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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

Using social media platforms for building data science profile

Rohit
Rohit Last Updated : 13 Jan, 2021
5 min read

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

Introduction

Everyone is familiar with the power that social media holds in spreading the word or engaging with colleagues on any matter. As virtual platforms become more intertwined with our daily lives, they are becoming one with our physical environments.

“Let Twitter be the club and GitHub be the gym
 LinkedIn be the office and Facebook, well nothing “

Amusing saying, yet true.

data science profile image

 

Twitter, for example, is a café: a platform where we interact with any stranger using a one-liner. We engage with people from all walks of life.

LinkedIn is the synonym for workspace – professional networking, sharing news, and connecting with coworkers.

Facebook/Instagram is your living room where you catch up with friends ( well!! privacy 😉 ).

Youtube is the biggest library of visual content.

Let’s dive into different social media platforms and their uses. This will help you understand the importance of each platform and how people have been using them to reach out to their audience.

Twitter

It is this café-like atmosphere that makes Twitter the ultimate platform for customer engagement. Twitter provides an even playing field where brands and consumers have unrestricted lines of clear, concise communication.

One can start following major twitter celebs in the field of data science to remain updated.

Start browsing through this exhaustive list to follow, which is the culmination of major active Twitter accounts in the field of A.I, data science – click here.

LinkedIn

LinkedIn is a resume that elaborates on our professional life and daily interactions. In my view, the best part of LinkedIn is the reach it provides to your post, so use it to the fullest to build the academic and professional circle.

Check this, some awesome personalities to follow:

Vin Vashishta – Founder and chief data scientist at V-Squared Data Strategy. With more than 20 years of experience.

Kunal Jain – A huge contributor to the field of data science and knowledge sharing. Founder of Analytic Vidhya, and a big advocate of learning for all.

Andriy Burkov – Global Machine Learning Team Leader at Gartner. Active content creator.

Andrew Ng –  Co-founder of Coursera. His passion for the field of deep learning, NLP, and robotics is world-known.

Github

Github, while not an active front for the conversation,  is essential to build a data science portfolio and keep projects and tasks updated.  Git is a version control system that can help us keep a track of our progress. There are several Github startup tutorials one can use to start and learn.

Refer to this article for top Github repositories to follow.

Facebook/ Instagram/ YouTube

When we talk about content consumption, these platforms are way above then any other platform, we just need to find our target audience and engage in healthy conversations.

Keep your tabs on for any good channel. Mukul has curated a list of some amazing channels that you can follow – click here.

Now as we know how each platform and some of the prominent personalities using it, let’s dive into how we can use it to power our growth.

 

How to use social platforms?

Modern social media has democratized the discussion, impacting all communities from casual to professional. One should know the potential reason to engage with social media. Draft them and let them define your steps ahead.

social platforms for data science profile

Once done, there are several ways we can use social media to benefit ourselves, such as :

  1. Gaining visibility

  2. Learning about the new trends

  3. Publishing and sharing our own knowledge

  4. Building a community

Just like influencers, leveraging the power of social media, data science practitioners should use these tools to increase engagement. They all are connected, so if we put our efforts into content creation, one will gain visibility and that will gradually help in building a community. Similarly being active in conversations will help you gain visibility that results in building our community.

How to engage and gain visibility?

Start by defining your target audience. For example, a Data Scientist would like to engage with the community interested in coding, machine learning, text analytics, and so on.   This will help you initiate your journey and learn about new advances in the field. Check new websites, comment on your views, and cultivate a habit of engaging on healthy topics.

How to build and share your content?

Once you gain a fair presence among your target audience, you will have a diverse set of minds as your audience. They will help you grow by reviewing any project you intend to work on. The next step is to develop and share content to help the community.

Based on that, some simple steps you can apply for any social platform:

data science portfolio content

Fear of Social platforms !! 

Well, not to fear, the below steps are a tried and tested way to help you grow:

  1. Start following/connecting most trending personalities, hashtags, topics, and groups.
  2. Take part in the ongoing conversations actively.
  3. Create informative posts or content.
  4. Use of hashtags, visuals ( photographs, video) to increase the engagement.
  5. Reach out for help if you feel so, no need to hold back :).

Sharing your own knowledge

An important step is to be a part of a creative community and keep sharing your own experiences and knowledge.

You can write blogs, keep posting your experiences as short posts or tweets.  If you have worked on something, you can share your project on GitHub.

There are several posts on the internet to guide you to start building your Github portfolio (try this), and writing blogs ( you can start here, comes with a chance to earn too ;-)).

Build your own community

This being the ultimate goal of any thriving developer to have an active audience where he can collaborate and share his experience and gain from others. There are several such personalities, who have used internet platforms and now have a huge community.

One such personality, that I know and have used the power of social media to grow is “Pratham Prasoon“. Go on and check his account, you will be amazed. How much he has gained in such a small span using the power of social media.

The list of examples and sources is non-ending. Keep in mind, this is an iterative process, with efforts to be put in continuously.

Some amazing names that I admire  – Josh Starmer (Statquest), Harrison Kinsley (Sentdex),

 

End Notes

In the end, it all depends on how nicely you curate your social media time to interact more rather than scrolling through random posts.

adventure

 

  1. Do set a time limit for your time on social platforms.
  2. Set weekly goals.
  3. Be optimistic.
  4. Let it roll!!

You can reach back anytime ( click on the platform names above) for platform-specific questions.

Working to build a thriving community of professionals across the domain.

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

 

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Rohit
Rohit

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