Intermediate AWS

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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

In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud).Data science can be defined as a blend of mathematics, business acumen, tools, algorithms, and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.In data science, one deals with both structured and unstructured data. The algorithms also involve predictive analytics. Thus, data science is all about the present and future. That is, finding out the trends based on historical data which can be useful for present decisions, and finding patterns that can be modeled and can be used for predictions to see what things may look like in the future.Data Science is an amalgamation of Statistics, Tools, and Business knowledge. So, it becomes imperative for a Data Scientist to have good knowledge and understanding of these.With the amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for companies. To make the most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT or Bank. All are looking for Data Scientists. This has led to a huge demand for Data Scientists all over the globe. With the kind of salary that a company has to offer and IBM is declaring it as the trending job of the 21st century, it is a lucrative job for many. This field is such that anyone from any background can make a career as a Data Scientist.In This Course, We Are Going To Work On 50 Real World Projects Listed Below:Project-1: Pan Card Tempering Detector App -Deploy On Heroku

This course is complete guide of AWS Sage Maker wherein student will learn how to build, deploy Sage Maker models by brining on-premises docker container and integrate it to Sage Maker. Course will also do deep drive on how to bring your own algorithms in AWS Sage Maker Environment. Course will also explain how to use pre-built optimized Sage Maker Algorithm.Course will also do deep drive how to create pipeline and workflow so model could be retrained and scheduled automatically.This course covers all aspect of AWS Certified Machine Learning Specialty (MLS-C01)This course will give you fair ideas of how to build Transformer framework in Keras for multi class classification use cases. Another way of solving multi class classification by using pre-trained model like Bert .Both the Deep learning model later encapsulated in Docker in local machine and then later push back to AWS ECR repository.This course offers:AWS Certified Machine Learning Specialty (MLS-C01)What is Sage Maker and why it is required Sage Maker Machine Learning lifecycle Sage Maker Architecture Sage Maker training techniques:Bring your own docker container from on premise to Sage Maker Bring your own algorithms from local machine to Sage Maker Sage Maker Pre built Algorithm Sage Maker Pipeline development Schedule the Sage Maker Training notebook More than 5 hour course are provided which helps beginners to excel in Sage Maker and will be well versed with build, train and deploy the models in Sage Maker

Solve Business Problems Using Data Science Practically. Learn To Build &amp; Deploy Artificial Intelligence, Machine Learning, Data Science , Auto Ml, Deep Learning, Natural Language Processing (NLP) Web Applications Projects With Python (Flask, Django, Heruko, Streamlit Cloud).How much does a Data Scientist make in the United States?The national average salary for a Data Scientist is US$1,20,718 per year in the United States, 2.8k salaries reported, updated on July 15, 2021 (source: glassdoor)Salaries by Company, Role, Average Base Salary in (USD)Facebook Data Scientist makes USD 1,36,000/yr. Analyzed from 1,014 salaries.Amazon Data Scientist makes USD 1,25,704/yr. Analyzed from 307 salaries.Apple Data Scientist makes USD 1,53,885/yr. Analyzed from 147 salaries.Google Data Scientist makes USD 1,48,316/yr. Analyzed from 252 salaries.Quora, Inc. Data Scientist makes USD 1,22,875/yr. Analyzed from 509 salaries.Oracle Data Scientist makes USD 1,48,396/yr. Analyzed from 458 salaries.IBM Data Scientist makes USD 1,32,662/yr. Analyzed from 388 salaries.Microsoft Data Scientist makes USD 1,33,810/yr. Analyzed from 205 salaries.Walmart Data Scientist makes USD 1,08,937/yr. Analyzed 187 salaries.Cisco Systems Data Scientist makes USD 1,57,228/yr. Analyzed from 184 salaries.Uber Data Scientist makes USD 1,43,661/yr. Analyzed from 151 salaries.Intel Corporation Data Scientist makes USD 1,25,930/yr. Analyzed from 131 salaries.Airbnb Data Scientist makes USD 1,80,569/yr. Analyzed from 122 salaries.Adobe Data Scientist makes USD 1,39,074/yr. Analyzed from 109 salaries.<

Do you want to become an AWS Machine Learning Engineer Using Sage Maker in 30 days?Do you want to build super-powerful production-level Machine Learning (ML) applications in AWS but don’t know where to start?Are you an absolute beginner and want to break into AI, ML, and Cloud Computing and looking for a course that includes everything you need?Are you an aspiring entrepreneur who wants to maximize business revenues and reduce costs with ML but don’t know how to get there quickly and efficiently?Do you want to leverage ChatGPT&nbsp;as a programmer to automate your coding tasks?If the answer is yes to any of these questions, then this course is for you!Machine Learning is the future one of the top tech fields to be in right now! ML and AI will change our lives in the same way electricity did 100 years ago. ML is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospectsAWS is the one of the most widely used cloud computing platforms in the world and several companies depend on AWS for their cloud computing purposes. AWS Sage Maker is a fully managed service offered by AWS that allows data scientist and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.This course is unique and exceptional in many ways, it includes several practice opportunities, quizzes, and final capstone projects. In this course, students will learn how to create production-level ML models using AWS. The course is divided into 8 main sections as follows:Section 1 (Days 1 – 3): we will learn the following: (1) Start with an AWS and Machine Learning essentials “starter pack” that includes key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) an

Description Take the next step in your cloud-powered AI and machine learning journey! Whether you're an aspiring data scientist, ML engineer, developer, or business leader, this course will equip you with the skills to harness AWS for scalable, real-world data science and machine learning solutions. Learn how services like Sage Maker, Glue, Redshift, and Quick Sight are transforming industries through data-driven intelligence, automation, and predictive analytics.Guided by hands-on projects and real-world use cases, you will:• Master foundational data science workflows and machine learning principles using AWS cloud services.• Gain hands-on experience managing data with S3, Redshift, Glue, and building models with AWS Sage Maker.• Learn to train, optimize, and deploy ML models at scale using advanced tools like AutoML, hyperparameter tuning, and deep learning frameworks.• Explore industry applications in e-commerce, finance, healthcare, and manufacturing using AWS AI/ML solutions.• Understand best practices for cost management, security, and automation in cloud-based data science projects.• Position yourself for a competitive advantage by building in-demand skills at the intersection of cloud computing, AI, and machine learning.The Frameworks of the Course· Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises— designed to help you deeply understand how to leverage AWS for data science and machine learning applications.· The course includes industry-specific case studies, cloud-native tools, reference guides, quizzes, self-paced assessments, and hands-on labs to strengthen your ability to build, manage, and deploy ML models using AWS services.· In the first part of the course, you’ll learn the basics of data science, machine learning, and how AWS enables scalable cloud-based solutions.· In

Welcome to this course on Machine Learning and Data Science with AWS. Amazon Web services or AWS is one of the biggest cloud computing platform where everything gets deployed to scale and action. Understanding the concepts and methods are vital, but being able to develop and deploy those concepts in forms of real life applications is something that is most weighted by the industry. Thus, here in this course, we are focused on ways you can use various cloud services on AWS to actually build and deploy you ideas into actions on multiple domains on Machine Learning and Data Science. You could be an IT&nbsp;professional looking for job change or upgrading your skillset or you could be a passionate learner or cloud certification aspirant, this course is for wider audience that if formed by the people who would like to learn any of these or a combination of these things-Create and Analyze dataset to find insights and spot outliers or trends Build Data visualization reports and dashboards by combining various visualization charts to represent data insights Develop machine learning models for Natural Language Processing for various applications on AWS And much more.Course Structure This course consists of multiple topics that are arranged in multiple sections. In the first few sections you would learn cloud services related to Data Science and Analysis on AWS with hands on practical examples. There you would be learning about creating a crawler in Glue, Analyzing dataset using SQL in Amazon Athena. After that you would learn to prepare a dataset for creating Data Visualization charts and reports that can be used for finding critical insights from the dataset that can be used in decision making process. You will learn to create calculated fields, excluded lists and filters on AWS Quicksight, followed by some advanced charts such as Word cloud and Funnel chart.After that in Machine Learning section, you will learn