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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

Join the Upcoming Datahour Sessions!

D
Deeksha Rana Last Updated : 19 Sep, 2022
4 min read

Introduction

Data Science is the emerging and essential domain of the current time, especially in India and  Analytics Vidhya is India’s largest Data science community. We guide Data science enthusiasts on how to enter and excel in this domain. With this goal in mind Analytics Vidhya has launched DataHour sessions. These sessions will not only help you upgrade your existing dataset but will also help you learn the practical applications of Data Science.

So Don’t wait; click and register for the  DataHour now!

Prerequisites for registering:

Enthusiasm and interest in Data Science.

Who can Attend these DataHour Sessions?

  • Any student or professional looking for a way to enter this field.
  • Data Science professionals want to update and polish their domain knowledge and skills.

DataHour Session: AI & ML in the Automotive Industry

Artificial Intelligence and Machine Learning are changing the world at a huge pace, and their impact can be well observed in the automotive industry. In this DataHour, you will learn how AI/ML is used in the automotive industry to create self-driving cars, predict maintenance, and improve safety.

BOOK YOUR SEAT NOW!

The speaker of this Datahour, Abhishek Raj Permani, who is currently designated as Machine Learning Engineer at Jaguar Land Rover, is a Computer Science Graduate from IIT. He has experience working with Samsung Research and Development Institute, India as a Data science team member. He has now presented three data science papers at international conferences. He is also working as a mentor of more than 2000 students by providing Data Structure and Algorithm courses and various live sessions.

DataHour Session: Building Machine Learning Models in BigQuery

Building Machine Learning models is an efficient way of channelling an idea into practicality. Nowadays, for doing so, Google BigQuery is found to be one of the best supporters, providing various features.  In this Datahour, you will learn how Google Cloud BigQuery aids various features like regression, classification, forecasting, dimensionality reduction, collaborative filtering, hyperparameter tuning etc.

DataHour

BOOK YOUR SEAT NOW!

Through this Datahour, learn from Dan Sullivan. He is the Principal Data Architect at 4 Mile Analytics and the author of the official Google Cloud study guides for the Professional Architect, Professional Data Engineer, and Associate Cloud Engineer with tremendous experience in data architecture, data science, machine learning, stream processing, and cloud architecture domain. He also helps the community by training software engineers.

DataHour Session: NLP aspects in Telecommunication Industry

Natural Language Processing (NLP) is a methodology that studies how machines and languages interact by training the computer to understand human speech as it is spoken. This DataHour will explain how machine learning and deep learning help Interpret current data using NLP and how this NPL technology is changing the Telecommunication industry.

DataHour

BOOK YOUR SEAT NOW!

Learn with Sakshi Gujral. She is a GATE  and UGC-NET qualifier with almost 5 years of experience working as a Data Scientist with leading Industries like TCS Genpact. Currently working as a Data Scientist at Concentrix and simultaneously pursuing her PhD from IIIT- Delhi. She has successfully published five research papers in reputed Journals.

DataHour Session: Practical Time Series Analysis

Do you know that you can perform the dynamic analysis of Data in Nature? This is called Time series analysis, wherein you analyze the trend in the subject that changes over time. This DataHour session will cover all about time series predictions from the first level and demonstrate how various TSA algorithms are executed using Python.

BOOK YOUR SEAT NOW!

Learn the analysis of Time series Data with Rahul Kumar. Rahul holds 7 + years of experience in the Data Science and analysis domain with the expertise in Machine Learning and NLP and is currently working with Oracle as a Data Scientist.

Conclusion

Stop thinking and start acting now to make a better future in the field of Data Science. Dive into this immense pool of knowledge and register now. For any doubts regarding the session or the topics, please reach us at [email protected], or clarify it directly with the speaker during the session. If you skip any part of the session, ‘The DataHour,’ you may watch the recordings on our YouTube channel or read the synopsis here.

Connect

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

automotive industryBigQuery MLlearning pathNLPtelecommunicationThe DataHourTime Series Analysiswebinar seriesYoutube Videos
D
Deeksha Rana

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Top 10 Data Analytics Projects with Source Codes
Top 10 Data Analytics Projects with Source Codes
Upcoming DataHour Sessions: Book your Calendars!
Upcoming DataHour Sessions: Book your Calendars!

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