Machine Learning serves as the sub-core of Artificial Intelligence.The concept of Machine learning helps computers to use the self-learning mode without the need for specific programming.  The idea of Machine Learning evolved from pattern recognition and computational theory that is of use in Artificial Intelligence.

Machine learning deals with the development and study of algorithms that allows you to make predictions on data. Machine learning is of extensive use in computing tasks. 

Applications of Machine Learning includes web search, credit scoring, spam systems, fraud detection, stock trading and computer vision and much more. Machine learning makes an enormous contribution to the industry as it automates the process of data mining.

Data Science refers to the extraction of information and insights from data. Data science provides an ability to analyze massive data sets and helps users save much time in this process. The popularity of Data science is due to the vast amount of data generated in different fields.  Data Science consists of data preparation, data cleansing, and Data analysis. 

Machine Learning and Data Science go well hand in hand. Machine learning specifies the ability of a machine to understand the data and the ability of algorithms to consume the data. Hence, machine learning is possible only if data exists and so machine learning becomes a standard requirement for data scientists.

The machine learning field is always growing. All Data scientists require machine learning for valuable predictions of data that helps to make smart decisions and better actions in real time without the intervention of humans.

The technology in Machine learning enables data scientists to analyze data using an automated process and thereby reduces the task of the data scientists. Machine learning has changed the working way of data extraction and interpretation by using fast, and efficient algorithms and data-driven models for real-time data processing and produce correct results and analysis.

Data Science is a combination of statistics, programming, mathematics, problem-solving, capturing data in smart ways, the ability to look at things differently, and includes the act of cleansing, preparing, and aligning the data.

Machine Learning for Data Science has tremendously replaced the traditional statistical techniques with automated processing of data that helps to analyze a large amount of data in an efficient and effective method.

Mainly, all the internet search engines make use of data science algorithms to deliver best and quick results for search queries. The whole digital marketing spectrum uses the data science algorithms from display banners to digital billboards.

• Learn about Machine learning and its relation to statistics and data analysis
• Understand the algorithms and their applicability for searching patterns of data
• Learn to make better decisions and predictions using real-time data
• Use topic modeling to identify the hidden texts in large collection of data
• Gain skills in data preparation, handling missing data and creating custom data analysis solution
• Determine the applications of machine learning for data science
• Understand the supervised and unsupervised learning
• Perform logistic and linear regression

• Basic knowledge of Computer programming terminology
• Basic experience/ awareness of any programming language
• Familiarity with mathematics and statistics

• Anyone interested in the data science field and willing to become a Data Scientist
• Software Engineers ready to change their career to the field of Data  Science and Machine Learning

Machine learning serves a significant role in the present data world. Machine learning is applicable in web search results, mobile devices, email spam filtering, web pages advertisements, network intrusion detection, image recognition and pattern or handwriting recognition.

 In this lesson, you will gain knowledge on machine learning and their uses.

Class 1:

• Machine Learning Overview
• Benefits of Machine Learning
• Applications of Machine Learning

Supervised learning is the commonly used method to Machine learning. In supervised learning, the data consist of labeled inputs and known outputs, like mapping “x” inputs to “y” outputs.

In Unsupervised learning, Machine Learning provides output data without any input data. Unsupervised learning is more typical of human and animal learning. Unsupervised learning is more applicable than supervised learning as in this learning there is no requirement for a human expert to label the data manually.

Reinforcement learning is less commonly used and is useful for learning to reward data for training the machine within a particular context.

Class 1:

• Supervised learning
• UnSupervised learning
• Reinforcement learning

In Supervised learning, the output datasets are already available to train and obtain the desired outputs.

The supervised learning in machine learning, the output data sets are prepared to get the allows you to infer a function from supervised and trained data. Every example in supervised learning is a pair that contains an input object whish is a vector and the desired output value. The output value you desire is known as a supervisory signal. A supervised learning algorithm analyzes the training data and produces an inferred function, that is called a classifier. Classification is for discrete output and regression is for continuous output.

Class 1:

• Regression
• Classification

This lesson explains you the method of regression in supervised learning. In supervised learning, the task is to infer hidden structure from labeled data, that consists of values. Regression means the output that takes continuous values. Regression has less or no emphasis on using probability to explain the random variation between the predictor and the target

Class 1:

• Regression Analysis
• Linear Regression and Logistic
• Regression fundamentals   
• Feature selection  
• Training/ Test curves
• Adding other features
• Regression ML block diagram

The target variable is categorical in the case of Classification.  This chapter teaches you the method to perform classification and handle classifiers.

Class 1:

• Classification fundamentals: Data and Models
• Understanding Decision Trees and Naive Bayes
• Linear classifiers

Class 2:

• Decision boundaries
• Classifier evaluation
• Classification Machine Learning block diagram

Unsupervised learning allows the underlying structure or distribution in the data to learn more about the data. Unsupervised learning is different from supervised learning as there is no correct answer and there is no teacher. There are only predictor variables in unsupervised learning and no target variable.

Class 1:

• Clustering
• Recommendation
• Deep Learning

Unsupervised learning in Machine learning is grouped into clustering and association problems. A clustering problem allows you to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. An association rule learning problem allows you to identify rules that describe large portions of your data

Class 1:

• Clustering System Overview
• Clustering fundamentals  
• Feature selection in Model Building
• Clustering and ML block diagram

Class 1:

• Recommending Products
• Recommender systems overview
• Collaborative filtering
• Understanding Support Vector Machine
• Matrix completion task

This chapter gives you an introduction to deep learning. Deep learning is a subset of machine learning.  Deep Learning is all about learning multiple levels of representation and abstraction that help to identify data such as sound, images, and text.  

Class 1:

• Deep Learning  
• Application of deep learning   
• Deep learning performance
• Deep learning and Machine Learning

This chapter introduces you to the basic different data mining techniques.

Class 1:

• K- Nearest Neighbors concepts
• Predictions Analysis
• Principal Component Analysis

Class 2:

• Regression Analysis
• Bayesian Models