use ggplot2 for data visualizations
Use plotly for interactive visualizations
Machine Learning with R, including:
Linear Regression
K Nearest Neighbors
K Means Clustering
Decision Trees
Random Forests
Data Mining Twitter
Neural Nets and Deep Learning
Support Vectore Machines
and much, much more!
Enroll in the course and become a data scientist today!

New!
Updated for 2021
with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's)
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!
If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over
100 lectures
spanning
15 hours of video
, and most topics include
hands-on Python code examples
you can use for reference and for practice. I’ll draw on
my 9 years of experience at Amazon and IMDb
to guide you through what matters, and what doesn’t.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a
final project
to apply what you've learned!
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 machine learning, AI, and data mining techniques real employers are looking for, including:
Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras
Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's)
Data Visualization in Python with MatPlotLib and Seaborn
Transfer Learning
Sentiment analysis
Image recognition and classification
Regression analysis
K-Means Clustering
Principal Component Analysis
Train/Test and cross validation
Bayesian Methods
Decision Trees and Random Forests
Multiple Regression
Multi-Level Models
Support Vector Machines
Reinforcement Learning
Collaborative Filtering
K-Nearest Neighbor
Bias/Variance Tradeoff
Ensemble Learning
Term Frequency / Inverse Document Frequency
Experimental Design and A/B Tests
Feature Engineering
Hyperparameter Tuning
...and much more! There's also an entire section
on machine learning with Apache Spark
, which lets you scale up these techniques to "big data" analyzed on a computing cluster.
If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs.
If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for?
Enroll now!
"I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD

This course aims at making you comfortable with the most important optimization technique - Linear Programming. It starts with the concept of linear, takes you through linear program formulation, brings you at ease with graphical method for optimization and sensitivity, dives into simplex method to get to the nuances of optimization, prepares you to take advantage of duality and also discusses various special situations that can help you in becoming smart user of this technique.

This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies using Scikit Learn library. The course provides path to start career in Data Science , Artificial Intelligence, Machine Learning. Machine Learning Types such as Supervise Learning, Unsupervised Learning, Reinforcement Learning are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.