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Introduction to Deep Learning
· Introduction to Deep Learning
· Deep Learning Frameworks
· Deep Learning Applications
· Scaling & Deployment
· Assignment on Deep Learning
· Introduction to Neural Networks
· Artificial Neural Networks
· Keras and Deep Learning Libraries
· Deep Learning Model
· Assignment for the abovementioned concept
· Tensor and Datasets
· Linear Regression
· Linear Regression using PyTorch
· Multiple input output using Linear Regression
· Logistic regression for classification
· Softmax regression
· Shallow Neural Networks
· Deep Networks
· Convulutional Neural Network
· Introduction to Deep Learning using Tensor flow
· Supervised Learning
· Unsupervised Learning
· Loading Data
· Data preparation with Keras
· Linear Classifier PyTorch
· Building a classifier with Pre-Trained Model
· Evaluating and Testing Pre-Trained models
· Gain a comprehensive understanding of Deep Learning concepts and techniques.
· Explore various Deep Learning frameworks and their applications in real-world scenarios.
· Master the fundamentals of Neural Networks using Keras and understand its practical applications.
· Learn to implement Deep Learning models using TensorFlow for tasks such as image classification and natural language processing.
· Acquire proficiency in building Deep Neural Networks with PyTorch, including convolutional neural networks.
· Develop hands-on experience through practical assignments and projects, culminating in a capstone project to apply Deep Learning concepts in a real-world scenario.
Deep Learning using Python is a course focused on advanced neural network techniques and their implementation with Python programming. It covers topics like neural network architectures, training algorithms, and practical applications using libraries like TensorFlow and Keras. Participants gain skills in building and training deep learning models for tasks such as image recognition, natural language processing, and more.
The Deep Learning using Python course offers essential skills in advanced neural network techniques and their application using the Python programming language. It provides hands-on experience in building and training deep learning models for tasks such as image recognition, natural language processing, and more. It is the fastest growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. With the growing demand for deep learning expertise across various industries, this course equips participants with valuable skills for pursuing rewarding career opportunities in the field of artificial intelligence and data science.
More than ever before, companies are relying on data to make business decisions. Without data science, these industry trends stay undiscovered — no story to tell and no insights to share. In order to determine business goals, more and more companies are looking for data scientists to fill in the gaps. Data science is one of the fastest-growing and sectors of the tech industry.
This course will qualify you for a position as a data scientist or a data analyst. If you have a professional background in programming, you may also be able to get a position as a data engineer or a machine learning engineer.
Individuals looking for a career in programming or are currently working as developers, programmers, or web developers should attend this course. The course covers basic data science along with all advance features that an individual will perform as a data science professional. So, this course will help IT Developer, Project Manager and Analytics Professional to grow in their analytics journey.What background knowledge is necessary?
Not required as such. Anyone with an aptitude for learning programming and has interest for doing analysis on data can be a good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.
· Fundamentals of Machine Learning.
· Tools supporting Machine Learning solutions.
· Understanding concept of Classification.
· Understanding concept of Clustering.
· Understanding concept of Regression.
· Understanding concept of Recommender Systems
· Advanced Machine Learning
· Capstone Project with the usage of Machine Learning
Total Duration is 48 hours
Salary Estimates as on October 8th, 2020 in for a Data Scientist in USA are
Zip Recruiter – 76K-160K per Year
Glass Door – 83K-150K per Year
PaySclae USA – 67K-130K per Year
The average salary for a Data Scientist in Detroit, Michigan is $87815 as per PaySclae USA.
There is no pre-requisite as such. Anyone with an aptitude for learning programming and interest for doing analysis on data can be the good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.
Here are few to mention practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with python, statistics for model building and evaluation using the following tools and methods.
· Jupyter Notebook for Python
· RStudio for R
· Python Programming for Data Science
· Databases and SQL for Data Science
· Machine Learning with Python
· Deep Learning
This course is designed to give you an insight into Industry driven Data Science tools and methodologies, which is enough to prepare you to excel in your next role as a Data Scientist. The program will train you on R and Python, Machine Learning techniques, data reprocessing, regression, clustering, data analytics, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operationalizing an analytics project, and data visualization techniques.
While we do work in R and Python during the training, knowledge of any programming language will work- as we teach the principles from a “software agnostic” point of view, the principles transfer across programming languages. We teach how to interpret data, and then how to apply machine learning to take that to the next level.
We work hard to ensure that no prior statistics knowledge is required. We will teach you all the basics you need to know before and during the bootcamps. We cover correlations, hypothesis testing, and Linear Regression in the Course, all at a level appropriate for someone with no/little statistics experience.
Yes, you will receive grades for your work during the course.
Once you successfully complete the Data Science with Python, Machine Learning & AI Professional course, Global IT will provide you with an industry-recognized course completion certificate which will have a lifelong validity.
Yes, we provide both course materials and practice tests as part of our course curriculum to help you prepare for the actual certification exam.
We’ve certainly seen variation in regards to what employers have in mind when they use these terms, so please consider the answers below as general guidelines.
A Data Analyst is someone who creates and communicates insights from data to measure outcomes, make predictions, and guide business decisions. Often, there is a lighter coding burden placed upon someone with the title Data Analyst, though they may be expected to know certain languages or packages in R or python.
A Data Engineer is the designer, builder, and manager of the information or "big data" infrastructure. Each develops the architecture that helps analyze and process data in the way the organization needs it – and they make sure those systems are performing smoothly.
The term Data Scientist is used the most broadly. A job posting for a Data Scientist might describe a role identical to others calling for “data analyst,” though there is usually more diverse coding skills needed for a data scientist job. For the most part, data scientists are asked to participate in the entire cycle of problems and solutions. They help identify opportunities for companies to use data, while also finding, collecting, and integrating relevant data sources, performing analyses of varying degrees of complexity, writing code and creating tools that teams and businesses can use over time, and telling the story of what they’ve done to company stakeholders.
· Gain a comprehensive understanding of Deep Learning concepts and techniques.
· Explore various Deep Learning frameworks and their applications in real-world scenarios.
· Master the fundamentals of Neural Networks using Keras and understand its practical applications.
· Learn to implement Deep Learning models using TensorFlow for tasks such as image classification and natural language processing.
· Acquire proficiency in building Deep Neural Networks with PyTorch, including convolutional neural networks.
· Develop hands-on experience through practical assignments and projects, culminating in a capstone project to apply Deep Learning concepts in a real-world scenario.
Introduction to Deep Learning
· Introduction to Deep Learning
· Deep Learning Frameworks
· Deep Learning Applications
· Scaling & Deployment
· Assignment on Deep Learning
· Introduction to Neural Networks
· Artificial Neural Networks
· Keras and Deep Learning Libraries
· Deep Learning Model
· Assignment for the abovementioned concept
· Tensor and Datasets
· Linear Regression
· Linear Regression using PyTorch
· Multiple input output using Linear Regression
· Logistic regression for classification
· Softmax regression
· Shallow Neural Networks
· Deep Networks
· Convulutional Neural Network
· Introduction to Deep Learning using Tensor flow
· Supervised Learning
· Unsupervised Learning
· Loading Data
· Data preparation with Keras
· Linear Classifier PyTorch
· Building a classifier with Pre-Trained Model
· Evaluating and Testing Pre-Trained models
Deep Learning using Python is a course focused on advanced neural network techniques and their implementation with Python programming. It covers topics like neural network architectures, training algorithms, and practical applications using libraries like TensorFlow and Keras. Participants gain skills in building and training deep learning models for tasks such as image recognition, natural language processing, and more.
The Deep Learning using Python course offers essential skills in advanced neural network techniques and their application using the Python programming language. It provides hands-on experience in building and training deep learning models for tasks such as image recognition, natural language processing, and more. It is the fastest growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. With the growing demand for deep learning expertise across various industries, this course equips participants with valuable skills for pursuing rewarding career opportunities in the field of artificial intelligence and data science.
More than ever before, companies are relying on data to make business decisions. Without data science, these industry trends stay undiscovered — no story to tell and no insights to share. In order to determine business goals, more and more companies are looking for data scientists to fill in the gaps. Data science is one of the fastest-growing and sectors of the tech industry.
This course will qualify you for a position as a data scientist or a data analyst. If you have a professional background in programming, you may also be able to get a position as a data engineer or a machine learning engineer.
Individuals looking for a career in programming or are currently working as developers, programmers, or web developers should attend this course. The course covers basic data science along with all advance features that an individual will perform as a data science professional. So, this course will help IT Developer, Project Manager and Analytics Professional to grow in their analytics journey.What background knowledge is necessary?
Not required as such. Anyone with an aptitude for learning programming and has interest for doing analysis on data can be a good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.
· Fundamentals of Machine Learning.
· Tools supporting Machine Learning solutions.
· Understanding concept of Classification.
· Understanding concept of Clustering.
· Understanding concept of Regression.
· Understanding concept of Recommender Systems
· Advanced Machine Learning
· Capstone Project with the usage of Machine Learning
Total Duration is 48 hours
Salary Estimates as on October 8th, 2020 in for a Data Scientist in USA are
Zip Recruiter – 76K-160K per Year
Glass Door – 83K-150K per Year
PaySclae USA – 67K-130K per Year
The average salary for a Data Scientist in Detroit, Michigan is $87815 as per PaySclae USA.
There is no pre-requisite as such. Anyone with an aptitude for learning programming and interest for doing analysis on data can be the good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.
Here are few to mention practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with python, statistics for model building and evaluation using the following tools and methods.
· Jupyter Notebook for Python
· RStudio for R
· Python Programming for Data Science
· Databases and SQL for Data Science
· Machine Learning with Python
· Deep Learning
This course is designed to give you an insight into Industry driven Data Science tools and methodologies, which is enough to prepare you to excel in your next role as a Data Scientist. The program will train you on R and Python, Machine Learning techniques, data reprocessing, regression, clustering, data analytics, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operationalizing an analytics project, and data visualization techniques.
While we do work in R and Python during the training, knowledge of any programming language will work- as we teach the principles from a “software agnostic” point of view, the principles transfer across programming languages. We teach how to interpret data, and then how to apply machine learning to take that to the next level.
We work hard to ensure that no prior statistics knowledge is required. We will teach you all the basics you need to know before and during the bootcamps. We cover correlations, hypothesis testing, and Linear Regression in the Course, all at a level appropriate for someone with no/little statistics experience.
Yes, you will receive grades for your work during the course.
Once you successfully complete the Data Science with Python, Machine Learning & AI Professional course, Global IT will provide you with an industry-recognized course completion certificate which will have a lifelong validity.
Yes, we provide both course materials and practice tests as part of our course curriculum to help you prepare for the actual certification exam.
We’ve certainly seen variation in regards to what employers have in mind when they use these terms, so please consider the answers below as general guidelines.
A Data Analyst is someone who creates and communicates insights from data to measure outcomes, make predictions, and guide business decisions. Often, there is a lighter coding burden placed upon someone with the title Data Analyst, though they may be expected to know certain languages or packages in R or python.
A Data Engineer is the designer, builder, and manager of the information or "big data" infrastructure. Each develops the architecture that helps analyze and process data in the way the organization needs it – and they make sure those systems are performing smoothly.
The term Data Scientist is used the most broadly. A job posting for a Data Scientist might describe a role identical to others calling for “data analyst,” though there is usually more diverse coding skills needed for a data scientist job. For the most part, data scientists are asked to participate in the entire cycle of problems and solutions. They help identify opportunities for companies to use data, while also finding, collecting, and integrating relevant data sources, performing analyses of varying degrees of complexity, writing code and creating tools that teams and businesses can use over time, and telling the story of what they’ve done to company stakeholders.
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· Gain a comprehensive understanding of Deep Learning concepts and techniques.
· Explore various Deep Learning frameworks and their applications in real-world scenarios.
· Master the fundamentals of Neural Networks using Keras and understand its practical applications.
· Learn to implement Deep Learning models using TensorFlow for tasks such as image classification and natural language processing.
· Acquire proficiency in building Deep Neural Networks with PyTorch, including convolutional neural networks.
· Develop hands-on experience through practical assignments and projects, culminating in a capstone project to apply Deep Learning concepts in a real-world scenario.
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Introduction to Deep Learning
· Introduction to Deep Learning
· Deep Learning Frameworks
· Deep Learning Applications
· Scaling & Deployment
· Assignment on Deep Learning
· Introduction to Neural Networks
· Artificial Neural Networks
· Keras and Deep Learning Libraries
· Deep Learning Model
· Assignment for the abovementioned concept
· Tensor and Datasets
· Linear Regression
· Linear Regression using PyTorch
· Multiple input output using Linear Regression
· Logistic regression for classification
· Softmax regression
· Shallow Neural Networks
· Deep Networks
· Convulutional Neural Network
· Introduction to Deep Learning using Tensor flow
· Supervised Learning
· Unsupervised Learning
· Loading Data
· Data preparation with Keras
· Linear Classifier PyTorch
· Building a classifier with Pre-Trained Model
· Evaluating and Testing Pre-Trained models
.svg)
Deep Learning using Python is a course focused on advanced neural network techniques and their implementation with Python programming. It covers topics like neural network architectures, training algorithms, and practical applications using libraries like TensorFlow and Keras. Participants gain skills in building and training deep learning models for tasks such as image recognition, natural language processing, and more.
The Deep Learning using Python course offers essential skills in advanced neural network techniques and their application using the Python programming language. It provides hands-on experience in building and training deep learning models for tasks such as image recognition, natural language processing, and more. It is the fastest growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. With the growing demand for deep learning expertise across various industries, this course equips participants with valuable skills for pursuing rewarding career opportunities in the field of artificial intelligence and data science.
More than ever before, companies are relying on data to make business decisions. Without data science, these industry trends stay undiscovered — no story to tell and no insights to share. In order to determine business goals, more and more companies are looking for data scientists to fill in the gaps. Data science is one of the fastest-growing and sectors of the tech industry.
This course will qualify you for a position as a data scientist or a data analyst. If you have a professional background in programming, you may also be able to get a position as a data engineer or a machine learning engineer.
Individuals looking for a career in programming or are currently working as developers, programmers, or web developers should attend this course. The course covers basic data science along with all advance features that an individual will perform as a data science professional. So, this course will help IT Developer, Project Manager and Analytics Professional to grow in their analytics journey.What background knowledge is necessary?
Not required as such. Anyone with an aptitude for learning programming and has interest for doing analysis on data can be a good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.
· Fundamentals of Machine Learning.
· Tools supporting Machine Learning solutions.
· Understanding concept of Classification.
· Understanding concept of Clustering.
· Understanding concept of Regression.
· Understanding concept of Recommender Systems
· Advanced Machine Learning
· Capstone Project with the usage of Machine Learning
Total Duration is 48 hours
Salary Estimates as on October 8th, 2020 in for a Data Scientist in USA are
Zip Recruiter – 76K-160K per Year
Glass Door – 83K-150K per Year
PaySclae USA – 67K-130K per Year
The average salary for a Data Scientist in Detroit, Michigan is $87815 as per PaySclae USA.
There is no pre-requisite as such. Anyone with an aptitude for learning programming and interest for doing analysis on data can be the good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.
Here are few to mention practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with python, statistics for model building and evaluation using the following tools and methods.
· Jupyter Notebook for Python
· RStudio for R
· Python Programming for Data Science
· Databases and SQL for Data Science
· Machine Learning with Python
· Deep Learning
This course is designed to give you an insight into Industry driven Data Science tools and methodologies, which is enough to prepare you to excel in your next role as a Data Scientist. The program will train you on R and Python, Machine Learning techniques, data reprocessing, regression, clustering, data analytics, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operationalizing an analytics project, and data visualization techniques.
While we do work in R and Python during the training, knowledge of any programming language will work- as we teach the principles from a “software agnostic” point of view, the principles transfer across programming languages. We teach how to interpret data, and then how to apply machine learning to take that to the next level.
We work hard to ensure that no prior statistics knowledge is required. We will teach you all the basics you need to know before and during the bootcamps. We cover correlations, hypothesis testing, and Linear Regression in the Course, all at a level appropriate for someone with no/little statistics experience.
Yes, you will receive grades for your work during the course.
Once you successfully complete the Data Science with Python, Machine Learning & AI Professional course, Global IT will provide you with an industry-recognized course completion certificate which will have a lifelong validity.
Yes, we provide both course materials and practice tests as part of our course curriculum to help you prepare for the actual certification exam.
We’ve certainly seen variation in regards to what employers have in mind when they use these terms, so please consider the answers below as general guidelines.
A Data Analyst is someone who creates and communicates insights from data to measure outcomes, make predictions, and guide business decisions. Often, there is a lighter coding burden placed upon someone with the title Data Analyst, though they may be expected to know certain languages or packages in R or python.
A Data Engineer is the designer, builder, and manager of the information or "big data" infrastructure. Each develops the architecture that helps analyze and process data in the way the organization needs it – and they make sure those systems are performing smoothly.
The term Data Scientist is used the most broadly. A job posting for a Data Scientist might describe a role identical to others calling for “data analyst,” though there is usually more diverse coding skills needed for a data scientist job. For the most part, data scientists are asked to participate in the entire cycle of problems and solutions. They help identify opportunities for companies to use data, while also finding, collecting, and integrating relevant data sources, performing analyses of varying degrees of complexity, writing code and creating tools that teams and businesses can use over time, and telling the story of what they’ve done to company stakeholders.
Expanded baseline security topics essential for IT support, including physical vs. logical security concepts, malware, and more.
A revised approach to operational procedures, covering basic disaster prevention, recovery, and scripting basics.
A stronger focus on networking and device connectivity
