Machine Learning with Python

This course provides practical skills in using Python and the Scikit-Learn library for machine learning, with a focus on supervised learning.

A book that provides a comprehensive guide to machine learning using two popular Python libraries, covering a wide range of supervised learning models.

Master Deep Learning with Python for AI Excellence Course Description: This meticulously crafted course is designed to empower you with comprehensive knowledge and practical skills to thrive in the world of artificial intelligence.Immerse yourself in engaging lectures and hands-on lab sessions that cover fundamental concepts, cutting-edge methodologies, and real-world applications of deep learning. Gain expertise in essential Python libraries, machine learning algorithms, and advanced techniques, setting a solid foundation for your AI career.Course Highlights:In-Demand Skills: Acquire the highly sought-after skills demanded by today's AI-centric job market, opening doors to data science, machine learning, and AI development roles.Hands-On Learning: Learn by doing! Our interactive lab sessions ensure you gain practical experience, from data preprocessing to model evaluation, making you a proficient deep learning practitioner.Comprehensive Curriculum: From foundational Python libraries like Pandas and Num Py to cutting-edge neural network architectures like CNNs and RNNs, this course covers it all. Explore linear regression, logistic regression, decision trees, clustering, anomaly detection, and more.Expert Guidance: Our experienced instructors are committed to your success. Receive expert guidance, personalized feedback, and valuable insights to accelerate your learning journey.Project-Based Learning: Strengthen your skills with real-world projects that showcase your <

In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course Overview Our course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and Technologies To equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python . Whether you're manipulating data with Pandas, performing numerical operations with Num Py, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning.&nbsp; You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources.&nbsp; You'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best Practices Navigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your ability to work efficiently and maintain project dependencies.Data

Are you ready to start your journey into Python programming and machine learning? This course is your ultimate guide to becoming a skilled Python programmer and mastering machine learning from scratch. Whether you're a beginner or have some experience, this course will take you from zero to hero with a practical, hands-on approach.What You’ll Learn:Python Fundamentals: Master variables, data types, control flow, functions, and libraries.Data Preprocessing: Learn to clean, scale, and transform data for machine learning models.Machine Learning Basics: Build regression and classification models with real-world datasets.Advanced ML Techniques: Explore clustering, dimensionality reduction, and ensemble learning.Real-World Projects: Solve practical problems like predicting housing prices and customer segmentation.Why Take This Course?This course is designed for learners who want to gain practical programming and machine learning skills. You’ll work on real-world projects, gaining confidence to apply these skills in various industries. By the end of the course, you’ll have a strong portfolio and the ability to build your own machine learning models.Who This Course is For:Complete beginners looking to learn Python and machine learning.Professionals aiming to enhance their data science skills.Students and developers curious about applying machine learning in real-world scenarios.Join now to kickstart your career in data science and AI!

Machine learning brings together computer science and statistics to build smart, efficient models. Using powerful techniques offered by machine learning, you’ll tackle data-driven problems. The effective blend of Machine Learning with Python, Scikit-Learn, and TensorFlow, helps in implementing solutions to real-world problems as well as automating analytical model. This comprehensive 3-in-1 course is your one-stop solution in mastering machine learning algorithms and their implementation. Learn the fundamentals of machine learning and build your own intelligent applications. Explore popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Contents and Overview This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python, Scikit-Learn and TensorFlow. The first course, Step-by-Step Machine Learning with Python, covers easy-to-follow examples that get you up and running with machine learning. In this course, you’ll learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. You’ll build your own models from scratch. The second course, Machine Learning with Scikit-Learn, covers effective learning algorithms to real-world problems using Scikit-Learn. You’ll build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use Scikit-Learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance. The third cou

Becoming Data Science professional (Data Scientist) is a long journey and need guidance from seasoned Data Science professional (Chief Data Scientist). We are trying to manage the journey such a way that you learn right skills and in the right way. The whole concepts of the course are to make you ready for Data Science projects, mainly in Machine learning and AI projects. You will learn1. Foundation of Machine learning2. Supervised Machine learning - Regression3. Supervised Machine learning - Classifications4. Unsupervised Machine learning (Clustering, KNN, PCA)5. Text Analytics6. Time Series

This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts, Numpy Library , Scipy Library , Pandas Library, Matplotlib Library, Seaborn Library, Plotlypy Library, Introduction to Data Science and steps to start Project in Data Science, Case Studies of Data Science and Machine Learning Algorithms such as Linear, Logistic, SVM, NLP This is best course for any one who wants to start career in data science. with machine Learning.Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered. Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered. This Course will design to understand Machine Learning Algorithms with case Studies using Scikit-Learn Library. The Machine Learning Algorithms&nbsp; such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies

Ready to master machine learning in Python and launch your career in data science? This hands-on, comprehensive course is the definitive guide to becoming a skilled practitioner, taking you from the fundamentals of Scikit-Learn to building powerful, real-world AI models.You'll gain a deep understanding of Scikit-Learn, Python's most essential and widely used machine learning library. By focusing on practical application, you will not only learn the algorithms but also how to implement the full data science workflow—a critical skill for employers.Master the Complete Data Science and Machine Learning Workflow This masterclass will teach you to:Prepare and Preprocess complex, real-world datasets using Python (Pandas &amp; Num Py) and the integrated tools within Scikit-Learn.Build Powerful Models using core Machine Learning algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SV Ms).Optimize Performance with advanced techniques like Regularization, Cross-Validation, and Principal Component Analysis (PCA) for Dimensionality Reduction.Apply both Supervised and Unsupervised Learning to solve diverse business problems in data science.Understand the AI Landscape by covering the basics of Neural Networks and their role in Deep Learning.Work through short coding exercises and large, project-style assignments, mirroring the daily work of a professional data scientist.Why Learn Machine Learning with Us?We're

This course focuses on the fundamentals of Data Science, Machine learning, and deep learning in the beginning and with the passage of time, the content and lectures become advanced and more practical. But before everything, the introduction of python is discussed. Python is one of the fastest-growing programming languages and if we specifically look from the perspective of Data Science, Machine learning and deep learning, there is no other choice then “python” as a programming language.First of all, there is a crash course on python for those who are not very good with python and then there is an exercise for python that is supposed to be solved by you but if you feel any difficulty in solving the exercise, the solution is also provided.Then we moved on towards the Data Science and we start from data parsing using Scrapy then the data visualizations by using several libraries of python and finally we end up learning different data preprocessing techniques. And in the end, there is a complete project that we’ll do together.After that, we’ll be learning a few classical and a few advanced machine learning algorithms. Some of them will be implemented from scratch and the others will be implemented by using the builtin libraries of python. At the end of every algorithm, there will be a mini-project.Finally, Deep learning will be discussed, the basic structure of an artificial neural network and it’s the implementation in TensorFlow followed by a complete deep learning-based project. And in the end, some hyperparameter tuning techniques will be discussed that’ll improve the performance of the model.About The Instructor:Below is an introduction to Mr. Sajjad Mustafa, the instructor of this course.He an expert in Web

Learn Python for Data Science &amp; Machine Learning from A-ZIn this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.We'll go over some of the best and most important Python libraries for data science such as Num Py, Pandas, and Matplotlib +Num Py —&nbsp; A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.Num Py and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.This Machine Learning with Python course dives into the basics of machine learning using Python. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!Python coding experience

Unlock the power of Artificial Intelligence, Python, Machine Learning, Data Science, and Big Data Analytics in this comprehensive, hands-on course. Whether you’re a beginner or an aspiring data professional, this course equips you with the practical skills and knowledge to solve real-world problems using cutting-edge technologies.What You Will Learn:Fundamentals of Python programming for AI and data analysis Building and deploying Machine Learning models from scratch Exploring Data Science techniques, including data cleaning, visualization, and analysis Working with Big Data Analytics tools to handle massive datasets Implementing AI solutions for real-world projects and business applications Understanding key concepts in Deep Learning, Neural Networks, and Predictive Analytics Who This Course is For:Anyone passionate about leveraging AI and Big Data to make smarter decisions Why Choose This Course:Hands-on projects and real-world examples Learn from beginner-friendly to advanced concepts in a structured way Focused on practical applications that can boost your career or business Certificate after course complete By the end of this course, you will have the confidence and skills to design and implement AI-powered solutions, build machine learning models, analyze complex datasets, and tackle big data challenges.Start your journey to becoming an AI, Machine Learning, and Data Science expert today!

Hello there,Welcome to the “Machine Learning Python with Theoretically for Data Science” course.Machine Learning with Python in detail both practically and theoretically with machine learning project for data science Machine learning courses teach you the technology and concepts behind predictive text, virtual assistants, and artificial intelligence. 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 training helps you stay ahead of new trends, technologies, and applications in this field.Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.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. machine learning, python, data science, machine learning python, python data science, machine learning a-z, python for data science and machine learning bootcamp, python for data science, complete machine learning, machine learning projects,Use Scikit-Learn, Num Py, Pandas, Matpl

Este curso pretende ser una introducción a las técnicas más relevantes de Machine Learning y mostrar ejemplos de aplicación de estas técnicas. Que sirva para conocer qué técnicas existen, en qué se fundamentan y sobre qué tipos de problemas pueden aplicarse. El enfoque será teórico-práctico y se hará uso del lenguaje de programación Python y del toolkit Scikit-Learn. Se recomienda a los alumnos instalarse ANACONDA en su plataforma habitual. ANACONDA incluye Python, Scikit-Learn y Matplotlib. La versión de python que utilizaremos será la 3.6.También veremos pyspark como plataforma de desarrollo de aplicaciones distribuídas Entre los principales objetivos podemos destacar:Introducir los conceptos de ciencias de datos y machine learning.Introducir las principales librerías que podemos encontrar en python para aplicar técnicas de machine learning a los datos.Introducir las principales librerías que podemos encontrar en python para tratamiento y visualización de datos Dar a conocer los pasos para construir un modelo de machine learning, desde la adquisición de datos,pasando por la generación de funciones, hasta la selección de modelos.Dar a conocer los principales algoritmos para resolver problemas de machine learning.Introducir Scikit-Learn como herramienta para resolver problemas de machine learning.Introducir pyspark como herramienta para aplicar técnicas de big data y map-reduce a los datos.Conocer y aplicar algoritmos de machine learning con pyspark.Introducir los sistemas de recomendación basados en contenidos

In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as:computer visionnatural language processingfinancial analysishealthcaregenomics Why should you take this course?&nbsp;Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You'll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You'll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!Thank you for reading and I hope to see you soon!Suggested Prerequisites:Decent Python programming skill Comfortable with data science libraries like Numpy and Matplotlib For the advanced section, probability knowledge is requiredWHAT&nbsp;ORDER&nbsp;SHOULD&nbsp;I&nbsp;TAKE&nbsp;YOUR&nbsp;COURSES&nbsp;IN?Check out the lecture "Machine Learning and AI&nbsp;Prerequisite Roadmap" (available in the FAQ of any of my courses, including my free course)UNIQUE&nbsp;FEATURES Every line of code explained in detail - email me any time if you disagree Less than 24 hour

¿Por qué estudiar ciencia de datos?Las empresas tienen un problema: recolectan y guardan enormes cantidades de datos en su día a día. El problema es que no tienen las herramientas y capacidades para extraer conocimiento y tomar decisiones a partir de esos datos. Pero eso está cambiando. Desde hace algunos años, la demanda de científicos de datos ha crecido exponencialmente. Tanto es así, que el número de personas con estas habilidades no es suficiente para cubrir todas las vacantes que hay. Una búsqueda básica en Glassdoor o Indeed te revelará por qué los salarios de los científicos de datos han crecido tanto en los últimos años.¿Por qué este curso?Casi todos los cursos que existen son demasiado teóricos o demasiado prácticos. Los cursos de universidad no suelen desarrollar las habilidades necesarias para enfrentarse a problemas de ciencia de datos desde cero, ni te enseñan a utilizar el software necesario de forma fluida. Por otra parte, muchos cursos y bootcamps online enseñan a utilizar estas técnicas sin llegar a entenderlas en profundidad, pasando por la teoría de forma superficial.Nuestro curso combina lo mejor de cada método. Por una parte, veremos de dónde surgen y por qué se utilizan estos métodos, entendiendo por qué funcionan de la forma que lo hacen. Por otra, vamos a programar estos métodos desde cero, utilizando las librerías más populares de la ciencia de datos y el machine learning en Python. Solo cuando hayas entendido exactamente cómo funciona cada algoritmo, aprenderemos a usarlos con las librerías avanzadas de Python.Contenido del curso Introducción al machine learning y a la ciencia de datos.Regresión lineal simple. Aprenderemos a estudiar la relación entre distintos fenómenos.Regresión lineal multiple. Crearemos modelos de más de una variable para estudiar el comportamiento de una variable de interés.Regresión