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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

6 Upcoming DataHour Sessions! Mark Your Calendars Now!

Atrij Dixit
Atrij Dixit Last Updated : 22 Feb, 2023
5 min read

Introduction

Looking to connect with the top minds in the field and deepen your knowledge of data tech? Get ready to accelerate your career in data tech! Introducing new sessions of February’s DataHour series, an exciting series of expert-led webinars that delve into cutting-edge advancements in the field. A platform where you gain invaluable insights and hands-on experience, learning from top industry leaders who make even the most complex topics easy to understand. Whether you’re an experienced professional or just starting out, join DataHour to connect with the brightest minds in the industry and take your knowledge to the next level. Through these webinars, you’ll gain hands-on experience, deepen your understanding of the field, and connect with industry leaders.
In this blog post, we’ll introduce you to a few exciting webinars that cover various topics related to data science and machine learning. Whether you’re interested in understanding Microsoft Excel, learning about A/B  testing, or exploring RCNN Family for Object Detection, there is a webinar for you. You’ll also have the opportunity to dive deep into the Power of Embedding, Data Preparation, and Feature Engineering in ML and get an introduction to Deep Learning with FastAI. So, let’s dive in and explore what these webinars have to offer.

Who can Attend these DataHour Sessions?

  • Aspiring individuals who looking to launch a career in the data-tech industry, including students and freshers.
  • Current professionals seeking to transition into the data-tech domain.
  • Data science professionals seeking to enhance their career growth and development.

Table of Contents

DataHour: Solving Business Problems with Microsoft Excel

Funnel analysis is the process that maps the flow of website visitors to a set of specific funnel steps, which provides us with insights into the number of conversions or signups. Businesses use funnel analysis to trace the user journey throughout their website, optimize it, and see how many visitors end up in each funnel stage.

📅Date: 22nd February 2023

⌚Time: 07:00 PM IST

🔗Registration Link: Register Now

In this DataHour, Eshan Tiwari, Data Science Lead at Google, will demonstrate how to perform funnel analysis in excel to identify product improvement opportunities and provide feature development recommendations.

Excel in DataHour

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DataHour: A/B testing – Theory, Practice, and Pitfalls

Technology companies rely heavily on data and experimentation to improve their products or services. Although there are several experimental designs available, the most frequently used. One is the Randomized Control Design, commonly referred to as A/B testing.

📅Date: 22nd February 2023

⌚Time: 8:30 PM IST

🔗Registration Link: Register Now

In this context, Srimugunthan will explain various experimental designs and highlight the benefits of using a Randomized Control Design. Additionally, he will discuss the essential steps involved in setting up an A/B test, including power analysis to determine the test duration based on use cases.

A/B Testing in DataHour

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DataHour: Demystifying RCNN Family for Object Detection

CNN or RCNN stands for Region-Based Convolutional Neural Network; it is a machine learning model used for computer vision tasks, specifically for object detection.

📅Date: 23rd February 2023

⌚Time: 7:00 PM IST

🔗Registration Link: Register Now

In this DataHour, Jaiyesh will explain the progression of RCNNs, starting from RCNN to Fast RCNN and, finally, the state-of-the-art Faster RCNN, followed by a walkthrough of the Faster RCNN pipeline in python for training on a custom dataset.

RCNN in DataHour

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DataHour: Data Preparation and Feature Engineering in ML

During the DataHour, Rishabh will cover the fundamental process of Machine Learning, including defining a problem, proposing a solution, constructing and transforming datasets, and a practice problem. Participants will gain insights into the necessary steps for identifying key components of a problem and selecting appropriate ML algorithms.

📅Date: 23rd February 2023

⌚Time: 8:30 PM IST

🔗Registration Link: Register Now

Participants will also learn how to construct datasets and transform data for ML problems, which can significantly impact the accuracy and effectiveness of the models developed. Additionally, the practice problem will allow participants to apply their newfound skills and knowledge.

Feature Engineering

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DataHour: Unlocking the Power of Embeddings

During this DataHour, Priya will cover embeddings, dense numerical representations of real-world objects and relationships expressed as a vector. She will explain what an embedding is, how it encodes semantic relations, and how to use and train meaningful embeddings. Participants will clearly understand how embeddings work and how they can be utilized effectively in various applications.

📅Date: 24th February 2023

⌚Time: 7:00 PM IST

🔗Registration Link: Register Now

NLP

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DataHour: Introduction to Deep Learning with FastAI

During this DataHour session, Victor will provide an in-depth demonstration of using the FastAI library to get started in Deep Learning. Victor will highlight the features and capabilities of FastAI and provide examples of what can be achieved with this library.

📅Date: 24th February 2023

⌚Time: 8:30 PM IST

🔗Registration Link: Register Now

Participants will gain insights into how to run notebooks and how to utilize the FastAI library for their own projects. Moreover, Victor will explain this library’s simplicity and ease of use, which can be a significant advantage for those new to Deep Learning. By the end of the session, participants will have a solid understanding of the potential of FastAI and how to get started with it.

REGISTER NOW

Conclusion

Don’t miss out on this opportunity to take your tech journey to the next level. Register for DataHour sessions today and discover a world of possibilities. Have questions? Reach out to the speaker during the session or email us at [email protected]. Missed a session? No problem, catch up with recordings on our YouTube channel and resources sent to your registered email. What are you waiting for? Reserve your spot now!

Connect

If you’re having trouble enrolling or would like to conduct a session with us. Contact us at [email protected].

Atrij Dixit
Atrij Dixit

Atrij--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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