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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

zPod, India’s First AI-Driven Autonomous Vehicle

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 05 Jun, 2023
3 min read

Bengaluru-based start-up Minus Zero is making waves in the field of autonomous vehicles with the unveiling of India’s first AI-powered self-driving vehicle. The fully-electric prototype, called zPod, showcases impressive capabilities and aims to redefine mobility through cutting-edge technology. With a focus on affordability and advanced AI systems, Minus Zero’s zPod is poised to transform the transportation landscape. Let’s delve into the details of this groundbreaking development.

AI-based start-up Minus Zero has launched India's first AI-based self-driving autonomous vehicle, zPod.

Breaking Barriers: India’s First Autonomous Vehicle Hits the Road

Minus Zero proudly introduces zPod, a breakthrough in autonomous vehicle technology. The AI-based start-up has successfully developed a fully autonomous vehicle that can easily navigate various environmental and geographical constraints.

Also Read: AI Traffic Management System Hits Indian Roads

Level 5 Autonomy: A Giant Leap in Self-Driving Capabilities

The zPod prototype boasts Level 5 autonomy, representing the highest level of autonomous driving. This means that the vehicle can operate without human intervention in all driving conditions and environments, making it a significant achievement in self-driving technology.

Also Read: DataHour: Applications of Machine Learning in Self Driving Cars

Minus Zero's zPod with level-5 autonomy is India's first self-driving autonomous vehicle.

Seeds of Success: Minus Zero’s Impressive Funding Journey

Founded in 2021 by Gagandeep Reehal and Gursimran Kalra, Minus Zero has garnered substantial support from investors. With a seed funding of $1.7 million, the start-up has attracted notable backers, including Chiratae Ventures, Snow Leopard Ventures, IIT Mandi, and angel investors from the autonomous vehicles industry.

Gagandeep Reehal and Gursimran Kalra - founders of the AI-based start-up Minus Zero

Camera-Based Sensory System: Driving Innovation and Affordability

Unlike traditional autonomous vehicles that rely on expensive sensor arrays, zPod utilizes a network of high-resolution cameras strategically positioned as its primary sensory system. These cameras capture real-time images of the surroundings, which are then processed by the AI system to make informed decisions regarding navigation, speed control, and obstacle avoidance. Minus Zero’s camera-based approach enhances affordability and opens up new possibilities for autonomous mobility.

Also Read: 6 AI-powered Applications for Providing In-Vehicle Comfort

In-Campus Mobility: Pioneering Autonomous Transportation

The initial use case for zPod is limited to in-campus mobility, catering to transportation needs within specific controlled environments like tech parks, university campuses, and corporate campuses. This targeted approach allows Minus Zero to fine-tune their technology while providing efficient mobility solutions within defined areas.

Collaborating for Growth: Enhancing OEMs’ Advanced Driver-Assistance Systems

Using their pioneering technology, Minus Zero aims to collaborate with original equipment manufacturers (OEMs) to enhance their advanced driver-assistance systems (ADAS). By offering its expertise, Minus Zero intends to contribute to developing safer and more efficient autonomous features in vehicles.

India's first autonomous vehicle comes built-in with driver-assistance services and other latest technology.

Beyond Cars: Showcasing AI-based Autonomy Systems

While zPod is a remarkable vehicle in itself, the founders of Minus Zero emphasize that the start-up is not an original equipment manufacturer. Instead, the zPod serves as a demonstration of their robust AI-based autonomous vehicle technology. The focus remains on advancing their proprietary technologies and exploring collaborations with OEMs for future growth.

Also Read: Application of AI in Predictive Maintenance of Vehicles

Nature-Inspired AI and True Vision Autonomy: Pioneering Technologies

Minus Zero’s proprietary technologies, Nature Inspired AI (NIA) and True Vision Autonomy (TVA), drive their innovative solutions. NIA offers real-time low-power processing, adaptability to uncertain driving conditions, and eliminates the need for re-learning in different scenarios. TVA, a vehicle-agnostic technology, relies solely on cameras as its sensor suite, ensuring reliable and safe navigation in real-world scenarios.

Our Say

Minus Zero’s groundbreaking achievement with zPod marks a significant milestone in India’s autonomous vehicle landscape. By leveraging AI-based systems, the start-up aims to transform mobility by offering affordable and cutting-edge solutions. As Minus Zero continues to advance its proprietary technologies, we can expect a future where autonomous vehicles redefine transportation, making it more user-focused and experiential.

AIAI SystemsAi-drivenArtificial Intelligenceautomotive industryautonomous vehicleindiaminus zeronewsSelf-driving carTechnology
K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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