Learn the concepts of machine learning and AI.

ML is Machine Learning.
What is ML?
ML or Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
Machine learning algorithms use historical data as input to predict new output values.

Machine Learning is the study of data-driven methods capable of mimicking, understanding and aiding human and biological information processing tasks. In this pursuit, many related issues arise such as how to compress data, interpret and process it. Often these methods are not necessarily directed to mimicking directly human processing but rather to enhance it, such as in predicting the stock market or retrieving information rapidly.
In this probability theory is key since inevitably our limited data and understanding of the problem forces us to address uncertainty.
In the broadest sense, Machine Learning and related fields aim to ‘learn something useful’ about the environment within which the agent operates.
Machine Learning is also closely allied with Artificial Intelligence, with Machine Learning placing more emphasis on using data to drive and adapt the model.
In the early stages of Machine Learning and related areas, similar techniques were discovered in relatively isolated research communities.
This can be unified treatment via graphical models, a marriage between graph and probability theory, facilitating the transference of Machine Learning concepts between different branches of the mathematical and computational sciences.

Data science uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data as one of the hottest professions in the market today

Common algorithms used in Machine Learning: Classification, Regression, Clustering, Dimensionality reduction, Model selection, Preprocessing

Linear Regression is a machine learning algorithm based on supervised learning.
It performs a regression task. Regression models a target prediction value based on independent variables.
It is mostly used for finding out the relationship between variables and forecasting.
Different regression models differ based on – the kind of relationship between dependent and independent variables they are considering, and the number of independent variables getting used.
Exploratory Data Analysis: Performed initial investigations on data so as to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.
Data Visualization: Using data visualization, I summarized the data with graphs, pictures and maps, so that the human mind has an easier time processing and understanding the given data.
Data visualization plays a significant role in the representation of both small and large data sets, but it is especially useful when we have large data sets, in which it is impossible to see all of our data, let alone process and understand it manually.
Training and Testing: In this project, datasets are split into two subsets.
The first subset is known as the training data - it's a portion of our actual dataset that is fed into the machine learning model to discover and learn patterns. In this way, it trains our model. The other subset is known as the testing data.
Train and Evaluate Linear Regression: Simple linear regression is an approach for predicting a quantitative response using a single feature (or "predictor" or "input variable"). It takes the following form: y=β0+β1x

Machine learning and artificial intelligence are concepts to describe similar purposes under the data sciences.
Machine learning definition a component included in the general concept of artificial intelligence.
ML uses statistical methods to train algorithms and find patterns or insights that data, and use this to inform and take decisions.
The main types of machine learning are: Supervised, Unsupervised, and Reinforcement

Supervised machine learning uses data to train algorithms and models that can be used to make predictions.
The model predicts the outputs, based on the inputs and update this mapping adjusting the weights and bias until the final data is accurate.
Some examples of use supervised machine learning are text classification, spam detection and recommendation systems based on the related behaviour of the user.

Unsupervised machine learning also uses algorithms to uncover hidden patterns in data classification and other tasks.
It process information sorting big populations and demographics into different groups.
Unsupervised machine learning can be applied to image recognition systems, marketing and customer segmentation tools.

Reinforcement machine learning is another type of behavioral algorithm that learns in real time through the trial and error process. Can find better accuracy and can be trained for specific tasks.
Reinforcement uses the previous experiences to take better decisions and inform the best recommendation or solution for some specific problem.

What is Machine Learning?
The term machine learning refers to the automated detection of meaningful patterns in data.
In the past couple of decades it has become a common tool in almost any task that requires information extraction from large data sets. We are surrounded by a machine learning based technology: search engines learn how to bring us the best results (while placing profitable ads), anti-spam software learns to filter our email messages, and credit card transactions are secured by a software that learns how to detect frauds.
Digital cameras learn to detect faces and intelligent personal assistance applications on smart-phones learn to recognize voice commands.
Cars are equipped with accident prevention systems that are built using machine learning algorithms. Machine learning is also widely used in scientific applications such as bioinformatics, medicine, and astronomy.
One common feature of all of these applications is that, in contrast to more traditional uses of computers, in these cases, due to the complexity of the patterns that need to be detected, a human programmer cannot provide an explicit, fine- detailed specification of how such tasks should be executed. Taking example from intelligent beings, many of our skills are acquired or refined through learning from our experience (rather than following explicit instructions given to us). Machine learning tools are concerned with endowing programs with the ability to “learn” and adapt

scikit-learn : Machine Learning in Python
It is a simple and efficient tool for predictive data analysis.
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license
scikit-learn : Website, How to Install, Documentation, User Guide, API, Examples, Community, Download

Classification algorithms: Identify which category an object belongs to.
Applications: Spam detection, image recognition.
Algorithms: SVM, nearest neighbors, random forest, and more...
Classifier comparison

Predicts a continuous-valued attribute associated with an object.

Applications: Drug response, Stock prices.
Algorithms: SVR, nearest neighbors, random forest, and more...
Decision Tree Regression with AdaBoost

Clustering: Automatic grouping of similar objects into sets.

Applications: Customer segmentation, Grouping experiment outcomes
Algorithms: k-Means, spectral clustering, mean-shift, and more...
A demo of K-Means clustering on the handwritten digits data

Dimensionality reduction : To reduce the number of random variables to consider.

Applications: Visualization, Increased efficiency
Algorithms: PCA, feature selection, non-negative matrix factorization, and more...
PCA example with Iris Data-set

Machine Learning model selection.
Comparing, validating and choosing parameters and models.
Applications: Improved accuracy via parameter tuning
Algorithms: grid search, cross validation, metrics, and more...
Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

Preprocessing : Feature extraction and normalization.

Applications: Transforming input data such as text for use with machine learning algorithms.
Algorithms: preprocessing, feature extraction, and more...
Demonstrating the different strategies of KBinsDiscretizer

There are many machine learning online courses that teach the basics of this area, and explains all the differences between machine learning versus artificial intelligence, and help the route the steps for how to become a machine learning engineer.
A machine learning course explore the following topics:
Data science, data mining, data analysis, statistical learning, pattern discovery, predictive analytics, creating models and real applications.
A ML course might also explore into the real-life use of these models, such as credit card fraud detection, facial recognition, handwriting recognition, spam filtering and other uses.
The best machine learning courses you can take online.
Learn about artificial intelligence and computer science for free.
You can study machine learning and other areas of artificial intelligence and computer sciences to create software and algorithms that can make predictions based on data and training.
You can take some of the free online machine learning courses available on edX.