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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

Kickstart Guide to Become a M-A-D Programmer

H
Harsha Vardhan Last Updated : 22 Mar, 2021
6 min read
This article was published as a part of the Data Science Blogathon.

M-A-D Programmer skills
Artificial Intelligence – Machine Learning – Deep Learning – Data Science

Introduction

Many technologies are emerging in our day-to-day lives, some of them are used widely and have a greater impact and the rest are used for some specific applications. Especially, the recent years of this 21st century have seen a massive revolution in technology with the evolution of the latest technologies like Artificial Intelligence, Machine Learning, Data Science & Deep Learning. With the help of these technologies, the working style became easier, simple, smarter, and has benefitted many people and Industries. These technologies helped in solving many complex problems, in the areas where human beings failed to find a solution to that problem.

Applications of M-A-D Technologies in the real-world problem solving

Many industries like Healthcare, Manufacturing, Finance, Transportation, Advertising and many more use these technologies to enhance their business units and make more products to sustain in the present Market. At present, most of the industries are being automated with the evolution of these technologies and are becoming more smarter than human beings in every aspect. The Robotic industry has evolved as an application of M-A-D technologies, which resulted in doing work more accurately and efficiently than a human with better computation speed. I guess humans would be replaced by robots in many industries to get work done.

Knowing or Unknowingly, we use the applications of M-A-D in our day-to-day lives mostly in the form of Recommendations. At present and soon there is a vast scope for creating a large workspace for these M-A-D technologies and for their applications. There is no doubt that these M-A-D technologies would “Conquer” the world and would be a “Monarch” with its latest applications in any Industry. An M-A-D programmer would develop new strategies and algorithms that enhance the growth of the organization and can solve the real-world challenges faced by the people.

Wanted to be an M-A-D programmer….?? Do not know how to get started…?? Need a clear picture of M-A-D…?? Then Let’s get started. In this blog you will know the current applications, tools, programming languages, scope of research, opportunities, available workspaces, and the need to use, and the impact of M-A-D technologies in our day-to-day lives.

 

Prerequisites to M-A-D

Before getting started to be an M-A-D programmer, many people have some doubts in their mind like Do we need great coding skills…?? Do we need to have good command over data structures and Dynamic Programming….?? What programming languages are required to get started with M-A-D….?? What are the key skills to focus on to build M-A-D applications….??

Pillars to become an M-A-D Programmer

The foundation of these M-A-D technologies is the “MATHEMATICS”, and one needs to get started with it, to understand the problems to find out better solutions. There are mainly 4 pillars in mathematics namely “STATISTICS, CALCULUS, LINEAR ALGEBRA, and PROBABILITY”. Many algorithms and strategies in M-A-D are implemented using these pillars, that aim to produce accurate results at the end.

Most commonly used Languages in M-A-D

At present, the most used programming languages for M-A-D applications are “PYTHON & R – language”. The reason behind using these languages especially is because it has different libraries and functions that help in developing different types of applications in M-A-D.

In the aspect of M-A-D programming, it is not that mandatory for a person to have great coding skills, but he should be capable of analyzing the given problem and find out the better logic to the problem. M-A-D is not limited to only the Coding part, apart from coding it requires analytical skills, problem understanding skills, storytelling skills, presentation skills, Visualization skills, and many more.

 

General Scenario in M-A-D

An algorithm is called a “Model” in M-A-D. The common procedure most of the programmers follow while finding a solution to a problem using these M-A-D technologies is as follows:

a) Defining the problem.

b) Collect the data.

c) Understand the data.

d) Brainstorming on the data, to know how it suits the solution development.

e) Implementing Exploratory Data Analysis (EDA) technique to make the data fit the model development.

f) Split the data into 2 parts (Training & Testing).

g) Develop the Model.

h) Evaluate and Test the model with external data.

i) Obtain accurate results at the end.

j) Storytelling about data and model.

It takes less time to code for the given problem, most of the time is spent on understanding the given data, drawing insights from the data, transforming the data into a suitable format, removing null values and noise from the data, resolving the issues of outliers, etc., The solution to the problem lies in how better you understand about the given data, and the different techniques you use.

 

Websites & YouTube Channels for Learning M-A-D

These are the top websites, I have used in my journey of M-A-D, these sites have good content and plentiful information to learn and build the knowledge related to M-A-D. These are opensource and helps to lay a good foundation for our career in the latest M-A-D technologies.

Websites:

a) Analyticsvidhya

b) Machinelearningmastery

c) Analytics India Magazine

d) KDNuggets

e) Fast.ai

f) Data flair

g) Medium

h) Google’s Machine Learning

i) Towards Data Science

j) Kaggle

k) Reddit

 

YouTube Channels:

a) AndrewNg

b) CS Dojo

c) Krish Naik

d) Siraj Raval

e) Ken Jee

f) DeepLearning AI

g) Data Science Dojo

h) Data School

i) Code with Harry

Apart from these, I advise you to take courses on the leading platforms like Coursera, Udemy, Edx, etc., those certifications would add value to your skills in a particular stream. And you should try to do different projects on these technologies. Apart from the practical knowledge, you should have good knowledge in theory as well, which in turn helps a person to become a full-pledged M-A-D program and you can solve many problems easily.

 

Where do I run the M-A-D Scripts / Codes………….??

There are many tools to run the M-A-D scripts/codes, like

a) Python IDLE (Basic Start)

b) Jupyter Notebooks (Preferred)

c) Kaggle (OpenSource & Online) – Has GPU support

d) Google Colab (Opensource & Online) – Has GPU support

e) Azure Notebooks

f) Atom and many more…………!!

The above tools provide us a better user interface and programmers can connect with those environments easily and can start their work. GPU has better Computation Speed than normal CPU, we can execute our scripts very fast and get the output in a short period of time. When we run our scripts, we need to install the required libraries in our program.

Installing Libraries – “pip install library-name”.

 

Jobs For an M-A-D programmer

Once we are habituated to these M-A-D technologies, there is a wide variety of scope for a programmer to survive in the industry in the future. Some of the available job positions for an M-A-D programmer are as follows: –

A) AI Engineer

B) Machine Learning Engineer

C) Data Scientist

D) Data Analyst

E) Data Engineer

F) ML Architect

G) ML Specialist

H) Researcher

I) Applied ML Scientist and many more……………….!!

Major Companies that are hiring M-A-D programmers in the real world. some of the top companies are as follows:

Google, Microsoft, Apple, Amazon, Facebook, Cisco, etc., 

We can find these jobs in many famous Job searching sites like LinkedIn, Glassdoor, Indeed Jobs, Monster, etc., and are paid higher salaries compared to the Normal Software Engineer working in a company. These latest technologies help us to get jobs at Dream Companies like Facebook, Amazon, Google, Microsoft, etc., with a unique skillset and great programming skills in M-A-D.

 

Author Section

Author: – Harsha Vardhan Garlapati

I am Data Science Enthusiast and loves working with data. I am a final-year undergraduate student and passionate about Data Science. I am a smart worker, a passionate learner, an Icebreaker, and loves to participate in Hackathons to work on real-time projects. I am a Toastmaster Member at S.R.K.R Toastmasters Club, a Public Speaker, a good Innovator, and a problem solver.

LinkedIn: – https://www.linkedin.com/in/ghvardhan569/

GitHub: – https://github.com/ghvardhan569

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

blogathonM-A-D Programmer
H
Harsha Vardhan

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Responses From Readers

Clear

Ss
Ss

The point you want to convey through blog is good👏....but there are some grammatical mistakes, that you have to introspect, before publishing blog.

123

Shashank Dubey
Shashank Dubey

Hi Harsha, I wish I'd have found this blog sooner but its posted only 2 days back LOL. You have cleared many doubts with this blog and made new and better ones for the good. Been working at private for 2 years and never took any step in this direction, but will gladly do so because of you. Thanks a lot.

123

Anurag Singh
Anurag Singh

Nice descriptive article

123

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