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Free Download Udemy - Time-Series Analysis & Regression Forecasting with Python Published 4/2025 Created by eTech School MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 71 Lectures ( 5h 45m ) | Size: 2 GB Transform raw data into powerful forecasts with Python-learn time-series modeling, regression, real-world forecasting. What you'll learn Time-series concepts, notations, and use cases. Data loading, preprocessing, and feature engineering using Python. Visualizing time-series data for insights. Forecasting using AR, MA, ARIMA, and SARIMA models. Handling seasonality and trend components in data. Performing train-test splits and model validation correctly. Applying linear regression (simple and multiple) for forecasting. Interpreting regression outputs and evaluating model accuracy. Requirements Basic knowledge of Python (variables, loops, basic functions). Familiarity with basic statistics (mean, median, correlation). Some exposure to pandas, matplotlib, or NumPy is helpful but not mandatory. No prior experience with time-series or regression needed. Description Course Introduction:Forecasting is at the heart of modern data science, powering decision-making across finance, retail, healthcare, and beyond. This comprehensive course is your step-by-step guide to mastering time-series analysis and regression-based forecasting using Python. Whether you're a budding data scientist or an analyst aiming to add predictive analytics to your skillset, this course covers everything from basic notations to advanced models like ARIMA and SARIMA. You'll also learn how to prepare data for machine learning, visualize trends, and validate models like a pro.Through hands-on coding in Python, real-world use cases, and expert-led instruction, you'll gain the confidence to build and deploy forecasting models that actually drive impact.Section 1: Foundations of Time-Series Analysis in PythonStart your journey by understanding the fundamentals of time-series data-what makes it unique and why it matters in data science. You'll set up your environment with Anaconda and Jupyter, then dive into data loading, preprocessing, and feature engineering. You'll also learn how to visualize time-dependent patterns, apply transformations, and use basic statistical techniques like moving averages and exponential smoothing. By the end of this section, you'll be well-prepared for building time-aware models.Section 2: Time-Series Forecasting ModelsIn this section, you'll move from theory to practice with key time-series forecasting models. Starting with naive models, you'll progress to Auto-Regression (AR), Moving Average (MA), and ARIMA. Learn how to split time-series data properly, validate predictions using walk-forward validation, and interpret autocorrelation using ACF and PACF plots. You'll wrap up with SARIMA, an advanced seasonal model, and apply it all in Python through hands-on coding.Section 3: Data Preprocessing for Linear RegressionBefore building regression models, you need clean and meaningful data. This section teaches you the essential preprocessing steps required for high-quality regression modeling. You'll work through exploratory data analysis, outlier detection, missing value imputation, seasonality handling, and correlation analysis. You'll also transform variables, create dummy variables, and prepare your dataset for modeling. Each concept is reinforced with Python demos to solidify your understanding.Section 4: Building & Evaluating Regression ModelsFinally, bring your data to life by building regression models in Python. You'll understand how to apply the Ordinary Least Squares (OLS) method, interpret coefficients, and evaluate model performance using R-Squared, F-statistics, and more. You'll build both simple and multiple linear regression models, including handling categorical variables. This section ensures you're confident not only in model creation but also in explaining the results effectively.Course Conclusion:Congratulations! You've now developed the core skills required to perform both time-series forecasting and regression modeling using Python. From building clean datasets to visualizing trends and predicting future values, you're ready to tackle real-world forecasting challenges in any domain. Your ability to transform data into insight will set you apart as a capable and confident data professional. Who this course is for Aspiring data scientists and analysts looking to master forecasting. Business intelligence professionals wanting to enhance reporting with prediction. Students in data science or machine learning programs. Python developers eager to apply coding skills to statistical forecasting. Anyone curious about time-series analysis, regression, or predictive modeling. 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Free Download Data Science - Bayesian Linear Regression in Python Published: 3/2025 Created by: Lazy Programmer Inc.,Lazy Programmer Team MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Expert | Genre: eLearning | Language: English | Duration: 30 Lectures ( 4h 47m ) | Size: 1.2 GB Fundamentals of Bayesian Machine Learning Parametric Models What you'll learn Understand Bayesian Linear Regression: Learn how Bayesian inference applies to linear regression using priors and posteriors. Derive and Implement the Model: Work through the math and code Bayesian Linear Regression from scratch in Python. Compare Bayesian vs. Frequentist Methods: Explore key differences and benefits of Bayesian over traditional linear regression. Apply Bayesian Regression to Data: Use probabilistic modeling to analyze real-world datasets and quantify uncertainty. Requirements Python coding: if/else, loops, lists, dicts, sets Numpy and Pandas coding: matrix and vector operations, loading a CSV file Basic math: calculus, linear algebra, probability Linear regression A bit of Bayesian statistics: just know about conjugate priors Description Welcome to Bayesian Linear Regression!I first started this course series on Bayesian Machine Learning many years ago, with a course on A/B Testing. I had always intended to expand the series (there's a lot to cover!) but kept getting pulled in other directions.Today, I am happy to announce that the Bayesian Machine Learning series is finally back on track!In the first course, a lot of students asked, "but where is the 'machine learning'?", since they thought of machine learning from the typical supervised/unsupervised parametric model paradigm. The A/B Testing course was never meant to look at such models, but that is exactly what this course is for.If you've studied machine learning before, then you know that linear regression is the first model everyone learns about. We will approach Bayesian Machine Learning the same way.Bayesian Linear Regression has many nice properties (easy transition from non-Bayesian Linear Regression, closed-form solutions, etc.). It is best and most efficient "first step" into the world of Bayesian Machine Learning.Also, let's not forget that Linear Regression (including the Bayesian variety) is simply very practical in the real-world. Bayesian Machine Learning can get very mathematical, so it's easy to lose sight of the big picture - the real-world applications. By exposing yourself to Bayesian ideas slowly, you won't be overwhelmed by the math. You'll always keep the application in mind.It should be stated however: Bayesian Machine Learning really is very mathematical. If you're looking for a scikit-learn-like experience, Bayesian Machine Learning is definitely too high-level for you. Most of the "work" involves algebraic manipulation. At the same time, if you can tough it out to the end, you will find the results really satisfying, and you will be awed by its elegance.Sidenote: If you made it through my Linear Regression and A/B Testing courses, then you'll do just fine.Suggested Prerequisites:Python coding: if/else, loops, lists, dicts, setsNumpy and Pandas coding: matrix and vector operations, loading a CSV fileBasic math: calculus, linear algebra, probabilityLinear regressionBayesian Machine Learning: A/B Testing in Python (know about conjugate priors) Who this course is for Data scientists and ML practitioners who want to master Bayesian Linear Regression from theory to code. Students and professionals curious about Bayesian methods and their real-world applications. ML enthusiasts who love understanding models mathematically and implementing them from scratch. Anyone with basic Python and probability skills looking to apply Bayesian regression in data science. Anyone who wants to go beyond Scikit-Learn and truly understand Bayesian Machine Learning. Homepage: https://www.udemy.com/course/data-science-bayesian-linear-regression-in-python/ Rapidgator https://rg.to/file/03bd0e225236df1f4840a5443974262f/lrxyy.Data.Science.Bayesian.Linear.Regression.in.Python.part1.rar.html https://rg.to/file/6a14217cfd0285e833ba4a4c1d4aaaab/lrxyy.Data.Science.Bayesian.Linear.Regression.in.Python.part2.rar.html Fikper Free Download https://fikper.com/P5YbDoyx8L/lrxyy.Data.Science.Bayesian.Linear.Regression.in.Python.part1.rar.html https://fikper.com/1g29XKJemc/lrxyy.Data.Science.Bayesian.Linear.Regression.in.Python.part2.rar.html No Password - Links are Interchangeable

Free Download Linear Regression Machine Learning Forecasts. Co2 Case Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.98 GB | Duration: 3h 0m Master Predictive Analytics in Pyrhon by Building Accurate CO2 Emission Forecasts with Linear Regression What you'll learn learn about linear regression learn machine learning learn neural net assess CO 2 performance Requirements No prerequisites Description Welcome to the comprehensive course, "Linear Regression Machine Learning Forecasts: CO₂ Case," designed to equip you with powerful forecasting skills using linear regression techniques. Throughout this course, you'll gain practical insights by forecasting CO₂ emissions up to the year 2050, utilizing historical emissions data. Our rigorous, clearly structured 10-step methodology ensures your forecasts are scientifically robust, statistically valid, and highly reliable, setting you apart in data-driven decision-making roles.The course is enriched with practical case studies covering multiple key regions, including India, China, the USA, the UK, France, the European Union, and the global average. By examining diverse economies, you'll master how regional differences and trends impact CO₂ emissions, enabling you to generate tailored, precise forecasts. Each forecasting exercise leverages real-world datasets and comprehensive statistical analyses, reinforcing your expertise and building confidence in applying linear regression techniques to environmental and economic scenarios.To guarantee the highest accuracy in your predictions, you'll learn to rigorously implement advanced statistical tests such as residual analysis, goodness-of-fit measures, and hypothesis testing. You'll discover how to validate your forecasts systematically, quantify uncertainties, and interpret results effectively. By the end of this course, you'll be adept at producing credible long-term CO₂ emission forecasts, capable of influencing policy, business strategies, and sustainable planning initiatives worldwide. engineers,ML practitioners,students,energy professionals Homepage: https://www.udemy.com/course/linear-regression-machine-learning-forecasts-co2-case/ Rapidgator https://rg.to/file/8026bc2ff39340fcaa3205eaace4be2a/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part1.rar.html https://rg.to/file/c3c8968c89959ea148046b05f46b920b/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part3.rar.html https://rg.to/file/f40e0ccf559f8efc7f9e3de2e8879183/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part2.rar.html Fikper Free Download https://fikper.com/P4tyOt0V6H/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part1.rar.html https://fikper.com/kxBr9HQPnT/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part2.rar.html https://fikper.com/vgT3lcKjC5/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part3.rar.html : No Password - Links are Interchangeable

pdf | 30.47 MB | English| Isbn:9781108898133 | Author: Eric W. Schoon, David Melamed, Ronald L. Breiger | Year: 2024 Description: https://fileaxa.com/0vejn274x9c3 https://ddownload.com/cos2hmpchik6 https://rapidgator.net/file/1d59e7f7870d6aac1f03f67f0ff713b9/ https://turbobit.net/8jb91kqplvps.html

Free Download MQL5 MACHINE LEARNING Linear Regression for Algo Trading Published 10/2024 Created by Latvian Trading Solutions MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 25 Lectures ( 3h 42m ) | Size: 2 GB A complete guide to developing linear regression based models for algorithmic trading in MQL5 What you'll learn: The concept of Linear Regression and its application in Algorithmic Trading How to Develop a Linear Regression model on a spread sheet How to code a Linear Regression model Indicator in MQL5 How to develop a Linear Regression Strategy and code an Expert advisor in MQL5 Requirements: Basics of MQL5 Description: Simple linear regression is a statistical method used to model the relationship between two variables: an independent variable (x) and a dependent variable (y). It assumes a linear relationship between the two variables and aims to find the best-fitting straight line that represents this relationship.The equation for a simple linear regression model is:y = ax + bWhere:y is the dependent variable (the variable we want to predict).x is the independent variable (the variable used to make predictions).a is the slope of the line, representing the rate of change of y with respect to x.b is the y-intercept, representing the value of y when x is zero.While simple linear regression is a statistical technique, it can also be considered as a machine learning algorithm. In machine learning, the goal is to build models that can learn from data and make predictions. Linear regression fits this framework because it learns the relationship between x and y from a given dataset and uses this learned relationship to make predictions for new data points. As neural networks learn the best non-linear relationships between data by finding the weights that best fit the data, linear regression aims to find the best values of a and b that best describe the linear relationship between variables.In this course, our aim is to build a linear regression model in mql5 that seeks to predict the closing prices of a currency pair given its specific bar index. We shall start by creating a linear regression model on a spread sheet to basically explain the calculations involved in creating a linear regression model. We shall then develop our linear regression model as an mql5 indicator by coding it using the mql5 programming language. After that, we shall develop our trading strategy as an mql5 expert advisor coded using the mql5 algorithmic trading language. We shall use the linear regression model we created as an indicator to analyze data and find patterns we can use to profit from the market. We shall base our trading logic on the fact that if price goes beyond one or two standard deviations from its predicted or expected price, it has to reverse and go back to its expected price. Hence our strategy will be a mean reversion type of strategy.For those that are still finding their way with MQL5, as long as you understand the basics of MQL5, this course is for you. We will patiently guide you through every step of the strategy development process and walk you through every line of code we shall craft. Hopefully, by the end of the course, you will have gained the necessary skills to code similar models and trading strategies and be able to appreciate how linear regression models can be an asset in developing your own trading ideas based on the ideas that shared in this course.So hit hard on that enroll button now and join me in this incredible journey of coding a linear regression model using the mql5 algorithmic trading language. Who this course is for: Anyone willing to learn about the applications of Linear Regression in Market analysis and timeseries forecasting Homepage https://www.udemy.com/course/mql5-machine-learning-linear-regression-for-algo-trading/ Rapidgator https://rg.to/file/630d36f1b4288747a471e52674d9d4dd/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part1.rar.html https://rg.to/file/7645de1e68ac2dcf4f2b70fa72700e70/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part2.rar.html https://rg.to/file/482c3aed89c0666e8b54135282d626d2/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part3.rar.html Fikper Free Download https://fikper.com/1HmmIr9mbW/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part1.rar https://fikper.com/R3AmHQhj0Z/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part2.rar https://fikper.com/4wDuaEE6tI/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part3.rar No Password - Links are Interchangeable

Free Download Healing Your Life With Past Life Regression Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 2.82 GB | Duration: 2h 15m Unlock the Secrets of Your Past Lives to Heal Emotional, Mental, and Physical Blocks What you'll learn Understand how past life traumas can block psychic abilities. Learn how to access past life memories through guided meditations. Discover how to release limiting beliefs and negative energy from past lives. Enhance your psychic power through energy clearing techniques. Gain practical tools to continue clearing blocks to your intuition. Discover your Unique Soul Journey as an Intuitive and a Healer and How You can Reclaim these Ancient Gifts Release Blockages from Past-lifetimes that Prevent You Earning Income as a Healer or Psychic Requirements Open Mind: A willingness to explore past life experiences and an openness to the healing process. Basic Understanding of Healing Practices: Familiarity with healing modalities (such as Reiki, energy healing, or other spiritual practices) is beneficial but not mandatory. Interest in Personal Growth: A desire to engage in self-discovery and personal transformation through the exploration of past lives. Journaling Supplies: A journal or notebook for documenting experiences, insights, and reflections throughout the course. Comfort with Introspection: A readiness to engage in self-reflection and potentially confront past traumas or blockages. Access to a Quiet Space: A calm and comfortable environment for practicing regression techniques and meditation. Basic Meditation Skills: Familiarity with meditation can be helpful, as some practices may involve meditative states. Internet Access: A device with internet connectivity to access course materials, discussions, and resources on the online platform. Description Welcome to the Course.Hi and welcome! I'm so glad you're here. In this course, we'll be exploring past life regression to help you heal and understand yourself better. We'll dive into the idea of past lives, how they affect your present life and ways you can access and heal from those memories.Overview of the Course.Here's what you can expect:Understanding Past Life Regression: You'll learn what past life regression is, how it works, and why it can help heal emotional, mental, and physical blocks.Techniques for Regression: I'll guide you through simple, safe techniques to access your past life memories.Healing and Growth: You'll discover how to release old wounds and patterns so you can live more fully in the present.Each lesson will build on the previous one, giving you practical tools to connect with your past lives and heal.Setting Intentions for Healing.Before we begin, let's set an intention for your healing journey. An intention is like a goal or focus for what you want to get out of this experience. Take a moment to think about what you want to heal or learn from your past life experiences. It could be something like:Healing an emotional pain or fearUnderstanding a current relationship or patternFinding peace and balance in your lifeSetting this intention will help guide you throughout the course and keep your healing process focused and meaningful. Overview Section 1: Introduction Lecture 1 Welcome to the course Lecture 2 Disclaimer Lecture 3 What is Past Life Therapy and how can it help you? Lecture 4 How you can use the course to get the maximum results Lecture 5 What is Karma Lecture 6 Manual Guide Section 2: How your past life can be preventing you Lecture 7 How your past life can be preventing you from accessing your psychic abilities Lecture 8 How your past life can be blocking your success as a healer or psychic Lecture 9 Common Vows from Past Lives That Limit Healers Lecture 10 The Role of the Higher Self Section 3: Key ingredients for powerful past life regression session Lecture 11 The Soul's Journey and Reincarnation Lecture 12 Master Lessons and Reincarnation Lecture 13 Healing Your Karma Lecture 14 Healing Karmic Contracts Lecture 15 Healing Karmic Debt with People Lecture 16 The Akashic Records Section 4: Preparation for your past life regression session Lecture 17 Keeping Safe During Past-Life Regressions Lecture 18 Tools and Rituals to Enhance Your Regression Lecture 19 Your Powerful Prayers for Psychic Protection before Past life regression Lecture 20 How to Prepare for Your Past-Life Regression Section 5: Your Guided Past Life Regression Lecture 21 Self-Guided Past Life Regression Lecture 22 Your Guided Past Life Regression Session With Akashic Records Lecture 23 Past Life Regression Meditation with counting Lecture 24 Journaling Your Experiences Lecture 25 Understanding Symbols, Places, and People in Your Past Lives Lecture 26 Common Themes: War, Wealth, Power, Love, and Loss Section 6: Your Ongoing Healing Journey Lecture 27 How to Integrate Past Life Knowledge Lecture 28 How Your Present Actions Influence Future Incarnations Lecture 29 Blockages that can arise during past life therapy Lecture 30 What can you do to enhance the positive healing of your regression? Section 7: Conclusion Lecture 31 Congratulations on completing your course. Lecture 32 Certificate Aspiring Healers: Individuals looking to enhance their healing abilities and learn techniques to clear past life blockages that may affect their practice.,Intuitive Practitioners: Those who want to deepen their psychic and intuitive skills through the exploration of past lives.,Personal Development Enthusiasts: Anyone interested in personal growth, self-discovery, and understanding how past experiences influence their current life.,Spiritual Seekers: Individuals on a spiritual journey who wish to connect with their soul's history and gain insights into their current life challenges.,Therapists and Counselors: Professionals in the healing arts who want to integrate past life regression techniques into their practice for deeper client healing.,Curious Souls: Anyone with an interest in exploring past lives, regardless of previous experience, who is open to the possibility of learning and growth through regression work. 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Free Download Dick Sutphen's Past Life Regression: Explorations Beyond Past Lives; Past-Life Talents and Abilities; Viewing Past-Lives (Audiobook) English | ASIN: B0D6PLDTVD | 2024 | 6 hours and 18 minutes | M4B@128 kbps | 351 MB Author: Dick Sutphen Narrator: Dick Sutphen Go Back in Time With Dick Sutphen's Past Life Regression. Dick Sutphen was America's foremost psychic researcher. A leading past-life therapist, he was a best-selling author and lecturer. A specialist in brain/mind technology, he instructed medical professionals in how to use his life-changing techniques. Now you can experience three of his classic subliminal hypnotic programs. Rapidgator https://rg.to/file/480dd9eb92af84f2342ea20df901a1e4/x0q2c.rar.html Fikper Free Download https://fikper.com/Ot8o2AZplw/x0q2c.rar.html Links are Interchangeable - No Password - Single Extraction

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Regression (2015) LIMITED BDRip X264 AMIABLE File Size: 456 MiB RELEAsEN0TEs General Complete name : ReGr15.LIMITED.BDRip.X264-AMiABLE.mkv Format : Matroska Format version : Version 4 / Version 2 File size : 456 MiB Duration : 1h 46mn Overall bit rate : 601 Kbps Video ID : 1 Format : AVC Format/Info : Advanced Video Codec Format profile : [email protected] Format settings, CABAC : Yes Format settings, ReFrames : 5 frames Codec ID : V_MPEG4/ISO/AVC Duration : 1h 46mn Width : 720 pixels Height : 302 pixels Display aspect ratio : 2.40:1 Frame rate mode : Constant Frame rate : 23.976 fps Color space : YUV Chroma subsampling : 4:2:0 Bit depth : 8 bits Scan type : Progressive Writing library : x264 core 148 r2638 7599210 Language : English Default : Yes Forced : No DURATION : 01:46:04 Audio ID : 2 Format : AAC Format/Info : Advanced Audio Codec Format profile : LC Codec ID : A_AAC Duration : 1h 46mn Channel count : 2 channels Channel positions : Front: L R Sampling rate : 48.0 KHz Compression mode : Lossy Delay relative to video : 9ms Language : English Default : Yes Forced : No Plot: A young girl is sexually abused by her father. Thus, begins the disturbing tale of a father and daughter torn apart, thrown into the center of a conspiracy that shocks the nation. http://www.imdb.com/title/tt3319920/ sCREENsH0Ts D0WNL0AD http://rapidgator.net/file/37ad1ef4e4c01940d3dc2317d631743f/ReGr15.LIMITED.BDRip.X264-AMiABLE.mkv.html http://uploaded.net/file/ogvapzxa/ReGr15.LIMITED.BDRip.X264-AMiABLE.mkv http://k2s.cc/file/561bd910fa7f8/ReGr15.LIMITED.BDRip.X264-AMiABLE.mkv http://nitroflare.com/view/B300B4A81018070/ReGr15.LIMITED.BDRip.X264-AMiABLE.mkv