Chapter-1 Machine Learning Introduction

Welcome to Machine Learning Series !

Machine Learning nowadays going to be popular because of his nature to handle data and this is very powerful tool which can make human life so easy and authentic.When I had started machine learning, I was stuck on “ how to start machine learning?” then I started following some Analytics Vidhya Blog material for reading and it’s make my picture so clear. I followed Learning path of some website but without background knowledge you never start this things.So, in this chapter I start writing is background knowledge we have to needed and some basics about machine learning.

Definition of Machine Learning

Machine learning is part of AI that use statistics techniques to give a intelligence to machine to perform specific task accurately.

Difference Data Mining, Machine Learning, Deep Learning

Data Mining is the process of extracting useful information or knowledge (not so obvious patterns or insights) from huge sets of data. This knowledge could further be used for business applications such as Market analysis, Risk management, Fraud detection etc. Data mining could be accomplished by analysis, visualization or Machine Learning modelling.

Machine Learning is the process of gaining knowledge from past data, and using that knowledge to make future predictions. Example Algorithm: Support Vector Machines

Deep learning is a type of machine learning technique with more capabilities since it tries to mimic the neurons in human brain. It tries to learn a phenomenon as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts. Example Algorithm: Convolution Neural Networks

How Machine Learning Works?

Machine learning systems are made up of three major parts, which are:

• Model: the system that makes predictions or identifications.
• Parameters: the signals or factors used by the model to form its decisions.
• Learner: the system that adjusts the parameters — and in turn the model — by looking at differences in predictions versus actual outcome.

But Let me show you the Big picture of machine learning in below infographics.

Machine Learning introduction

How to Start Machine learning ?

Step 1: Adjust Mindset. Believe you can practice and apply machine learning.
Step 2: Pick a Process. Use a systemic process to work through problems.
Step 3: Pick a Tool. Select a tool for your level and map it onto your process.
Step 4: Practice on Datasets. Select datasets to work on and practice the process.
Step 5: Build a Portfolio. Gather results and demonstrate your skills.

Machine Learning Types

There are variation of types based on their nature to solve the problem.

• Supervised learning : In supervised learning one is furnished with input (x1, x2, . .,) and output (y1, y2, . .,) and are challenged with finding a function that approximates this behavior in a generalizable fashion. The output could be a class label (in classification) or a real number (in regression) — these are the “supervision” in supervised learning.
• Unsupervised Learning : In the case of unsupervised learning, in the base case, you receives inputs x1, x2, . ., but neither target outputs, nor rewards from its environment are provided. Based on the problem (classify, or predict) and your background knowledge of the space sampled, you may use various methods: density estimation (estimating some underlying PDF for prediction), k-means clustering (classifying unlabeled real valued data), k-modes clustering (classifying unlabeled categorical data), etc
• Semi-supervised Learning : Semi-supervised learning involves function estimation on labeled and unlabeled data. This approach is motivated by the fact that labeled data is often costly to generate, whereas unlabeled data is generally not. The challenge here mostly involves the technical question of how to treat data mixed in this fashion. See this Semi-Supervised Learning Literature Survey for more details on semi-supervised learning methods.
• Reinforcement Learning : Reinforcement learning algorithms try to find the best ways to earn the greatest reward. Rewards can be winning a game, earning more money or beating other opponents. They present state-of-art results on very human task, for instance, this paper from the University of Toronto shows how a computer can beat human in old-school Atari video game.

Source : Analytics vidhya

Source : kdnuggets

other resources to learn:

1. http://dataconomy.com/2015/01/whats-the-difference-between-supervised-and-unsupervised-learning/
2. http://enhancedatascience.com/2017/07/19/machine-learning-explained-supervised-learning-unsupervised-learning-and-reinforcement-learning/
3. https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
4. https://medium.com/deep-math-machine-learning-ai/different-types-of-machine-learning-and-their-types-34760b9128a2

What are the steps used in Machine Learning?

There are 5 basic steps used to perform a machine learning task:

1. Collecting data: Be it the raw data from excel, access, text files etc., this step (gathering past data) forms the foundation of the future learning. The better the variety, density and volume of relevant data, better the learning prospects for the machine becomes.
2. Preparing the data: Any analytical process thrives on the quality of the data used. One needs to spend time determining the quality of data and then taking steps for fixing issues such as missing data and treatment of outliers. Exploratory analysis is perhaps one method to study the nuances of the data in details thereby burgeoning the nutritional content of the data.
3. Training a model: This step involves choosing the appropriate algorithm and representation of data in the form of the model. The cleaned data is split into two parts — train and test (proportion depending on the prerequisites); the first part (training data) is used for developing the model. The second part (test data), is used as a reference.
4. Evaluating the model: To test the accuracy, the second part of the data (holdout / test data) is used. This step determines the precision in the choice of the algorithm based on the outcome. A better test to check accuracy of model is to see its performance on data which was not used at all during model build.
5. Improving the performance: This step might involve choosing a different model altogether or introducing more variables to augment the efficiency. That’s why significant amount of time needs to be spent in data collection and preparation.

Be it any model, these 5 steps can be used to structure the technique and when we discuss the algorithms, you shall then find how these five steps appear in every model!

Application of Machine Learning

1.Virtual Personal Assistants

Virtual Assistants are integrated to a variety of platforms. For example:

• Smart Speakers: Amazon Echo and Google Home
• Smartphones: Samsung Bixby on Samsung S8
• Mobile Apps: Google Allo

2. Predictions while Commuting

• Traffic Prediction
• Online Transportation Networks

3. Videos Surveillance

4. Social Media Services

• People You May Know
• Face Recognition
• Similar Pins

5. Email Spam and Malware Filtering

6. Online Customer Support

7. Search Engine Result Refining

8. Product Recommendations

9. Online Fraud Detection

References :

1. https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/
2. https://www.kdnuggets.com/2017/11/3-different-types-machine-learning.html
3. https://medium.com/deep-math-machine-learning-ai/different-types-of-machine-learning-and-their-types-34760b9128a2
4. https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
5. https://medium.com/app-affairs/9-applications-of-machine-learning-from-day-to-day-life-112a47a429d0