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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

List of Upcoming DataHour Session(s)

Shweta Rawat
Shweta Rawat Last Updated : 22 Nov, 2022
4 min read

Introduction

Do you know that almost 2.5 Quintillion bytes of data are generated daily, and much more data is yet to be explored?

Data Science is no more talk of the Future; it’s the present. So enhancing your knowledge in data science and related topics is the Smartest Decision. Data Science helps big businesses in making profit-driven decision-making. Private Sector Companies rely on Data Scientists to look for loopholes to improve before facing any Losses. 

We at Analytics Vidhya, aiming to build a future data tech community, organise DataHour sessions. Here you can explore various topics coming under Data Science. The best thing about these sessions is that you can Attend them from any place and at your convenience. You need a mobile phone/laptop and a good Internet connection.

So please don’t wait and Register Now for our Upcoming DataHour Sessions!

Requirements for Registration

Curiosity and Eagerness to Learn and Grow.

Who can Attend these DataHour Sessions?

  • Students who want to pursue a career in the Data-Tech Industry.
  • Working Professionals looking to change their career in the Data-Tech Industry.
  • Professionals in the data science industry who wish to improve their current skills and gain knowledge regarding ongoing trends.

DataHour Session: Understanding Dimensionality Reduction

About DataHour: Our Speaker Varun Behl, Data Science Engineer at Adobe, will discuss PCA and Factor analysis to understand how dimensional reduction is performed.

You will learn that while building ML/DL models, one has to take care of a large volume of data, increasing model run time and the complexity of understanding the predictions. Dimensionality reduction is a way to deduce large columns of data to few; the transformed data explain data precisely and makes generating output easy for classic ML models.

Book Your Seat Now!

About the Speaker: Varun Behl is a Data Science Engineer with Adobe. He has 5+ years of experience in the Data Science profession and has worked in industries like Telco, Ecommerce, Pharma, Retail, Musigma and Web Analytics.

Connect with Varun Behl on LinkedIn.

DataHour Session: Statistics in Data Science

About DataHour: Our Speaker Abhishek Raj Permani, Machine Learning Engineer at Jaguar Land Rover, will talk about necessary things about statistics in Data Science right from scratch, along with good resources to learn the same.

Data Scientists use statistics to analyze data patterns and find conclusions from them. This provides strategies for businesses to grow in the future. Statistics is the core of advanced ML algorithms that gather and translate data insights into actionable events.

DataHour Sessions

Book Your Seat Now!

About the Speaker: Abhishek is currently working as a Machine Learning Engineer with Jaguar Land Rover.  He is a Computer Science Graduate from IIT. He has worked with “Samsung Research and Development Institute India” in the Data Science team and presented three data science papers in international conferences. Abhishek is also a mentor to more than 2000 students.

Connect with Abhishek Raj Permani on LinkedIn.

DataHour Session: Indeterminate String Matching

About DataHour: Our Speaker Munmun Das, Principal – Automation and Innovation at Accenture, will cover various algorithms of indeterminate string matching with sample code.

Indeterminate String Matching, though it sounds simple, has applications in very complex applications. The broad spectrum of applications includes the areas of Document Similarity, Information Security, Document Matching, Bioinformatics (DNA sequence matching), Pattern Recognition etc.

DataHour Sessions

Book Your Seat Now!

About the Speaker: Munmun Das is a professional and research scholar with 16 + years of experience in Software Project Management, Product development, Product strategy, and delivery of large-scale, complex automation/transformation solutions, and web and mobile applications. She has implemented handshaking projects on RPA with Dot Net web applications and successfully built on-demand RPA.

Connect with Munmun Das on LinkedIn.

DataHour Session: NFTs – Friend or FOMO?

About DataHour: Our Speaker Ishaan Bhardwaj, Full Stack Software Engineer at IDS Infotech Limited, will unearth whether the NFT is worth the hype in the current world or just a trending topic to be discussed.

An NFT is a digital asset representing real-world objects like art, music, in-game items and videos. They are bought and sold online frequently with cryptocurrency and are generally encoded with the same underlying software as many cryptos. Each NFT has a digital signature that makes it impossible for NFTs to be exchanged for or equal to one another (hence, non-fungible).

DataHour Sessions

Book Your Seat Now!

About the Speaker: Ishaan Bhardwaj is a Tech Enthusiast who creates awesome things through the art of coding and is currently working as Full Stack Software Engineer with IDS Infotech Limited. He has built awesome websites and projects, and working on numerous projects end-to-end enabled him to acquire project management skills along the way. He assists businesses in the building and deploying coherent software solutions. 

Connect with Ishaan Bhardwaj on LinkedIn.

Conclusion

Isn’t the topics covered above excite you? DataHour sessions are the best source to Enhance your skill set. You can attend these sessions from anywhere in your Comfort. You can also ask your query Directly to the Speaker through Q&A Session. So don’t wait for more and Register Now for the above Upcoming sessions. If you have missed previous DataHour sessions, then read its synopsis here.

Connect with Us

Kindly Connect with us for any query at [email protected]. 

machine learningstatsticsstring matchingThe DataHourVideoswebinar series
Shweta Rawat
Shweta Rawat

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Top 10 Data Analytics Projects with Source Codes
Top 10 Data Analytics Projects with Source Codes
Book Your Seats Now For Upcoming DataHour Session(s)
Book Your Seats Now For Upcoming DataHour Session(s)

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