Machine learning has given the computer systems the abilities to automatically learn without being explicitly programmed. But how does a machine learning system work? So, it can be described using the life cycle of machine learning. Machine learning life cycle is a cyclic process to build an efficient machine learning project. The main purpose of the life cycle is to find a solution to the problem or project.

Machine learning life cycle involves seven major steps, which are given below:

• Gathering Data

• Data preparation

• Data Wrangling

• Analyse Data

• Train the model

• Test the model

• Deployment

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The most important thing in the complete process is to understand the problem and to know the purpose of the problem. Therefore, before starting the life cycle, we need to understand the problem because the good result depends on the better understanding of the problem.

In the complete life cycle process, to solve a problem, we create a machine learning system called “model”, and this model is created by providing “training”. But to train a model, we need data, hence, life cycle starts by collecting data.

1. Gathering Data:

Data Gathering is the first step of the machine learning life cycle. The goal of this step is to identify and obtain all data-related problems.

In this step, we need to identify the different data sources, as data can be collected from various sources such as files, database, internet, or mobile devices. It is one of the most important steps of the life cycle. The quantity and quality of the collected data will determine the efficiency of the output. The more will be the data, the more accurate will be the prediction.

This step includes the below tasks:

• Identify various data sources

• Collect data

• Integrate the data obtained from different sources

By performing the above task, we get a coherent set of data, also called as a dataset. It will be used in further steps.

2. Data preparation

After collecting the data, we need to prepare it for further steps. Data preparation is a step where we put our data into a suitable place and prepare it to use in our machine learning training.

In this step, first, we put all data together, and then randomize the ordering of data.

This step can be further divided into two processes:

• Data exploration:

It is used to understand the nature of data that we have to work with. We need to understand the characteristics, format, and quality of data.

A better understanding of data leads to an effective outcome. In this, we find Correlations, general trends, and outliers.

• Data pre-processing:

Now the next step is preprocessing of data for its analysis.

3. Data Wrangling

Data wrangling is the process of cleaning and converting raw data into a useable format. It is the process of cleaning the data, selecting the variable to use, and transforming the data in a proper format to make it more suitable for analysis in the next step. It is one of the most important steps of the complete process. Cleaning of data is required to address the quality issues.

It is not necessary that data we have collected is always of our use as some of the data may not be useful. In real-world applications, collected data may have various issues, including:

• Missing Values

• Duplicate data

• Invalid data

• Noise

So, we use various filtering techniques to clean the data.

It is mandatory to detect and remove the above issues because it can negatively affect the quality of the outcome.

4. Data Analysis

Now the cleaned and prepared data is passed on to the analysis step. This step involves:

• Selection of analytical techniques

• Building models

• Review the result

The aim of this step is to build a machine learning model to analyze the data using various analytical techniques and review the outcome. It starts with the determination of the type of the problems, where we select the machine learning techniques such as Classification, Regression, Cluster analysis, Association, etc. then build the model using prepared data, and evaluate the model.

Hence, in this step, we take the data and use machine learning algorithms to build the model.

5. Train Model

Now the next step is to train the model, in this step we train our model to improve its performance for better outcome of the problem.

We use datasets to train the model using various machine learning algorithms. Training a model is required so that it can understand the various patterns, rules, and, features.

6. Test Model

Once our machine learning model has been trained on a given dataset, then we test the model. In this step, we check for the accuracy of our model by providing a test dataset to it.

Testing the model determines the percentage accuracy of the model as per the requirement of project or problem.

7. Deployment

The last step of machine learning life cycle is deployment, where we deploy the model in the real-world system.

If the above-prepared model is producing an accurate result as per our requirement with acceptable speed, then we deploy the model in the real system. But before deploying the project, we will check whether it is improving its performance using available data or not. The deployment phase is similar to making the final report for a project.

So, this brings us to the end of blog. This Tecklearn ‘Machine Learning Life cycle’ blog helps you with commonly asked questions if you are looking out for a job in Machine Learning. If you wish to learn Machine Learning and build a career in Data Science or Machine Learning domain, then check out our interactive, Machine Learning Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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About the Course

Tecklearn’s Machine Learning training will help you develop the skills and knowledge required for a career as a Machine Learning Engineer. It helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning Certification Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. With these key concepts, you will be well prepared for the role of Machine Learning (ML) engineer. In addition, it is one of the most immersive Machine Learning online courses, which includes hands-on projects

Why Should you take Machine Learning Training?

• The average machine learning salary, according to Indeed’s research, is approximately $146,085 (an astounding 344% increase since 2015). The average machine learning engineer salary far outpaced other technology jobs on the list.

• IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle & other MNCs worldwide are using Machine Learning for their Data analysis

• The Machine Learning market is expected to reach USD $8.81 Billion by 2022, at a growth rate of 44.1-percent, indicating the increased adoption of Machine Learning among companies. By 2020, the demand for Machine Learning engineers is expected to grow by 60-percent.

What you will Learn in this Course?

Introduction to Machine Learning

• Need of Machine Learning

• Types of Machine Learning – Supervised, Unsupervised and Reinforcement Learning

• Applications of Machine Learning

Concept of Supervised Learning and Linear Regression

• Concept of Supervised learning

• Types of Supervised learning: Classification and Regression

• Overview of Regression

• Types of Regression: Simple Linear Regression and Multiple Linear Regression

• Assumptions in Linear Regression and Mathematical Concepts behind Linear Regression

• Hands On

Concept of Classification and Logistic Regression

• Overview of the Concept of Classification

• Comparison of Linear regression with Logistic regression

• Mathematics behind Logistic Regression: Detailed Formulas and Functions

• Concept of Confusion matrix and Accuracy Measurement

• True positives rate, False positives rate

• Threshold evaluation with ROCR

• Hands on

Concept of Decision Trees and Random Forest

• Overview of Tree Based Classification

• Concept of Decision trees, Impurity function and Entropy

• Concept of Impurity function and Information gain for the right split of node and

• Concept of Gini index and right split of node using Gini Index

• Overfitting and Pruning Techniques

• Stages of Pruning: Pre-Pruning, Post Pruning and cost-complexity pruning

• Introduction to ensemble techniques and Concept of Bagging

• Concept of random forests

• Evaluation of Correct number of trees in a random forest

• Hands on

Naive Bayes and Support Vector Machine

• Introduction to probabilistic classifiers

• Understanding Naive Bayes Theorem and mathematics behind the Bayes theorem

• Concept of Support vector machines (SVM)

• Mathematics behind SVM and Kernel functions in SVM

• Hands on

Concept of Unsupervised Learning

• Overview of Unsupervised Learning

• Types of Unsupervised Learning: Dimensionality Reduction and Clustering

• Types of Clustering

• Concept of K-Means Clustering

• Mathematics behind K-Means Clustering

• Concept of Dimensionality Reduction using Principal Component Analysis (PCA)

• Hands on

Natural Language Processing and Text Mining Concepts

• Overview of Concept of Natural Language Processing (NLP)

• Concepts of Text mining with Importance and applications of text mining

• Working of NLP with text mining

• Reading and Writing to word files and OS modules

• Text mining using Natural Language Toolkit (NLTK) environment: Cleaning of Text, Pre-Processing of Text and Text classification

• Hands on

Introduction to Deep Learning

• Overview of Deep Learning with neural networks

• Biological neural network Versus Artificial neural network (ANN)

• Concept of Perceptron learning algorithm

• Deep Learning frameworks and Tensor Flow constants

• Hands on

Time Series Analysis

• Concept of Time series analysis, its techniques and applications

• Time series components

• Concepts of Moving average and smoothing techniques such as exponential smoothing

• Univariate time series models

• Multivariate time series analysis and the ARIMA model

• Time series in Python

• Sentiment analysis using Python (Twitter sentiment analysis Use Case) and Text analysis

• Hands on

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