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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

Unleash Your Data Insights: Learn from the Experts in Our DataHour Sessions

Atrij Dixit
Atrij Dixit Last Updated : 11 Apr, 2023
6 min read

Introduction

Analytics Vidhya DataHour is designed to provide valuable insights and knowledge to individuals looking to build a career in the data-tech industry. These sessions cover a wide range of topics, from the fields of artificial intelligence, and machine learning, and various topics related to data science. This blog post introduces a series of upcoming DataHour sessions, each of which will focus on a specific topic related to data science and its applications in various industries.

In this blog, we will cover a variety of topics that are essential for anyone starting out in the field of data science. We will begin with a beginner’s guide to natural language processing, a powerful tool used for text analysis and language modelling. We will then move on to web scraping using Python libraries, a crucial skill for extracting data from the web. Furthermore, we will provide an overview of named entity recognition (NER), a technique used for identifying and classifying named entities in text. Whether you’re just getting started or looking to expand your knowledge, this blog is the perfect place to learn and grow in the exciting field of data science.

Who can Attend these DataHour Sessions?

  • Aspiring individuals 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: A Beginner’s Guide to Natural Language Processing

The Natural Language Toolkit (NLTK) is a widely-used library for Natural Language Processing (NLP) in Python. It provides a range of tools for preprocessing text data for analysis, including machine learning models. One key function of NLTK is to transform text into numerical format, which can then be used by the model. NLTK has many features that make it useful for NLP tasks, such as tokenization, stemming, lemmatization, and part-of-speech (POS) tagging. These tools are crucial for text analysis and can help improve the accuracy of NLP models.

In this Datahour, Akash will explain the various features, libraries and methodologies of conversions used in NLP from basics.

📅Date: 11th April 2023

⌚Time: 07:00 PM IST

🔗Registration Link: Register Now

DataHour: A Beginner's Guide to Natural Language Processing

DataHour: Web Scraping Using Python Libraries

If you’re looking to learn more about web scraping, you’ve come to the right place! In this session, we’ll be sharing our technical knowledge on how to fetch data from both dynamic and static live websites, and how to use that extracted information for analysis.

Web scraping, also known as data web scraping, involves automatically extracting information from websites. This method is widely utilized for research, data analysis, and automation tasks. By utilizing web scraping, one can gather information from diverse sources and utilize it to acquire significant insights, innovate new products or services, and make informed decisions for their business.

📅Date: 12th April 2023

⌚Time: 07:00 PM IST

🔗Registration Link: Register Now

Overall, web scraping is a powerful technique for collecting and analyzing data from the web. With the right tools and techniques, you can extract valuable insights and gain a competitive advantage in your industry.

DataHour: Web Scraping using Python Libraries

DataHour: Beginners Guide to Data Analysis Using Alteryx and PowerBI

During the upcoming Datahour, Vivek will delve into the fundamentals of Data Analysis and the various ways it can be applied across diverse fields. He will demonstrate the criticality of data cleansing using Alteryx to transform raw datasets into refined information that can be easily analyzed. Moreover, he will elaborate on the process of framing analysis through data preparation.

📅Date: 12th April 2023

⌚Time: 08:30 PM IST

🔗Registration Link: Register Now

Furthermore, Vivek will showcase the use of Power BI in analyzing data, including the creation of a visually appealing dashboard for comprehensive data visualization. This presentation promises to provide valuable insights and practical tips for individuals looking to improve their understanding of data analysis and its potential applications.

DataHour: Beginners Guide to Data Analysis using Alteryx and PowerBI

DataHour: An Overview of Named Entity Recognition(NER)

In this upcoming Datahour session, Pallavi will provide a comprehensive understanding of Named Entity Recognition (NER), covering various aspects of the process. The session will begin with an introduction to Natural Language Processing (NLP) and NER. Pallavi will then explain the methods and usage of NER and cover different libraries that can be used to train NER models, from basic to advanced levels.

📅Date: 13th April 2023

⌚Time: 08:30 PM IST

🔗Registration Link: Register Now

Attendees will also get to learn about the practical implementation of NER through examples using 4-5 different libraries. Pallavi will discuss the usage of libraries for different languages and domain-specific applications of NER. The session promises to provide valuable insights into NER and its practical applications for anyone interested in NLP and data analysis.

DataHour: An Overview of Named Entity Recognition(NER)

DataHour: Exploratory Data Analysis Using Matplotlib and Seaborn

Exploratory Data Analysis (EDA) is a methodology used for summarizing the primary characteristics of datasets. Its purpose is to gain a comprehensive understanding of the data, including the variables and their relationships. By doing so, it can facilitate the formulation of hypotheses that may be beneficial when constructing predictive models.

📅Date: 14th April 2023

⌚Time: 07:00 PM IST

🔗Registration Link: Register Now

In this upcoming DataHour session, Nitin will guide the attendees on how to perform Exploratory Data Analysis (EDA) using data visualization techniques. The focus will be on using the Matplotlib and Seaborn Python libraries to conduct EDA. Nitin will delve into the significance of EDA, its practical applications, and its impact on data analysis. Attendees will also get hands-on experience with EDA using the Matplotlib and Seaborn libraries. Nitin will cover these topics in detail during the session. Overall, the session promises to provide valuable insights and practical knowledge on EDA for anyone interested in data analysis.

Exploratory Data Analysis using Matplotlib and Seaborn

DataHour: Clustering and Segmentation in Data Science

Data Science comprises various branches, and one of its vital components is Clustering and Segmentation. These techniques have widespread applications across numerous domains, such as business, healthcare, and social sciences, to group together data points that belong to similar clusters or cohorts. For example, clustering can be used to classify customers based on their purchasing habits or segment users based on their interests. Clustering and segmentation are powerful tools that can extract valuable insights from data and improve the transparency of decision-making processes.

📅Date: 14th April 2023

⌚Time: 07:00 PM IST

🔗Registration Link: Register Now

In the upcoming DataHour session, Arani will delve into the nuances of clustering and segmentation techniques, focusing on K Means clustering and Decision Trees. Arani will provide a detailed explanation of these techniques, highlighting their real-life applications. The session will equip attendees with practical knowledge of clustering and segmentation, enabling them to implement these techniques effectively in their work. The insights gained from the session can aid in developing better business strategies, optimizing operations, and improving overall performance.

Clustering and Segmentation in Data Science

Conclusion

Don’t miss this chance to advance your technological path. Discover a world of possibilities by signing up for DataHour sessions right away. Have inquiries? During the session, you can contact the speaker, or you can send us an email at [email protected]. Why are you holding out? Book your space right away!

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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