https://github.com/newking9088/mitx_6.86x_machine_learning_with_python-from_linear_models_to_deep_learning_fall_2020

Welcome to 6.86x Machine Learning with Python–From Linear Models to Deep Learning. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover: Representation, over-fitting, regularization, generalization, VC dimension; Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning; On-line algorithms, support vector machines, and neural networks/deep learning. You will be able to: Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models Choose suitable models for different applications Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering You will implement and experiment with the algorithms in several Python projects designed for different practical applications. You will expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.

Last synced: 21 Apr 2025

https://github.com/newking9088/mitx_14.310x_data_analysis_for_social_scientist_fall_2020

This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, randomized control trials (and A/B testing), machine learning, data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction on the use of the statistical package R, and opportunities for students to perform self-directed empirical analyses. Students taking the graduate version will complete additional assignments. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.

Last synced: 21 Apr 2025

https://github.com/newking9088/mitx-6.431x-probability---the-science-of-uncertainty-and-data

A guide on how to use the wealth of available material This class provides you with a great wealth of material, perhaps more than you can fully digest. This “guide" offers some tips about how to use this material. Start with the overview of a unit, when available. This will help you get an overview of what is to happen next. Similarly, at the end of a unit, watch the unit summary to consolidate your understanding of the “big picture" and of the relation between different concepts. Watch the lecture videos. You may want to download the slides (clean or annotated) at the beginning of each lecture, especially if you cannot receive high-quality streaming video. Some of the lecture clips proceed at a moderate speed. Whenever you feel comfortable, you may want to speed up the video and run it faster, at 1.5x. Do the exercises! The exercises that follow most of the lecture clips are a most critical part of this class. Some of the exercises are simple adaptations of you may have just heard. Other exercises will require more thought. Do your best to solve them right after each clip — do not defer this for later – so that you can consolidate your understanding. After your attempt, whether successful or not, do look at the solutions, which you will be able to see as soon as you submit your own answers. Solved problems and additional materials. In most of the units, we are providing you with many problems that are solved by members of our staff. We provide both video clips and written solutions. Depending on your learning style, you may pick and choose which format to focus on. But in either case, it is important that you get exposed to a large number of problems. The textbook. If you have access to the textbook, you can find more precise statements of what was discussed in lecture, additional facts, as well as several examples. While the textbook is recommended, the materials provided by this course are self-contained. See the “Textbook information" tab in Unit 0 for more details. Problem sets. One can really master the subject only by solving problems – a large number of them. Some of the problems will be straightforward applications of what you have learned. A few of them will be more challenging. Do not despair if you cannot solve a problem – no one is expected to do everything perfectly. However, once the problem set solutions are released (which will happen on the due date of the problem set), make sure to go over the solutions to those problems that you could not solve correctly. Exams. The midterm exams are designed so that in an on-campus version, learners would be given two hours. The final exam is designed so that in an on-campus version, learners would be given three hours. You should not expect to spend much more than this amount of time on them. In this respect, those weeks that have exams (and no problem sets!) will not have higher demands on your time. The level of difficulty of exam questions will be somewhere between the lecture exercises and homework problems. Time management. The corresponding on-campus class is designed so that students with appropriate prerequisites spend about 12 hours each week on lectures, recitations, readings, and homework. You should expect a comparable effort, or more if you need to catch up on background material. In a typical week, there will be 2 hours of lecture clips, but it might take you 4-5 hours when you add the time spent on exercises. Plan to spend another 3-4 hours watching solved problems and additional materials, and on textbook readings. Finally, expect about 4 hours spent on the weekly problem sets. Additional practice problems. For those of you who wish to dive even deeper into the subject, you can find a good collection of problems at the end of each chapter of the print edition of the book, whose solutions are available online.

Last synced: 23 Feb 2025

https://github.com/newking9088/sql-guide-to-solve-complex-data-science-problems

This is a comprehensive SQL guide for both MySQL users and PostgreSQL users, covering topics from basic `SELECT` statements to advanced window functions. My SQL learning journey and the suggestions from my mentees, colleagues and seniors to document this were the motivations to write this document.

dataanalysis datascience-machinelearning mysql postgresql sql

Last synced: 18 Mar 2025

https://github.com/newking9088/dental_office_ai_agent

🦷 Autonomous AI Agent for dental offices that conducts human-like phone interactions with insurance providers to verify benefits, eligibility, and process administrative tasks. Built with LLMs for natural language processing and automated workflow management.

Last synced: 13 Feb 2025

https://github.com/newking9088/cop3035-cgs5935-introduction-to-programming-using-python

COURSE DESCRIPTION: This is an introductory course in Python using the Textbook by Tony Gaddis.

Last synced: 23 Feb 2025

https://github.com/newking9088/ad-hoc-data-analysis-automation

Use Python and SQL to automate data analysis by pulling data from AWS Redshift through a pipeline

automation aws grafana python3 quicksight redshift sql sqlite

Last synced: 23 Feb 2025

https://github.com/newking9088/a-b-n_testing_best_charging_algorithm

This repository contains code and documentation for evaluating battery charging algorithms through statistical analysis. We developed a framework for comparing multiple charging algorithms against the traditional Constant Current-Constant Voltage (CCCV) method using A/B/N testing and post-hoc analysis.

Last synced: 13 Mar 2025

https://github.com/newking9088/battery-intelligence-and-automation

This repo contains information about battery intelligence and data pipeline automation.

Last synced: 13 Mar 2025

https://github.com/newking9088/marketing_campaign_ml_prediction_dashboard

Transform your marketing strategy with our intuitive ML Prediction Dashboard, providing real-time, data-driven insights to optimize campaign success.

data-visualization finance-application streamlit-webapp

Last synced: 23 Feb 2025

https://github.com/newking9088/bch5884-programming-for-chemist-and-biochemist

COURSE DESCRIPTION : The modern chemist or biochemist is required to be proficient in many techniques including spectroscopy, mass spectrometry, sequence analysis, structural biology, and other biophysical techniques. Analysis of data from these sources often requires developing new algorithms and computer programs to process the data. This course will cover the fundamentals of programming using the scripting language Python and will be geared towards scientists with a need to process data in novel ways. Topics that will be covered include an introduction to UNIX, HTML, the fundamentals of programming, scripting with Python, and object-oriented programming. As part of the course, students will be required to do a programming project related to their research. No previous knowledge of programming is required.

Last synced: 23 Feb 2025

https://github.com/newking9088/marketing_campaign_customer_segmentation_classification

This repository contains a machine learning project to optimize marketing strategies for bank term deposits. Using models like Logistic Regression, Random Forest, SVM, KNN, XGBoost and Neural Network, it classifies customers based on their likelihood of subscribing.

artificial-neural-networks classification-algorithm customer-segmentation machine-learning

Last synced: 23 Feb 2025

https://github.com/newking9088/isc-5425-introduction-to-bioinformatics

Catalog Description: This course on bioinformatics provides a quantitative framework for understanding how the genomic sequence and its variations affect the phenotype. The course is designed for biologists and biochemists seeking to improve quantitative data interpretation skills, and for mathematicians, computer scientists and other quantitative scientists seeking to learn more about computational biology. Laboratory exercises are designed to reinforce the classroom learning.

Last synced: 23 Feb 2025

https://github.com/newking9088/data-science-materials-from-coursera

Contains reading materials, assignments and some real projects for data science, ML, DL and AI.

Last synced: 23 Feb 2025

https://github.com/newking9088/real_estate_cost_estimation_and_property_recommendation

We've developed an intelligent real estate recommendation engine that achieves 95% accuracy in price predictions while personalizing property suggestions to individual preferences. By implementing the KNN algorithm, our system analyzes thousands of historical transactions to determine recommendation properties

automated-machine-learning descriptive-analysis descriptive-statistics mlops-workflow predictive-modeling recommender-system

Last synced: 21 Apr 2025

https://github.com/newking9088/nasdaq100_hedge_fund_dashboard

LLMs empower hedge funds and investors to leverage NASDAQ-100 insights, making informed decisions with key financial metrics. An LLM-powered app allows uploading financial reports and images for analysis, offering instant insights and trend analysis in a dynamic market. 🚀📊

dashboard-application data-visualization langchain llm rag streamlit

Last synced: 01 Apr 2025

https://github.com/newking9088/test

Just a test

Last synced: 23 Feb 2025

https://github.com/newking9088/newking9088

Config files for my GitHub profile.

config github-config

Last synced: 23 Feb 2025

https://github.com/newking9088/llm_semantic_search_systems

This repository explores AI technologies including Large Language Models (LLMs), semantic search implementation, prompt engineering techniques, Retrieval-Augmented Generation (RAG), AI agent development, and fine-tuning methodologies. Practical examples and implementation guides provided.

Last synced: 10 Apr 2025

https://github.com/newking9088/building_insurance_predictive_modeling

A comprehensive case study on implementing predictive modeling in insurance

ab-testing bagging-ensemble boosting-tree feature-selection glm insurance machine-learning mlops-workflow model-monitoring modeldeployment modelvalidation regulatory-compliance risk-analysis

Last synced: 22 Feb 2025

https://github.com/newking9088/css-html-javascript-solutions-coursera

This repository contains solutions for module-2, module-3, module-4 and module-5 for the course css,html and javascript for web-developers from coursera.

Last synced: 23 Feb 2025

https://github.com/newking9088/python_for_data_scientist

A detailed reference covering Python's essential tools for data science - from basic data structures (lists, tuples, sets, dictionaries) to advanced libraries (NumPy, Pandas) and visualization techniques (Matplotlib, Seaborn, Plotly). Includes practical examples, best practices, and optimization tips for working with data at scale.

Last synced: 22 Feb 2025

https://github.com/newking9088/product_recommendation_nlp_roberta_vader

Sentiment-Enhanced Product Recommendation System for E-Commerce: A Comparative Analysis of RoBERTa and VADER

Last synced: 13 Mar 2025

https://github.com/newking9088/orderreturnclassifier

orderReturnClassifier is a machine learning project that predicts whether an order will be returned. By analyzing various order features using classification algorithms, it aims to help businesses reduce return rates and improve customer satisfaction.

classification data-visualization ecommerce machine-learning

Last synced: 23 Feb 2025