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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

Gartner’s 2020 Magic Quadrant for Data Science and Machine Learning Tools – check out the new Leaders!

Pranav Dar
Pranav Dar Last Updated : 19 Feb, 2020
3 min read

Which Data Science or Machine Learning Tool is the Best?

We are living in the age of choices. The data revolution has transformed the way businesses run and customers behave. And we are most definitely spoilt for choice when it comes to data science and machine learning tools.

Where only SAS existed as the go-to tool for analytics once (it’s still right up there as Gartner’s Magic Quadrant will show), we now have so many other tools to works with now. One of the most thrilling releases in recent years is the automated machine learning slew of tools, such as Google Cloud Platform, Microsoft Azure ML, etc.

These machine learning tools offer off-the-shelf solutions that a lot of data science projects can plug and play (assuming they have the right infrastructure in place, of course).

Which leads me to the million-dollar question – which data science or machine learning tool should you choose? Well, who else can best answer that question than Gartner?

Gartner releases it’s annual Magic Quadrant for ‘Data Science and Machine Learning Tools’ every February. Top tech behemoths and organizations across domains and industries look to this Magic Quadrant to understand which machine learning tool they should integrate into their processes.

“Gartner defines a DSML platform as a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner and open source). Its primary users are data science professionals. These include expert data scientists, citizen data scientists, data engineers and machine learning (ML) engineers/specialists.”

Note: Looking for Gartner’s Magic Quadrant 2020 for Analytics and BI Tools? We have you covered!

 

So, here is Gartner’s Magic Quadrant 2020 for Data Science and Machine Learning Platforms:

gartner_magic_quadrant_machine_learning

And this is how the Magic Quadrant for Data Science and Machine Learning tools panned out in 2019:

gartner_magic_quadrant_machine_learning_2

You can view the full report on all the tools on Gartner’s official site. It’s an exhaustive report that details the strengths and weaknesses of every platform mentioned above.

 

Analytics Vidhya’s Take on Gartner’s Magic Quadrant 2020 for Data Science and Machine Learning Tools

  • We’ve seen a heavy movement towards the ‘Visionaries’ and ‘Leaders’ segments this year
  • SAS not only remains in the ‘Leaders’ section, but has improved its score on both the axes. This is very interesting – for all the discussions that SAS is dead or not innovating enough we hear in the community.
  • RapidMiner moved from ‘Leaders’ quadrant to ‘Visionaries’ – now positioned close to DataRobot and KNIME
  • Alteryx and Dataiku have moved from ‘Challengers’ to ‘Leaders’ – a huge leap for both companies
  • IBM, in a bit of a surprise move, has gone from a ‘Visionary’ to a ‘Challenger’
  • For all the Python fans, Anaconda remains in the ‘Niche Players’ segment
  • Altair is a newcomer this year and has settled into the ‘Niche Players’ section. I’ll be keeping an eye on this for next year’s Magic Quadrant. I wouldn’t be surprised to see a leap for Altair
  • Google, Microsoft, H20.ai and DataRobot retain their position in the ‘Visionaries’ segment of the Magic Quadrant
  • MathWorks and Databricks have made the leap from ‘Visionaries’ to ‘Leaders’
  • SAP is not included in any of the quadrants!

 

Have your say on the Magic Quadrant!

What jumped out at you in this year’s Magic Quadrant? I am certainly keeping an eye on Altair and H20.ai this year. I fully expect them to make a significant leap in the coming months.

If you’re new to the world of machine learning, I highly recommend checking out the popular Applied Machine Learning course!

Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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Responses From Readers

Clear

John
John

What jumps out at me is... where is Amazon? This is duch a glaring ommission that it causes me to question if Gartner should be taken seriously.

123

Pranav Dar
Pranav Dar

Hi John, That's a good point. I'll try to dig out why that's the case. AWS is a big omission for sure.

123

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