Data Science with Python Learning Path

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.Next, we implement a neural network using Google's new TensorFlow library.You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.Another project at the end of the course shows you how you can use deep learning for facial

Welcome to the first Data Science and Machine Learning course with ChatGPT. Learn how to use ChatGPT to master complex Data Science and Machine Learning real-life projects in no time! Why is this a game-changing course?Real-world Data Science and Machine Learning projects require a solid background in advanced statistics and Data Analytics. And it would be best if you were a proficient Python Coder. Do you want to learn how to master complex Data Science projects without the need to study and master all the required basics (which takes dozens if not hundreds of hours)? Then this is the perfect course for you!  What you can do at the end of the course:At the end of this course, you will know and understand all strategies and techniques to master complex Data Science and Machine Learning projects with the help of ChatGPT! And you don´t have to be a Data Science or Python Coding expert! Use ChatGPT as your assistant and let ChatGPT do the hard work for you! Use ChatGPT forthe theoretical part Python codingevaluating and interpreting coding and ML results This course teaches prompting strategies and techniques and provides dozens of ChatGPT sample prompts toload, initially inspect, and understand unknown datasets clean and process raw datasets with Pandasmanipulate, aggregate, and visualize datasets with Pandas and matplotlibperform an extensive Explanatory Data Analysis (EDA) for complex datasetsuse advanced statistics, multiple regression analysis, and hypothesis testing to gain further insightsselect the most suitable Machine Learning Model for your prediction tasks (Model Selection)evaluate and interpret the performance of your Machine Learning models (Perfo

Obtain skills in one of the most sort after fields of this century In this course, you'll learn how to get started in data science. You don't need any prior knowledge in programming. We'll teach you the Python basics you need to get started.  Here are some of the items we will cover in this course The Data Science Process Python for Data Science Num Py for Numerical Computation Pandas for Data Manipulation Matplotlib for Visualization Seaborn for Beautiful Visuals Plotly for Interactive Visuals Introduction to Machine Learning Dask for Big Data Power BI Desktop Google Data Studio Association Rule Mining - Apriori Deep Learning Apache Spark for Handling Big Data For the machine learning section here are some items we'll cover :How Algorithms Work Advantages & Disadvantages of Various Algorithms Feature Importances Metrics Cross-Validation Fighting Overfitting Hyperparameter Tuning Handling Imbalanced Data TensorFlow & Keras Automated Machine Learning(AutoML)Natural Language Processing The course also contains exercises and solutions that will help you practice what you have learned. By enrolling in this course, you'll have lifetime access to the videos and Notebooks. Purchasing the course also comes with a 30-day money-back guarantee, so you can try it at no risk at all. Let's now add Data Science, Machine Learning, and Deep Learning to your CV. See you inside the course. The course also contains exercises and solutions that will help you practice what you have learned. By enrolling in this course

Questo corso sul Data Science con Python nasce per essere un percorso completo su come si è evoluta l'analisi dati negli ultimi anni a partire dall'algebra e dalla statistica classiche. L'obiettivo è accompagnare uno studente che ha qualche base di Python in un percorso attraverso le varie anime del Data Science. Cominceremo con un ripasso delle basi di Python, a partire dallo scaricamento e installazione, all'impostazione dell'ambiente di lavoro, passando per le strutture, la creazione di funzioni, l'uso degli operatori e di alcune funzioni importanti. Passeremo poi a vedere come manipolare e gestire un dataset, estrarne dei casi oppure delle variabili, generare dei dataset casuali, calcolare delle misure statistiche di base, creare grafici con i pacchetti Matplotlib e Seaborn.Nelle sezioni successive cominciamo a entrare nel cuore del Data Science con Python, a cominciare dal preprocessing: vediamo infatti come ripulire e normalizzare un dataset, e come gestire i dati mancanti. La sezione successiva ci permette di cominciare a impostare dei modelli di machine learning con Python: vedremo tutti gli algoritmi più comuni, sia supervisionati che non supervisionati, come la regressione, semplice, multipla e logistica, il k-nearest neighbors, il Support Vector Machines, il Naive Bayes, gli alberi di decisione e il clustering. Passeremo poi ai più comuni metodi ensemble, come il Random Forest, il Bagging e il Boosting, e all'analisi del linguaggio naturale e al suo utilizzo nel machine learning per la catalogazione dei testi.

Let me tell you my story. I graduated with my Ph. D. in computational nano-electronics but I have been working as a data scientist in most of my career. My undergrad and graduate major was in electrical engineering (EE) and minor in Physics. After first year of my job in Intel as a "yield analysis engineer" (now they changed the title to Data Scientist), I literally broke into data science by taking plenty of online classes. I took numerous interviews, completed tons of projects and finally I broke into data science. I consider this as one of very important achievement in my life. Without having a degree in computer science (CS) or a statistics I got my second job as a Data Scientist. Since then I have been working as a Data Scientist. If I can break into data science without a CS or Stat degree I think you can do it too! In this class allow me sharing my journey towards data science and let me help you breaking into data science. Of course it is not fair to say that after taking one course you will be a data scientist. However we need to start some where. A good start and a good companion can take us further.We will definitely discuss Python, Pandas, Num Py, Sk-learn and all other most popular libraries out there. In this course we will also try to de-mystify important complex concepts of machine learning. Most of the lectures will be accompanied by code and practical examples. I will also use “white board” to explain the concepts which cannot be explained otherwise. A good data scientist should use white board for ideation, problem solving. I also want to mention that this course is not designed towards explaining all the math needed to “practice” machine learning. Also, I will be continuously upgrading the contents of this course to make sure that all the latest tools and libraries are taught here. Stay tuned!

Are you preparing for a career in Data Science or Machine Learning? Mastering the technical skills is crucial, but excelling in interviews requires more than just technical knowledge. Our course, "Data Science and Machine Learning: Top Interview Questions," equips you with the essential insights and strategies to ace your interviews with confidence.In this comprehensive course, we delve into the core concepts and practical techniques that are frequently tested in interviews for data science and machine learning roles. From feature engineering and model evaluation to unsupervised learning and ensemble methods, we cover a wide range of topics essential for success in interviews.Through a series of curated hands-on exercises, you will gain proficiency in:Crafting effective feature engineering and selection strategies to optimize model performance.Understanding various performance metrics and validation techniques to assess model accuracy and generalization.Exploring unsupervised learning algorithms and ensemble methods for tackling complex data problems.Leveraging cross-validation strategies to ensure robustness and reliability of your machine learning models.Moreover, our course goes beyond technical skills to offer invaluable interview insights, tips, and best practices. You'll learn how to articulate your thought process, communicate your solutions effectively, and tackle interview questions with clarity and confidence.Whether you're a seasoned professional or a beginner in the field, "Data Science and Machine Learning: Top Interview Questions" provides you with the knowledge and skills needed to excel in your next interview and kickstart your career in data science and machine learning. Enroll now and take the next step towards your dream job!

This comprehensive course, Machine Learning & Python Data Science for Business and AI, is designed to transform you from a data novice into a proficient practitioner. Whether you're a business professional looking to leverage data driven insights, a student eager to enter the field of AI, or a developer aiming to add powerful new skills to your toolkit, this course provides a clear, practical, and project based path to mastery.I'll skip the heavy, academic theory and dive straight into the practical application of machine learning. You'll learn by doing, building a portfolio of real world projects that are immediately applicable to business and AI challenges. Our focus is on problem-solving using the most popular and powerful tools in the industry: Python, Pandas, Num Py, Scikit-Learn, and Matplotlib.By the end of this course, you'll not only understand the core concepts of machine learning but also be able to implement them with confidence. You'll gain a deep understanding of how to collect, clean, and analyze data to make accurate predictions and informed decisions.Why This Course?In today’s data driven world, organizations rely on data science and AI to stay competitive. Understanding how to harness data effectively can help businesses predict trends, optimize operations, and make smarter decisions. This course is specifically tailored to bridge the gap between technical machine learning concepts and practical business applications.What You Will Learn Start with Python fundamentals and learn how to write clean, efficient code for data analysis.Learn how to process, clean, and visualize data using popular Python libraries like Pandas, Num Py, and Matplotlib to extract meaningful insights.Understand core statistical concepts that form the foundation of machine learning, including probability, distributions, and hypoth

85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). So naturally, 85% of the interview questions come from these topics as well.This concise course, created by UNP, focuses on what matter most. This course will help you create a solid foundation of the essential topics of data science. With this solid foundation, you will go a long way, understand any method easily, and create your own predictive analytics models.At the end of this course, you will be able to:independently build machine learning and predictive analytics modelsconfidently appear for exploratory data analysis, foundational data science, python interviews demonstrate mastery in exploratory data science and pythondemonstrate mastery in logistic and linear regression, the workhorses of data science This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications. Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. In addition, concepts of overfitting, regularization etc., are discussed in detail. These fundamental understandings are crucial as these can be applied to almost every machine learning method. This course also provides an understanding of the industry standards, best practices for formulating, applying and maintaining data-driven solutions. It starts with a basic explanation of Machine Learning concepts and how to set up your environment. Next, data wrangling and EDA with Pandas are discussed with hands-on

A warm welcome to the Data Science, Artificial Intelligence, and Machine Learning with Python course by Uplatz.Python is a high-level, interpreted programming language that is widely used for various applications, ranging from web development to data analysis, artificial intelligence, automation, and more. It was created by Guido van Rossum and first released in 1991. Python emphasizes readability and simplicity, making it an excellent choice for both beginners and experienced developers.Data Science Data Science is an interdisciplinary field focused on extracting knowledge and insights from structured and unstructured data. It involves various techniques from statistics, computer science, and information theory to analyze and interpret complex data.Key Components:Data Collection: Gathering data from various sources.Data Cleaning: Preparing data for analysis by handling missing values, outliers, etc.Data Exploration: Analyzing data to understand its structure and characteristics.Data Analysis: Applying statistical and machine learning techniques to extract insights.Data Visualization: Presenting data in a visual context to make the analysis results understandable.Python in Data Science Python is widely used in Data Science because of its simplicity and the availability of powerful libraries:Pandas: For data manipulation and analysis.Num Py: For numerical computations.Matplotlib and Seaborn: For data visualization.Sci Py: For advanced statistical operations.Jupyter Notebooks: For interactive data analysis and sharing code and re

Welcome to my " Complete Python for Data Science & Machine Learning from A-Z " course.Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn't specialized for any specific problems.Python instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn.Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.Do you want to learn one of the employer’s most requested skills? If you think so, you are at the right place. Python, machine learning, Django, python programming, machine learning python, python Bootcamp, coding, data science, data analysis, programming languages.We've designed for you "Complete Python for Data Science & Machine Learning from A-Z” a straightforward course for the Complete Python programming langu

Hello there,Welcome to the " Complete Data Science & Machine Learning A-Z with Python " Course Machine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, Kaggle Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the i Phone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data science Pandas is an open source Python package that is most widely used for

Hello there,Welcome to the “Complete Machine Learning & Data Science with Python | A-Z” course Use Scikit, learn Num Py, Pandas, Matplotlib, Seaborn, and dive into machine learning A-Z with Python and Data Science Machine learning isn’t just useful for predictive texting or smartphone voice recognition Machine learning is constantly being applied to new industries and new problems Whether you’re a marketer, video game designer, or programmer, my course on OAK Academy here to help you apply machine learning to your work Complete machine learning & data science with python | a-z, machine learning a-z, Complete machine learning & data science with python, complete machine learning and data science with python a-z, machine learning using python, complete machine learning and data science, machine learning, complete machine learning, data science It’s hard to imagine our lives without machine learning Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the i Phone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models Python, machine learning, django, python programming, machine learning python, python for beginners, data science Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn Python's simple syntax is especially suited for desktop, web, and business applications Python's design philosophy emphasizes readability and usability Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization The core programming language is quite small

Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more? Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.We are going to execute following real-life projects,Kaggle Bike Demand Prediction from Kaggle competition Automation of the Loan Approval process The famous IRIS Classification Adult Income Predictions from US Census Dataset Bank Telemarketing Predictions Breast Cancer Predictions Predict Diabetes using Prima Indians Diabetes Dataset Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others. As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning? Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,Understanding of the overall landscape of Data Science and Machine Learning Different types of Data Analy

Greetings, I am so excited to learn that you have started your path to becoming a Data Scientist  with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the impact of your work around your, is not is amazing?This is one of the most comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making. Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with  detailed code notebooks for every lecture. The course also includes practice exercises on real data for each topic you cover, because the goal is "Learn by Doing"! For your satisfaction, I would like to mention few topics that we will be learning in this course:Basis Python programming for Data Science Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter Num PyArrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal Functions Pandas Pandas Data Structures - Series, DataF

Data Science is an interdisciplinary field that leverages statistical analysis, data exploration, and machine learning techniques to derive knowledge and meaningful insights from data.Definition of Data Science:Data Science encompasses various processes, including data acquisition, thorough analysis, and informed decision-making.Data Science involves the identification and interpretation of data patterns to make predictive assessments.Through the application of Data Science, organizations can achieve:1. Improved decision-making processes, enabling the selection between alternatives (A or B) with greater confidence.2. Predictive analysis that anticipates future events or trends, aiding in proactive planning.3. Discovery of hidden patterns and valuable information within datasets, leading to actionable insights.Applications of Data Science:Data Science finds extensive application across diverse industries such as banking, consultancy, healthcare, and manufacturing.Examples of Data Science applications include:1. Optimizing route planning for shipping purposes.2. Anticipating potential delays in flights, ships, trains, etc., through predictive analysis.3. Crafting personalized promotional offers for customers.4. Determining the best time to deliver goods for maximum efficiency.5. Forecasting future revenue for a company.6. Analyzing the health benefits of specific training regimens.7. Predicting election outcomes.Data Science Integration in Business:Data Science can be seamlessly integrated into various facets of business operations where relevant data is available, including:1. Consumer goods industries for market analysis and consumer behavior prediction.2. Stock markets for financial analysis and forecasting.3. Industrial settings for process optimization and quality control.4. Political scenarios for opinion

Science des Données et Apprentissage Automatique : Compréhension Théorique Approfondie La science des données (Data Science) est un domaine vaste et fascinant, tandis que l'apprentissage automatique (Machine Learning) est une branche passionnante de la Data Science. Ce cours de deux heures offre une exploration détaillée de ces domaines pour ceux qui souhaitent comprendre leur fonctionnement.Ce cours se distingue par son approche visuelle et simplifiée, qui démystifie les concepts et algorithmes de l'apprentissage automatique sans se perdre dans les détails mathématiques. Il se concentre sur la théorie, offrant une base solide pour quiconque souhaite exceller dans le domaine de la science des données.Les sections de ce cours sont interconnectées et progressives, formant un ensemble cohérent qui facilite l'apprentissage. Chaque section se construit sur les précédentes, vous permettant d'explorer des concepts de plus en plus avancés au fur et à mesure de votre progression.Ce cours aborde les compétences les plus recherchées dans le monde réel de la science des données et de l'apprentissage automatique. Il est conçu pour être simple, facile à comprendre, et descriptif, vous permettant de progresser rapidement.Rejoignez ce cours pour démystifier la science des données et l'apprentissage automatique. C'est une opportunité unique d'acquérir des connaissances solides dans un format accessible et inspirant !Contenu du cours :Après avoir suivi ce cours avec succès, vous serez en mesure de :Comprendre les concepts, principes et théories de la science des données et de l'apprentissage automatique Appréhender la méthodologie de la science des données et de l'apprentissage automatiqueÉvaluer les avantages et les inconvénients des différents algorithmes d'apprentissage automatiqueSélectionner l'algorithme d'apprentissage automat

¿Te suenan términos como *Machine Learning* o *Data Scientist*? ¿Te has preguntado para qué se utilizan estas técnicas y por qué las empresas están dispuestas a pagar entre 120.000 y 200.000 dólares al año a un científico de datos?Este curso está diseñado para resolver todas tus dudas y brindarte una formación integral en Data Science. Juan Gabriel Gomila, un profesional reconocido en el campo del Data Science, te guiará a lo largo del curso, compartiendo su vasto conocimiento y ayudándote a desmitificar la teoría matemática detrás de los algoritmos de Machine Learning. Aprenderás a dominar las librerías de Python que son esenciales en esta área, convirtiéndote en un experto en la materia.A lo largo del curso, abordarás conceptos y algoritmos clave del Machine Learning, de manera progresiva y detallada. Cada sección te proporcionará nuevas habilidades que te permitirán comprender y aplicar los principios del Data Science, una disciplina no solo fascinante, sino también altamente lucrativa.Además, este curso mantiene el estilo característico y ameno de Juan Gabriel Gomila, lo que hará que disfrutes aprendiendo técnicas de Machine Learning con Python.El curso incluye ejercicios prácticos y datasets basados en ejemplos del mundo real, lo que te permitirá no solo aprender la teoría, sino también aplicarla en la creación de tus propios modelos de Machine Learning. Además, tendrás acceso a un repositorio en Git Hub con todo el código fuente en Python, listo para descargar y usar en tus proyectos.¡No esperes más! Únete a este curso y comienza a formarte en Machine Learning con el programa más completo y práctico del mercado en español.

Why study data science?Companies have a problem: they collect and store huge amounts of data on a daily basis. The problem is that they don't have the tools and capabilities to extract knowledge and make decisions from that data. But that is changing. For some years now, the demand for data scientists has grown exponentially. So much so, that the number of people with these skills is not enough to fill all the job openings. A basic search on Glassdoor or Indeed will reveal to you why data scientist salaries have grown so much in recent years.Why this course?Almost every course out there is either too theoretical or too practical. University courses don't usually develop the skills needed to tackle data science problems from scratch, nor do they teach you how to use the necessary software fluently. On the other hand, many online courses and bootcamps teach you how to use these techniques without getting a deep understanding of them, going through the theory superficially.Our course combines the best of each method. On the one hand, we will look at where these methods come from and why they are used, understanding why they work the way they do. On the other, we will program these methods from scratch, using the most popular data science and machine learning libraries in Python. Only when you have understood exactly how each algorithm works, we will learn how to use them with advanced Python libraries.Course content Introduction to machine learning and data science.Simple linear regression. We will learn how to study the relationship between different phenomena.Multiple linear regression. We will create models with more than one variable to study the behavior of a variable of interest.Lasso regression. Advanced version of multiple linear regression with the ability to filter the most useful variables.Ridge regression. A