hi programmers, i need your help about the subject of classification binary data base by deeplearning ,svm and RandomForest methodes please if any one had experience in this domain contact me
hi programmers, i need your help about the subject of classification binary data base by deeplearning ,svm and RandomForest methodes please if any one had experience in this domain contact me
Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
Downloadable PDF of Best AI Cheat Sheets in Super High DefinitionLet’s begin.
Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Data Science in HD
Neural Networks Cheat Sheets
Neural Networks Basics Cheat Sheet
An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.
Neural Networks Graphs Cheat Sheet
Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
Machine Learning Cheat Sheets
Machine Learning with Emojis Cheat Sheet
Scikit Learn Cheat Sheet
Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines is a simple and efficient tools for data mining and data analysis. It’s built on NumPy, SciPy, and matplotlib an open source, commercially usable — BSD license
Scikit-learn Algorithm Cheat Sheet
This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.
If you like these cheat sheets, you can let me know here.### Machine Learning: Scikit-Learn Algorythm for Azure Machine Learning Studios
Scikit-Learn Algorithm for Azure Machine Learning Studios Cheat Sheet
Data Science with Python Cheat Sheets
TensorFlow Cheat Sheet
TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.
If you like these cheat sheets, you can let me know here.### Data Science: Python Basics Cheat Sheet
Python Basics Cheat Sheet
Python is one of the most popular data science tool due to its low and gradual learning curve and the fact that it is a fully fledged programming language.
PySpark RDD Basics Cheat Sheet
“At a high level, every Spark application consists of a driver program that runs the user’s main
function and executes various parallel operations on a cluster. The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to persist an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.” via Spark.Aparche.Org
NumPy Basics Cheat Sheet
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
***If you like these cheat sheets, you can let me know ***here.
Bokeh Cheat Sheet
“Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.” from Bokeh.Pydata.com
Karas Cheat Sheet
Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.
Padas Basics Cheat Sheet
Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.
If you like these cheat sheets, you can let me know here.### Pandas Cheat Sheet: Data Wrangling in Python
Pandas Cheat Sheet: Data Wrangling in Python
The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.
Data Wrangling with Pandas Cheat Sheet
Data Wrangling with ddyr and tidyr Cheat Sheet
If you like these cheat sheets, you can let me know here.### Data Science: Scipy Linear Algebra
Scipy Linear Algebra Cheat Sheet
SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.[3]
Matplotlib Cheat Sheet
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented APIfor embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of matplotlib.
Pyplot is a matplotlib module which provides a MATLAB-like interface matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.
Data Visualization with ggplot2 Cheat Sheet
Big-O Cheat Sheet
Special thanks to DataCamp, Asimov Institute, RStudios and the open source community for their content contributions. You can see originals here:
Big-O Algorithm Cheat Sheet: http://bigocheatsheet.com/
Bokeh Cheat Sheet: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Bokeh_Cheat_Sheet.pdf
Data Science Cheat Sheet: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics
Data Wrangling Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf
Data Wrangling: https://en.wikipedia.org/wiki/Data_wrangling
Ggplot Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
Keras Cheat Sheet: https://www.datacamp.com/community/blog/keras-cheat-sheet#gs.DRKeNMs
Keras: https://en.wikipedia.org/wiki/Keras
Machine Learning Cheat Sheet: https://ai.icymi.email/new-machinelearning-cheat-sheet-by-emily-barry-abdsc/
Machine Learning Cheat Sheet: https://docs.microsoft.com/en-in/azure/machine-learning/machine-learning-algorithm-cheat-sheet
ML Cheat Sheet:: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html
Matplotlib Cheat Sheet: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY
Matpotlib: https://en.wikipedia.org/wiki/Matplotlib
Neural Networks Cheat Sheet: http://www.asimovinstitute.org/neural-network-zoo/
Neural Networks Graph Cheat Sheet: http://www.asimovinstitute.org/blog/
Neural Networks: https://www.quora.com/Where-can-find-a-cheat-sheet-for-neural-network
Numpy Cheat Sheet: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE
NumPy: https://en.wikipedia.org/wiki/NumPy
Pandas Cheat Sheet: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM
Pandas: https://en.wikipedia.org/wiki/Pandas_(software)
Pandas Cheat Sheet: https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.HPFoRIc
Pyspark Cheat Sheet: https://www.datacamp.com/community/blog/pyspark-cheat-sheet-python#gs.L=J1zxQ
Scikit Cheat Sheet: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet
Scikit-learn: https://en.wikipedia.org/wiki/Scikit-learn
Scikit-learn Cheat Sheet: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html
Scipy Cheat Sheet: https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.JDSg3OI
SciPy: https://en.wikipedia.org/wiki/SciPy
TesorFlow Cheat Sheet: https://www.altoros.com/tensorflow-cheat-sheet.html
Over the past few years, deep learning has given rise to a massive collection of ideas and techniques which are disruptive to conventional machine learning practices. However, are those ideas totally different from the traditional methods? Where are the connections and differences? What are the advantages and disadvantages? How practical are the deep learning methods for business applications? Chao will share her thoughts on those questions based on her readings and hands on experiments in the areas of text analytics (question answering system, sentiment analysis) and healthcare image classification.
Over the past few years, deep learning has given rise to a massive collection of ideas and techniques which are disruptive to conventional machine learning practices. However, are those ideas totally different from the traditional methods? Where are the connections and differences? What are the advantages and disadvantages? How practical are the deep learning methods for business applications? Chao will share her thoughts on those questions based on her readings and hands on experiments in the areas of text analytics (question answering system, sentiment analysis) and healthcare image classification.
Thanks for reading ❤
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This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.
Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial
It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.
Below topics are explained in this Machine Learning course for beginners:
Basics of Machine Learning - 01:46
Why Machine Learning - 09:18
What is Machine Learning - 13:25
Types of Machine Learning - 18:32
Supervised Learning - 18:44
Reinforcement Learning - 21:06
Supervised VS Unsupervised - 22:26
Linear Regression - 23:38
Introduction to Machine Learning - 25:08
Application of Linear Regression - 26:40
Understanding Linear Regression - 27:19
Regression Equation - 28:00
Multiple Linear Regression - 35:57
Logistic Regression - 55:45
What is Logistic Regression - 56:04
What is Linear Regression - 59:35
Comparing Linear & Logistic Regression - 01:05:28
What is K-Means Clustering - 01:26:20
How does K-Means Clustering work - 01:38:00
What is Decision Tree - 02:15:15
How does Decision Tree work - 02:25:15
Random Forest Tutorial - 02:39:56
Why Random Forest - 02:41:52
What is Random Forest - 02:43:21
How does Decision Tree work- 02:52:02
K-Nearest Neighbors Algorithm Tutorial - 03:22:02
Why KNN - 03:24:11
What is KNN - 03:24:24
How do we choose 'K' - 03:25:38
When do we use KNN - 03:27:37
Applications of Support Vector Machine - 03:48:31
Why Support Vector Machine - 03:48:55
What Support Vector Machine - 03:50:34
Advantages of Support Vector Machine - 03:54:54
What is Naive Bayes - 04:13:06
Where is Naive Bayes used - 04:17:45
Top 10 Application of Machine Learning - 04:54:48
How to become a Machine Learning Engineer - 04:59:46
Machine Learning Interview Questions - 05:09:03