Deep Learning & Artificial Intelligence (AI)

Pre-Requisites: 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. Some knowledge on Data Science and Machine Learning with Python is good to have but not a must have.

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Key Features

  • Online live classroom available
  • Quality learning materials
  • Small Class Sizes
  • State of the Art Facility
  • Free Retakes
  • Instructor Led Classroom training
  • Certified Industry Experienced Teachers
  • 100% Job Placement assistance

Individuals looking for a career in programming or are currently working as developers, data analysts, researchers, programmers, or web developers should attend this course. The course covers basic data science, basic Machine Learning With Python, Deep Learning & Artificial Intelligence (A.I) 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.

  • Understand the concepts of Deep Learning
  • Understand the concepts of Deep Learning Frameworks & Applications
  • Understand the concept of Neural Networks using Keras
  • How to use Tensor flow for deep learning models
  • How to use Deep Neural Networks with PyTorch
  • Finally, one capstone project to leverage the deep learning concept.

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

What is Deep Learning & Artificial Intelligence (AI) Course?

Deep learning is a specific method of machine learning that incorporates neural networks in successive layers to learn from data in an iterative manner. Deep learning is especially useful when you’re trying to learn patterns from unstructured data. Deep learning complex neural networks are designed to emulate how the human brain works, so computers can be trained to deal with poorly defined abstractions and problems. The average five-year-old child can easily recognize the difference between his teacher’s face and the face of the crossing guard. In contrast, the computer must do a lot of work to figure out who is who. Neural networks and deep learning are often used in image recognition, speech, and computer vision applications. Deep learning offers a set of techniques and algorithms that help us to parameterize deep neural network structures – artificial neural networks with many hidden layers and parameters. One of the key ideas behind deep learning is to extract high level features from the given dataset. Thereby, deep learning aims to overcome the challenge of the often tedious feature engineering task and helps with parameterizing traditional neural networks with many layers. Deep learning – originally, we can consider it as a subfield of AI – was concerned with the development of algorithms so that computers can automatically learn (predictive) models from data.

This course focuses on the practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with python, Loading Data, Data preparation with Keras, Linear Classifier PyTorch, Building a classifier with Pre-Trained Model, Evaluating and Testing Pre-Trained models.

Why Deep Learning & Artificial Intelligence (AI) Course?

If you want to break into AI, this course will help you do so. Deep Learning is one of the most highly sought after skills in tech. Deep Learning & Artificial Intelligence (AI) is the tool that helps data science get results and the solutions for specific problems. As a Deep Learning & Artificial Intelligence (A.I) specialist in Data Science projects your work is high sought 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. Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year. 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 as a highly lucrative career option.

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.

Who should attend for this Training?

Individuals looking for a career in programming or are currently working as developers, data analysts, programmers, researchers, 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.

What will I learn in this course?

You will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, we will teach you to-

  • Understand the concepts of Deep Learning
  • Understand the concepts of Deep Learning Frameworks & Applications
  • Understand the concept of Neural Networks using Keras.
  • How to use Tensor flow for deep learning models.
  • How to use Deep Neural Networks with PyTorch
  • Finally, one capstone project to leverage the deep learning concept.

AI is transforming multiple industries. We will help you master Deep Learning, understand how to apply it, and build a career in AI.

Is Data Science required for Deep Learning & Artificial Intelligence course ?

Since both Machine Learning, Deep Learning & Artificial Intelligence and Data Science are closely connected, a basic knowledge of each is required to specialize in either of the domains. Having said that, more than data science the knowledge of data analysis is required to get started with Deep Learning & Artificial Intelligence. Learning programming languages like R, and Python are required to understand and clean data to use it for creating Machine Learning algorithms. Most Machine Learning courses include tutorials on these programming languages and basic data analysis and data science concepts.

What is the duration of this Course?

Total Duration is 48 hours

How much does a Data Scientist earn?

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.

What skills do I learn in this course – Deep Learning & Artificial Intelligence (AI)?

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
  • Python Programming for Data Science
  • Databases and SQL for Data Science
  • Machine Learning with Python
  • Deep Learning

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 modelling, the technology and tools that can be used for advanced analytics, operational zing an analytics project, and data visualization techniques.

What is the difference between Deep Learning and Machine Learning?

Though often used interchangeably, deep learning and machine learning are both part of artificial intelligence and are not the same thing. Machine Learning is a broader spectrum which uses data to define and create learning models. Machine learning tries to understand the structure of data with statistical models. It starts with data mining where it extracts relevant information from data sets manually after which it uses algorithms to direct computers to learn from data and make predictions. Machine learning has been in use for a long time and has evolved over time. Deep Learning is a comparatively new field which focuses only on neural networking to learn and function. Neural networking, as discussed earlier, replicates the human neurals artificially to screen and gather information from data automatically. Since deep learning involves end-to-end learning where raw data is fed to the system, the more data it studies, the more precise and accurate the results are.

How does Deep Learning work?

At its core, deep learning relies on iterative methods to teach machines to imitate human intelligence. An artificial neural network carries out this iterative method through several hierarchical levels. The initial levels help the machines learn simple information, and as the levels increase, the information keeps building. With each new level machines pick up further information and combines it with what it had learnt in the last level. At the end of the process, the system gathers a final piece of information which is a compound input. This information passes through several hierarchies and has semblance to complex logical thinking.

Tell me few commonly-Used Deep Learning Applications.

Virtual Assistants: Amazon Echo, Google Assistant, Alexa, and Siri are all exploiting deep learning capabilities to build a customized user experience for you. They ‘learn’ to recognize your voice and accent and present you a secondary human experience through a machine by using deep neural networks imitating not just speech but also the tone of a human. Virtual assistants help you shop, navigate, take notes and translate them to text, and even make salon appointments for you.

Facial Recognition: The iPhone’s Facial Recognition uses deep learning to identify data points from your face to unlock your phone or spot you in images. Deep Learning helps them protect the phone from unwanted unlocks and making your experience hassle-free even when you have changed your hairstyle, lost weight, or in poor lighting. Every time you unlock your phone, deep learning uses thousands of data points to create a depth map of your face and the inbuilt algorithm uses those to identify if it is really you or not.

Personalization: E-Commerce and Entertainment giants like Amazon and Netflix, etc. are building their deep learning capacities further to provide you with a personalized shopping or entertainment system. Recommended items/series/movies based on your ‘pattern’ are all based on deep learning. Their businesses thrive on pushing out options in your subconscious based on your preferences, recently visited items, affinity to brands/actors/artists, and overall browsing history on their platforms.

Natural Language Processing: One of the most critical technologies, Natural Language Processing is taking AI from good to great in terms of use, maturity, and sophistication. Organizations are using deep learning extensively to enhance these complexities in NLP applications. Document summarization, question answering, language modelling, text classification, sentiment analysis are some of the popular applications that are already picking up momentum. Several jobs worldwide that depend on human intervention for verbal and written language expertise will become redundant as NLP matures.

Healthcare: Another sector to have seen tremendous growth and transformation is the healthcare sector. From personal virtual assistants to fitness bands and gears, computers are recording a lot of data about a person’s physiological and mental condition every second.

Autonomous Cars: Uber AI Labs in Pittsburg are engaging in some tremendous work to make autonomous cars a reality for the world. Deep Learning, of course, is the guiding principle behind this initiative for all automotive giants.

Text Generation: Soon, deep learning will create original text (even poetry), as technologies for text generation is evolving fast.

Visual Recognition: Convolutional Neural Networks enable digital image processing that can further be segregated into facial recognition, object recognition, handwriting analysis, etc. Computers can now recognize images using deep learning.

What software/technology stacks do you use?

While we do work in 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 Deep learning and Artificial Intelligence to take that to the next level.

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.

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.

What is the Future of Data Science?

Putting it slightly differently – Data Science is the future. No businesses or industries for that matter will be able to keep up without data science. A large number of transitions have already happened worldwide where businesses are seeking more data-driven decisions, more is to follow suit. Data science quite rightly has been dubbed as the oil of the 21st century which can mean endless possibilities across industries. So, if you are keen on pursuing this path, your efforts will be highly rewarded with not just a fulfilling career and fat pay cheques but also a lot of job security.

Course Number : DATA-0-103

Duration : 48 hours

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