Deep Learning & Artificial Intelligence (AI)

• 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.

To say Deep Learning and Artificial Intelligence is important is, to say nothing about its growing popularity. It contributes heavily towards making our daily lives more convenient, and this trend will grow in the future. Whether it is parking assistance through technology or face recognition at the airport, deep learning is fueling a lot of automation in today’s world. However, deep learning’s relevance can be linked most to the fact that our world is generating exponential amounts of data today, which needs structuring on a large scale. Deep learning uses the growing volume and availability of data has been most aptly. All the information collected from these data is used to achieve accurate results through iterative learning models. As part of this highly specialized tech force your work in Data Science projects you will be sort after.

Data science jobs are the most demanding jobs in the Information Technology field today. Prospective job seekers have numerous opportunities. It is the fastest growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. This makes Data Science a highly employable job sector.

Data Science is a vastly abundant field and has a lot of opportunities. The field of Data Science is high in demand but low in supply of Data Scientists.

Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option.

There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field. Therefore, you will have the opportunity to work in various fields.

Companies require skilled Data Scientists to process and analyze their data. They not only analyze the data but also improve its quality. Therefore, Data Science deals with enriching data and making it better for their company.

Data Scientists allow companies to make smarter business decisions. Companies rely on Data Scientists and use their expertise to provide better results to their clients. This gives Data Scientists an important position in the company.

Data Science has helped various industries to automate redundant tasks. Companies are using historical data to train machines in order to perform repetitive tasks. This has simplified the arduous jobs undertaken by humans before.

Data Science involves the usage of Machine Learning which has enabled industries to create better products tailored specifically for customer experiences.

In this course, you will learn about how the data is used with different analytics steps such as business understanding, data collection, data wrangling, data exploration, data modeling and data visualization etc.

Introduction to Deep Learning

• Introduction to Deep Learning
• Deep Learning Frameworks
• Deep Learning Applications
• Scaling & Deployment
• Assignment on Deep Learning

Introduction to Neural Networks with Keras

• Introduction to Neural Networks
• Artificial Neural Networks
• Keras and Deep Learning Libraries
• Deep Learning Model
• Assignment for the abovementioned concept

Deep Neural Networks with PyTorch

• 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

Building Deep Learning Models with Tensor Flow

• Introduction to Deep Learning using Tensor flow
• Supervised Learning
• Unsupervised Learning

AI Capstone Project with Deep Learning

• Loading Data
• Data preparation with Keras
• Linear Classifier PyTorch
• Building a classifier with Pre-Trained Model
• Evaluating and Testing Pre-Trained models

1. What is Deep Learning using Python Course?

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.

2. Why Deep Learning using Python Course?

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.

3. Who should go for this Training?

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.

4. 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.

5. What will I learn in 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

6. What is the duration of this Course?

Total Duration is 48 hours

7. How much does a Data Scientist earn?

Salary Estimates as on October 8^th, 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.

8. What are the prerequisites for this course?

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.

9. What skills do I learn in this course – Deep Learning using Python?

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

10. How do I become a Data Scientist?

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.

11. What software/technology stacks do you use?

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.

12. How much statistics will I need to know?

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.

13. Do we receive grades?

Yes, you will receive grades for your work during the course.

14. Who provides the certification and how long does its valid?

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.

15. Do you provide any material or practice tests during the course?

Yes, we provide both course materials and practice tests as part of our course curriculumto help you prepare for the actual certification exam.

16. What is the difference between a Data Scientist, Data Analyst, and Data Engineer?

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.