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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

PortfolioPilot: Verified ChatGPT Plugin for Investing Released

Yana Khare
Yana Khare Last Updated : 08 May, 2023
4 min read

Introducing PortfolioPilot, A Verified ChatGPT Plugin

Investing in the stock market can be daunting, especially for those needing more experience and knowledge. With so much information available, it can be easy to feel overwhelmed and make decisions based on emotions rather than facts. However, a new AI-powered investing tool promises to improve investing for millions. Meet PortfolioPilot, a verified ChatGPT plugin AI software entrepreneur Alex Harmsen co-founded. Hedge fund-caliber models back the tool and use OpenAI’s ChatGPT to manage portfolios effectively.

Also Read: Stock Prices Prediction Using Machine Learning and Deep Learning

PortfolioPilot offers a multitude of features that aim to simplify the investing process. Users can copy and paste their portfolios into the tool for analysis. Additionally, they can also receive investment recommendations, search for market information, and ask the bot questions – all for free.

Also Read: OpenAI Seeks Trademark for ‘GPT’

What Does The Co-Founder Have to Say?

“We are on an ambitious mission to build a complete map of how the economy works,” says Alex Harmsen, CEO and Co-Founder of PortfolioPilot, in a statement. “This will dramatically improve investing for millions, decrease risk in decision making, and remove huge inefficiencies in the financial sector.”

Alex Harmsen, CEO and Co-Founder of PortfolioPilot, a verified ChatGPT Plugin, is an AI-powered investing tool.


The tool has been in beta for over a year, with close to a billion dollars of assets on the platform. However, it was only recently opened up to the general public, said Alex. As part of their multi-week launch strategy, thousands of users signed up over the last few days. They also teased a tax loss harvesting tool they plan to launch on December 1st. It is one of the most requested things from people already using the system.

According to new data, Americans have wasted no time trying ChatGPT for stock recommendations since the AI-powered chatbot became available in November. A study released this week by The Motley Fool found that 47% of U.S. adults have already used the tool for stock picks. Moreover, 69% said they are open to doing so.

Learn More: Everything You Need to Know About ChatGPT

How Does PortfolioPilot Work?

PortfolioPilot uses a combination of AI and machine learning to make recommendations. The tool analyzes the user's portfolio and identifies areas for improvement. | stock


PortfolioPilot uses a combination of AI and machine learning to make investment recommendations. The tool analyzes the user’s portfolio and identifies areas for improvement. Further, it provides targeted suggestions based on market trends and other relevant factors.

Users can also use AI-powered investing tools like PortfolioPilot to search for information about specific companies, industries, or markets. The tool retrieves data from reliable sources such as Bloomberg and Yahoo Finance. Thus, providing users with accurate and up-to-date information about their investments.
In addition, users can ask the bot questions about investing, such as “What should I do if the market crashes?” or “How can I diversify my portfolio?” The bot will provide answers based on its extensive knowledge base and the user’s circumstances.

Features Of PortfolioPilot

PortfolioPilot, an AI-powered investing tool, offers a variety of investment strategies aimed at helping users achieve their financial goals. | stock

PortfolioPilot offers a variety of investment strategies aimed at helping users achieve their financial goals. One such strategy is tax loss harvesting, which minimizes taxes on investment gains. Here are the features included in it:

  1. ETF Searching: PortfolioPilot enables users to search for exchange-traded funds (ETFs) based on various criteria, including performance, expense ratio, and assets under management.
  2. Company Comparison: The platform allows users to compare multiple companies based on financial metrics such as revenue, profit margin, and earnings per share.
  3. Portfolio Importing: Users can import their existing portfolios into PortfolioPilot to track their performance and analyze their holdings.
  4. Portfolio Assessments: PortfolioPilot provides users with reviews of their portfolios’ risk level, diversification, and overall performance.
  5. Portfolio Analytics: The platform offers in-depth analytics tools that allow users to analyze their portfolios’ performance and identify areas for improvement.
  6. Subsequent Investment Suggestions: Based on a user’s portfolio and investment goals, PortfolioPilot can provide personalized investment suggestions.
  7. Security Details: PortfolioPilot provides users with detailed information about individual securities, including historical prices, dividends, and earnings.
  8. Stock Search: Users can search for individual stocks by ticker symbol or company name. Additionally, they can view detailed information about each stock.

Benefits of Using PortfolioPilot

There are several benefits to using AI-powered investing tools like PortfolioPilot for managing your investments. Some of these include:

  1. Improved Decision Making: Using PortfolioPilot, users can make more informed investment decisions. The tool provides data-driven insights that help users identify potential risks and opportunities.
  2. Time-Saving: Researching individual stocks and analyzing market trends can be time-consuming. PortfolioPilot streamlines this process, allowing users to access relevant information and make decisions more efficiently and quickly.
  3. Cost-Effective: PortfolioPilot is a free tool to help users save money on costly consulting fees. The tool can help users maximize their returns without incurring unnecessary costs by providing personalized recommendations based on each user’s unique circumstances.
  4. Accessible: PortfolioPilot is available to anyone with an internet connection, making it accessible to many investors. The tool is easy to use, even for those lacking stock market experience.

Our Say

PortfolioPilot, a verified ChatGPT plugin, is a promising new tool for investors looking to simplify and improve their investment strategies | stock


PortfolioPilot, a verified ChatGPT plugin, is a promising new tool for investors looking to simplify and improve their investment strategies. With its advanced AI-powered features, the tool provides users with data-driven insights and personalized recommendations. This can help them maximize their returns and minimize risk. With the number of users of ChatGPT for stock recommendations increasing, it is clear that AI-powered investing tools like PortfolioPilot are the future of investment management.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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Responses From Readers

Clear

Brian Heinz
Brian Heinz

As to your PortfolioPilot My need is to down load my Portfolio into PortfolioPilot and hopefully the AI tool will scan my portfolio and make suggestions concerning buy hold or sell recommendations and the data to support those recommendations

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