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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

Analytics events in 2014 – India and abroad

Kunal Jain
Kunal Jain Last Updated : 17 Apr, 2015
4 min read

One of the queries I frequently get on my blog is:

Which events / conferences are happening in India and are they worth attending?

So, I thought I will collate the resources about these articles and give my opinion about them.

I have collated the events happening in India and pointed to places which already have a collated list of events outside India (with my recommendations).

india_summits

Disclaimer: Before we go any further, I have to admit, I am not a big fan of events and conferences! I go to these events only if I am convinced that they are sure to add value. Also, I do not have any first hand experience of events outside India. I am giving the recommendation basis what I have read on the Internet and assimilated during my regular reading.

[stextbox id = “section”]Analytics events in India:[/stextbox]

1. Gartner Business Intelligence & Information Management Summit, June 9 – 10, Mumbai – A must attend event for all BI leaders. Expect who’s who of the BI industry in this event. The sessions are divided into 3 tracks and workshops. The topics vary from the assessment of your BI strategy to the integration of Hadoop in your BI landscape. Also, there will be opportunities to ask questions specific to your problems, which is a big plus.

You can see the latest agenda here. I would recommend this every to BI leader who has the resources to attend this (it is a bit expensive, but worth every rupee).

gartner_summit

2. NASSCOM Big Data and Analytics Summit, June 27, Hyderabad – The agenda looks to have a mix of entire analytics spectrum. Probably aimed to have mid-management / Senior management in the participants. The speakers also come from diverse background. If you want to attend this event, the early bird discount ends today (5th May 2014). Register and save on cost, if you are keen to attend this.

nasscom_conference

3. PyCon 2014, 26 – 28 September, Bangalore – I had mentioned this event while recommending tutorials from PyCon 2014, USA. The event would not be as big as the one in the US, but would still be a good opportunity to meet the Python community. Also, session details are not available currently, so it is difficult to comment how many sessions would be there on data analysis.

Pycon_india_2014

4. The fifth elephant, 23 – 26 July, Bangalore – OK, this is not an analytics event strictly! But, this would probably leave you with more knowledge / leads about Big data and Big data analytics, compared to any other events mentioned above. A word of caution – Go to this event, only if you want to get into details of big data and can cope up with IT jargon and technical details.

fifth_elephant

[stextbox id = “section”]Analytics events outside India:[/stextbox]

As mentioned, there are a few websites listing these events already. I usually refer KDNuggets list of meetings. The list differentiates business oriented and research oriented events and lists most of the events across the world.

Which are the events I would recommend? Here is the list:

1. Chief Data Officer Summit, May 22 – 23, San Francisco – One of the various events from Innovation enterprise.

2. Dataedge, May 8 – 9, UC Berkeley School of Information – for meeting the mathematicians, computer scientists and data scientists

3. Predictive Analytics World – Various chapters across the globe. PAW, founded by Eric Siegel, hosts one of the most recognized event in the industry. One not to miss

4. Gigaom Structure 2014, June 18 – 19, San Francisco – For looking at what Thought leaders across the globe are doing.

5  2014 INFORMS Conference on the business of big data, June 22 – 24, San Jose – Another event hosted by veterans

6. Discovery Summit, JMP, Sept 15 – 19, North Carolina – How could a SAS summit not be in this list?

Each of these conferences has an impressive list of speakers and should leave you with a lot of information, if you happen to attend them.

Which events are you attending this year? For what reasons? Do add your recommendations through the comments below.

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Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Mainak
Mainak

Sir which are the companies who are ready to give internship even for the free cost also

123

Kunal Jain
Kunal Jain

Mainak, The internship season is almost coming to an end. Normally, companies provide internship for 2 - 3 months during summers. You can still try analytics consultancies like Fractal, Mu-Sgma, WNS etc. and take your chances. Regards, Kunal

123
NK
NK

Dear Kunal, What are the opportunities Academicians from teaching, training & Research in Business Analytics jobs. (experience of 10-12 years post qualification) Thanks NK

123

Kunal Jain
Kunal Jain

NK, If you have been teaching Business Analytics, the world would be on your feet, trying to hire you! I know a few people who were in academics, made their mind for shifting, and were hired the very next day. However, if you are talking about teaching and training about other subjects, you will need to provide me more details about the subject, to be able to comment meaningfully. Regards, Kunal

123

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Top 10 Data Analytics Projects with Source Codes
Top 10 Data Analytics Projects with Source Codes
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Overview of Analytics Industry in India (my notes and views)

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