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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

A Quick Guide to Data science and Machine Learning

Mohammed Nabeel
Mohammed Nabeel Last Updated : 28 Dec, 2020
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

In today’s time, an enormous amount of data is created in one click. This data is valuable for any organization and company. In this digital era, we are always connected to the internet. And this leads to a massive amount of data generation. This data brings success to companies for their business problems and day to day solutions.

Do you know that data is the ultimate goal for every organization, and hence actually I believe that it is the ruler? Without data, nothing can be achieved. From a business perspective to solving problems for end-to-end applications we require data.

This data needs to be in order to derive some purpose from it. Because forms of data can be texts, images, videos, infographics, gifs, etc. Some data are structured while most of them are unstructured. Collection, analysis, and prediction are the necessary steps that are to take into consideration with this data.

machine learning data science featured image

Image Source

Now, what exactly are Data Science and Machine learning?

I’ll just define it for you in a simple way. All the context related to this can be similar if you search somewhere else. So data science is the science of deriving insights from data for the purpose of gaining the most important and relevant source of information. And with a reliable source of information making predictions by the use of machine learning. So I guess you might have very well understood this definition. Now my point here is that with data science you can bring meaningful insights.

Why there is a need for data science and machine learning?

Data has been there for a very long time. During earlier times analysis of data was done by statisticians and analysts. Analysis of data was done primarily to get the summary and what were the causes. Mathematics was also the core subject of interest when used for this work.

It was not a cumbersome process because there was a limited amount of data. Business problems were primarily solved also by the use of software tools like Microsoft excel. This tool is also used for the analysis of data. Here when I say business problems those are specifically in digital format. As companies started becoming digital, the internet and cloud computing became the backbone of their establishment. There was a huge amount of data generation in millions of bytes Which is usually referred to as big data. With the advent of social media, powerful search engines like Google and YouTube, it became mandatory for these companies to handle their data carefully.

How data science and machine learning solutions?

Data science uses statistical methods, maths, and programming techniques to solve these problems. The programming techniques are extensively used for analysis, visualizing, and making predictions. So you see it does all the work of a statistician, programmer, and maths. The study of all these major areas makes the best way of dealing with such big data. Machine learning is integrated by making models from various algorithms. 

AI ML machine learning data scienceImage Source

This is done for model building in data science which helps for future predictions. These predictions depend upon the new data which is given to the model without explicitly telling it what to do. The model understands it and then gives us the output or solution. For example, banks use machine learning algorithms to detect if there is a fraud transaction or not. Or if this customer will default in paying his credit card dues.

Cancer detection in the health care industry uses data science and machine learning to detect if patients are prone to cancer or not. So there are a lot of examples around us where companies are widely using this. Online food delivery companies like zomato or swiggy use for recommending us food to order based on what have we ordered in the past. This type of machine learning algorithm is a recommendation system. They are also used by YouTube, Spotify, Amazon, etc.

The Data science life cycle.

There are various steps involved in solving business problems with data science.

1. Data acquisition – this process involves the collection of data. Depends on are objectives or what is the problem that needs to be solved. By this means, we tend to gather the required data.

2. Data pre-processing – this stage involves processing data in a structured format for ease of use. Unstructured data cannot be used for any analysis because it will give wrong business solutions and can have a bad impact on consumers.

3.Exploratory data analysis (EDA) – it is one of the most important stages where all the summarizations of data by statistics and math’s. Identifying the target(output) variable and predictor(independent) variables. Visualization of data and then sorting all the necessary data that will be used for predictions. Programming plays a vital role in this. A data scientist spends almost 75% of their time on this to understand their data very well. Further in this stage data is divided into training and test data.

4. Model building – After EDA we select the most appropriate methods to build our model. This is done with the use of machine learning algorithms. Selection of algorithms like regression, classification, or clustering. As machine learning algorithms are of 3 types. Supervised learning, unsupervised learning, and reinforcement learning. There are different sets of algorithms for all these types. Selecting them depends mainly on what is a problem are we trying to solve.

5. Evaluation of model – model evaluation is done to see how efficient our model is doing on the test data. Minimizing errors and also tuning of the model.

6. Deployment of model – model deployment is done as now it is fit to cater to all the future data for making predictions.

Note: There are re-evaluation techniques involved even after deployment to keep our model up-to-date.

How all this is done?

Data science tools and frameworks are specifically used for this process. Some popular tools like jupyter, tableau, tensor flow. Programming languages such as Python and R are important to do these tasks. To know and learn any one language is sufficient. Python and R are widely used for data science because there are additional libraries that make it easy for any data science project. I prefer Python as it is open-source, easy to learn, and has huge community support across the world. Statistics, math, and linear algebra are some core subjects you need to understand before getting involved in any data science or machine learning project.

 

Conclusion: Data science and machine learning are ruling the digital world because artificial intelligence is the next big thing. There has been advancement in this field as well. Deep learning also a part of artificial intelligence and a subset of machine learning is becoming more popular. Deep learning makes use of neural networks similar to the functioning of neurons in our brain. It has a more deep, layered approach to solving business problems. For example like Tesla’s self-driving cars use extensively deep learning and machine learning as well.

In the future, these sources of data will keep on expanding and there will be a need to harvest all of these. An important part or information to get from this data will only derive the need for data scientists and machine learning engineers.

Mohammed Nabeel Qureshi

Mohammed Nabeel
Mohammed Nabeel

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Responses From Readers

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Ganiu Azeez Adeyinka
Ganiu Azeez Adeyinka

Good afternoon from here in Nigeria. My name is Ganiu Azeez Adeyinka. I'm highly interested in acquiring a certificate in data science and machine learning if its going to be affordable. I'm working in a small company where I received just a little amount of money as salary. But I believe if I can try and get certification in data science and machine learning, it will boost my career. I need your response urgently. Thanks

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Mahendran P
Mahendran P

Good blog for Data Science and Machine learning. I like this Page.

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Abdul Alim Baig
Abdul Alim Baig

A very comprehensive article which covers a lot in a simple way! Way to go Nabeel bhai :)

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