Machine learning is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms therefore learn from test data that has not been labeled, classified or categorized.
Ninety percent of all the world’s data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 exabytes a day. That number doubles every month.That means,there is always exponential need for Machine learning engineers to build, implement, and maintain machine learning systems,algorithms in technology products with focus on machine learning system reliability, performance, and scalability.
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- #10.Cluster Analysis and Unsupervised Machine Learning in Python
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- #25.Machine Learning with Python in Arabic
- Additional Resources :
Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R
- Should have prior experience of working with R and RStudio
- Should have basic knowledge of statistics
- Should have prior experience of using simple linear regression modelling
- Should have interest in building on the previous concepts to learn which regression models are applicable under different circumstances
- Should have an interest in learning the machine learning based regression models in R
Ratings : 4.1 (327 ratings)
Machine Learning and Artificial Intelligence for beginners. How to build a neural network in 77 lines of Python code.
- Basic Python knowledge
Ratings : 4.6 (66 ratings)
Data Science, intelligence artificielle, Python, Anaconda, Spyder, Big Data, Natural Language Processing, Arbre de dcision
Ratings : 4.5 (255 ratings)
A Jump start towards the most rewarding and in-demand career of Data Science and Machine Learning!
- A PC and passion to be successful!
- Some experience in programming could be helpful but not required!
Ratings : 4.6 (216 ratings)
Build a transit time predictor in python
- It will help if you are already familiar with the basics of python.
Ratings : 4.6 (71 ratings)
Build Machine Learning Model APIs
- A Python installation
- A Jupyter notebook installation
- Python coding skills including pandas and scikit-learn
- Familiarity with Machine Learning algorithms
- Familiarity with git
Ratings : 4.5 (210 ratings)
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
- Just some high school mathematics level.
Ratings : 4.5 (81,257 ratings)
Grundlagen in R und R-Studio fr Data Science! Von Daten Analysen bis zum Maschinellen Lernen!
- Access to a computer with download rights
- Mathematical knowledge
Ratings : 4.5 (306 ratings)
Aprende los algoritmos de Machine Learning con Python para convertirte en un Data Science con todo el cdigo para usar
- Knowledge of high school math or basic knowledge of statistics is needed
- It is recommended to know how to program a little to focus on learning the analysis techniques in Python although it is not absolutely necessary
Ratings : 4.5 (2,224 ratings)
Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.
- Know how to code in Python and Numpy
- Install Numpy and Scipy
Ratings : 4.6 (1,925 ratings)
Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More
- Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
- Python coding with the Numpy stack
Ratings : 4.6 (2,200 ratings)
Apprenez crer sur Python des modles de Machine Learning puissants et crateurs de valeur. Templates de codes inclus.
- Simplement les maths du niveau lycée
Ratings : 4.4 (869 ratings)
- Just some high school mathematics level.
Ratings : 4.6 (958 ratings)
Learn about cloud based machine learning algorithms and how to integrate with your applications
- All materials and software instructions are covered in housekeeping lecture
- Familiarity with a programming language
- AWS Account – if you want to try the hands-on activities. AWS charges a small amount for model creation and predictions
- Some basic knowledge of Pandas, Numpy, Matplotlib would be helpful but not absolutely needed
Ratings : 4.3 (743 ratings)
Learn Fundamentals Of Data Science & Machine Learning With Rapidminer (No Coding). Dataset & Solutions Included.
- Able to use a Windows computer and install software on it
- High school math
Ratings : 4.4 (152 ratings)
Aprenda os principais mtodos de Aprendizado de Mquina, Cincia de dados e Python neste curso COMPLETO!
- Noções básicas de programação
- Inglês básico (devido aos principais conjuntos de dados usados e métodos do Python serem em Inglês)
Ratings : 4.5 (4,051 ratings)
Maschinelles Lernen komplett: Regression, Klassifizierung, Clustering, NLP & Bonus: Deep Learning / Neuronale Netze
- You should have already programmed a bit before
- No knowledge in Python, nor in R is required
- All tools needed (R, RStudio, Anaconda, …) we install together in the course
Ratings : 4.6 (872 ratings)
New to machine learning? This is the place to start: Linear regression, Logistic regression & Cluster Analysis
- Basic coding skills in Python
Ratings : 4.6 (32 ratings)
Learn Concepts of Linear Algebra and Implement using Python 3, Hands on Numpy, Pandas for Data Science & Linear Algebra
- Basic High School Math
Ratings : 4.4 (10 ratings)
Salesforce Artificial Intelligence, Data Science & Data Discovery with Clicks Instead of Code / Salesforce Einstein AI
- You need a computer with an Internet connection
- You need a desire to learn Artificial Intelligence, Data Science and Discovery
- You need to be someone looking for an easier and faster way to learn and do AI rather than rotting away in a school for years
Ratings : 4.8 (189 ratings)
Gain a strong foundation into the world of procedural terrain generation taught by a 13-year game industry veteran.
- If you don’t own the Professional version of World Machine, you can download the Basic edition at http://www.world-machine.com. This course does not require the Pro version.
- Some files are required for some lectures of the course. They are included.
- Source project files are included.
Ratings : 4.5 (163 ratings)
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
- You’ll need a desktop computer (Windows, Mac, or Linux) capable of running Enthought Canopy 1.6.2 or newer. The course will walk you through installing the necessary free software.
- Some prior coding or scripting experience is required.
- At least high school level math skills will be required.
- This course walks through getting set up on a Microsoft Windows based desktop PC. While the code in this course will run on other operating systems, we cannot provide OS-specific support for them.
Ratings : 4.5 (14,350 ratings)
Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.
- A Windows, Mac, or Linux PC with at least 3GB of free disk space.
- Some experience with a programming or scripting language (preferably Python)
- Some computer science background, and an ability to understand new algorithms.
Ratings : 4.5 (594 ratings)
Arabic Course,Learn to create Machine Learning Algorithms in Python.
- Although the course in Arabic, but you must have the basics of English
- A good background in Python programming
- The basics of linear algebra and matrices
- The basics of statistics
- Know some basics of differentiation and integration
- Intermediate Python programming knowledge:
- Strings, numbers, and variables
- Statements, operators, and expressions
- Lists, tuples, and dictionaries
- Conditions, loops
- Procedures, objects, modules, and libraries
- Troubleshooting and debugging
- Research & documentation
- Problem solving
- Algorithms and data structures
- Intermediate statistical knowledge:
- Populations, samples
- Mean, median, mode
- Standard error
- Variation, standard deviations
- Normal distribution
- Precision and accuracy
- Hypothesis testing
- Problem solving
- Confidence Interval, P-values, T-test, Statistical Significance
- Intermediate calculus and linear algebra mastery:
- Series expansions
- Matrix operations through Eigenvectors and Eigenvalues
Ratings : 4.3 (411 ratings)
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