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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

Top 10 Data Science Webinars of 2020!

Ram Dewani
Ram Dewani Last Updated : 28 Dec, 2020
8 min read

Overview

  • Here is a list of Top 10 Data Science Webinars Organized by Analytics Vidhya in 2020
  • These Data Science Webinars are ranked on the basis of their number of registrations and quality
  • By no means is this list exhaustive. Feel free to add more in the comments below

 

Introduction

Learning data science has always been an uphill task for me whether through courses or videos on YouTube mainly because it lacked practical applications and career guidance from the industry experts. To fill this knowledge gap I have found webinars and meetups to be a perfect substitute. Since Coronavirus has disrupted meetups, webinars have completely taken over.

data science webinars

I have personally found Webinars to be context focussed, code-rich, and application focussed talk-sessions and that’s why I love them. In this article, I have highlighted some of the best webinars organized in the year 2020. These range from beginner-friendly career topics all the way up to advanced topics like that of Transfer Learning in NLP.

I have chosen the data science webinars according to their registration numbers and topic quality. Hope you enjoy it!

As mentioned above, data science webinars are a great way to learn the application focussed topic. If you are looking to begin your journey into the world of data science I would highly recommend you take up the comprehensive AI and ML Blackbelt+ course. Along with 14+ courses and 39+ projects, it comes with 1:1 mentorship sessions so that you are never off track from your goals!

 

Top 10 Data Science Webinars –

  1. Data Science vs. Data Engineering – Can you really separate them?  – 4500
  2. Going Beyond your First ML Project – 1535
  3. How to Deploy your ML Model? – 1405
  4. Storytelling using Visualizations – 1240
  5. Career Transition Into Data Science – 1232
  6. Getting Started with Recommendation Engines – 875
  7. Problem Solving In Business Analytics and Data Science – 834
  8. Business Analytics Isn’t Just About Model Building – 698
  9. An Introduction to Transfer Learning in NLP using HuggingFace – 646
  10. Getting Started with Natural Language Processing – 595

 

1. Data Science vs. Data Engineering – Can you really separate them?

“A data scientist is only as good as the data he/she has access to.”

data science webinars - DS vs DE

Most aspirants in the data science field want to land the coveted role of a “Data Scientist”. But did you know that tech giants such as Netflix, Facebook, Amazon, etc are hiring Data Engineers like never before to process the massive amount of data they are gathering!

Surprising, isn’t it? On second thought, not really. After all, “A data scientist is only as good as the data he/she has access to.”

A majority of people don’t even know what is data engineering and what is the role of data engineers. This is the perfect webinar to understand the difference between a data scientist and a data engineer and their industries. More than 4,000 people registered for this webinar!

This webinar is a great opportunity for you to hear from eminent industry experts who have seen both the data science and data engineering industries up close. Hear and learn from Kunal Jain (Founder & CEO, Analytics Vidhya), Ujjyaini Mitra (Head of Data for Zee5), K. Sankaran (Director, Data Science, LatentView Analytics) and Sachin Arora (Partner and Head of Lighthouse KMPG in India), as they draw upon their experience to help you navigate through these questions. See you at the webinar!

 

2. Going Beyond your First ML Project

data science webinars - Going beyond your first ML project

Oh, you’ve picked machine learning as your future career. You successfully completed your first machine learning project as well. Great! But what’s next? How do you go beyond the basics and take that next step, the big leap, that will make you industry-ready?

How can you build your profile in machine learning that will take you beyond the basics and into the realm of what the industry wants?

This super-exciting and interesting Webinar recording will help you in navigating your way through the bookish machine learning concepts to practical project learnings!

 

3. How to Deploy your ML Model? 

data science webinars - How to deploy your ML model

There is no real value generated to the business until your machine learning model is deployed and servicing real-world traffic no matter how good your model is!

One of the biggest challenges in the enterprise today is to integrate the developed machine learning model into a decision process. There is no real value generated to the business until your machine learning model is deployed and servicing real-world traffic no matter how good your model is!

If you have this gap in your data science portfolio on the model deployment part then you must watch this webinar.

In this webinar, Srivatsan Srinivasan will discuss moving data science from research to production through some real-world use cases. You will learn about various techniques and patterns to deploy and integrate the model with your business process.

 

4. Storytelling using Visualizations

data science webinars - Storyteling with visualization

Most often, Data Scientists get so involved with the process of model building that they forget the most crucial part – Turning insights into the form of stories!

Anand S, Founder and CEO of Gramener, is frequently approached by professionals asking questions like – where should they source the data from. But once you’ve got the data, the next questions to ask are:

  • How do you get interesting stories out of this data?
  • And how do you narrate these stories?

Are there patterns of questions that we can pose to the data and is there a systematic and structured way by which we can explore it? This talk by Anand S will answer these questions and more.

 

5. Career Transition Into Data Science

As organizations are realizing the potential of data science and machine learning, they are adapting to the trend by rapidly hiring prospective talent.

However, switching to a career in Data Science has its own challenges for freshers as well as for experienced professionals. Some of the most common challenges faced by Data Science aspirants are:
  • Can non-technical people transition to Data Science?
  • Will an experienced professional be treated as a relative fresher when he/she makes a switch to data science?
  • Which Data Science role should they consider?
  • Will their existing skill-set be useful in Data Science?
  • and more such questions.

If you are facing these questions, this is the perfect webinar recording for you! This webinar features speakers from Analytics Vidhya and eminent personalities from KPMG.

 

6. Getting Started with Recommendation Engines

data science webinars - getting started with recommendation engines

From Amazon to Netflix and Google to Goodreads, recommendation engines are one of the most widely used applications of machine learning techniques. Excited?

In today’s world, every customer has multiple choices. For example, If we’re looking for a book to read without any specific idea of what we want, there’s a wide range of possibilities how our search might pan out. We might waste a lot of time browsing around on the internet and trawling through various sites hoping to strike gold. We might look for recommendations from other people.

But if there was a site or app which could recommend books based on what we have read previously, that would be a massive help. Instead of wasting time on various sites, we could just log in, and voila!

In this webinar hosted by Dr. Sarabjot Singh Anand, an industry veteran who brings an immensely rich machine learning background, you will learn all about how recommendation engines work and how to get started with them as an analyst or data science professional.

 

7. Problem-Solving In Business Analytics and Data Science

problem business analytics and data science

Problem Solving is undoubtedly the most important skill in business analytics and data science. A structured thinking approach will not only help in constructing a clear and crisp problem statement but also help in communicating the results with the stakeholders.

In this webinar, Madhukar will talk about the following challenges and provide people with frameworks and best practices on structured thinking:

  • How to take ambiguous business problems and then break them into structured data science problems?
  • How to present your analysis and business insights in an impactful manner?
  • How to do clear and structured communications which people can easily understand?

 

8. Business Analytics Isn’t Just About Model Building

Models don’t solve business problems, people do.

Business Analytics Isn't Just About Model Building

One of the biggest presumptions amongst data scientists is that machine learning is all about the state of the art machine learning models but of course that’s totally false!

Business actions can’t be achieved alone, it requires collaboration along with in-depth domain knowledge. It is not the models you build but the business actions they translate to that create your impact as a data professional.

In this webinar, Eric will focus on how to maximize your impact by focusing less on the models you build and by focusing more on translating those models to definitive business actions.

 

9. An Introduction to Transfer Learning in NLP using HuggingFace

huggingface tokenizers

Interested in NLP? I am sure must have come across the recent developments in the field of transformer architectures and Transfer Learning.

The NLP field has come leaps and bounds in the last 3-4 years. And HuggingFace has been at the forefront of bringing the state-of-the-art NLP libraries to the NLP community. So who better to hear from about this than HuggingFace’s Co-Founder Thomas Wolf?

In this webinar, Thomas will start by introducing the recent breakthroughs in NLP that resulted from the combination of Transfer Learning schemes and Transformer architectures.

 

10. Getting Started with Natural Language Processing

Getting Started with Natural Language Processing

Just starting out on your journey in the field of Natural Language Processing? This is the perfect webinar for you!

In the past couple of years, Natural Language Processing (NLP) or processing of textual data has seen great interest and research. Text is not just another unstructured type of data, it has a lot more to it than what meets the eye. Textual data is a representation of our thoughts, ideas, knowledge, and even communication.

In this webinar, Raghav Bali will discuss the basics of natural language processing, creating word embedding, and developing models to perform various NLP tasks such as sentiment analysis, auto-correction, and much more.

 

End Notes

I have listed down the Top 10 Data Science and Machine Learning Webinars for the year 2020. These range from basic career guidance by experts to the advanced technical topic of transfer learning in NLP. You can skip to the webinar which suits your interest.

Hope this helps you take a step towards achieving your goal!

data engineering careerdata science careersdata science writing
Ram Dewani
Ram Dewani

Product Growth Analyst at Analytics Vidhya. I'm always curious to deep dive into data, process it, polish it so as to create value. My interest lies in the field of marketing analytics.

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