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Why enroll for Artificial Intelligence Complete Course?
AI is shaping the future of humanity across every industry and will continue to act as a technological innovator for the foreseeable future.
The global AI market size was valued at USD 136.55B in 2022 and is expected to hit USD 1,597.1B by 2030 at a CAGR of 37.3% from 2023 to 2030.
As per salary.com, the base salary for Lead AI Engineer ranges from USD 157,302 to USD 191,127 with the average base salary of USD 170,265.
Artificial Intelligence Complete Course Training Benefits
Today, the amount of data that is generated, by both humans and machines, far outpaces humans' ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning, allows organizations to improve core business processes and is the future of all complex decision making. The best way to land you a good job with a handsome salary in this domain is to get AI Certification.
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Why Artificial Intelligence Complete Course from edureka
Live Interactive Learning
World-Class Instructors
Expert-Led Mentoring Sessions
Instant doubt clearing
Lifetime Access
Course Access Never Expires
Free Access to Future Updates
Unlimited Access to Course Content
24x7 Support
One-On-One Learning Assistance
Help Desk Support
Resolve Doubts in Real-time
Hands-On Project Based Learning
Industry-Relevant Projects
Course Demo Dataset & Files
Quizzes & Assignments
Industry Recognised Certification
Edureka Training Certificate
Graded Performance Certificate
Certificate of Completion
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About your Artificial Intelligence Complete Course
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Applications of Machine Learning in Various Industries
Basics of Data Preprocessing Techniques
Classification and Regression Algorithms
AI in Practice
AI Ethics and Bias
Hands-on:
Preprocessing a Dataset for Machine Learning Application
AI Implementation across Industries
Skills You Will Learn:
Data Preprocessing
Machine Learning Essentials
Ethical Considerations of AI
Machine Learning
9 Topics
Topics:
Data Preprocessing
Handling Imbalanced Data
Feature Engineering for Fraud Detection Models
Model Training and Building for Fraud Detection
Hyperparameter Tuning for Model Optimization
Performance Metrics
Analyzing Results and Performance Metrics
Model Selection Techniques
Ensemble Learning Techniques
Hands-on:
Fraud Detection Using Machine Learning
Feature Engineering
Skills You Will Learn:
Model Evaluation
Model Selection and Validation
Model Building
Introduction to Text Mining and NLP
8 Topics
Topics:
Overview of Text Mining
Need of Text Mining
Natural Language Processing (NLP) in Text Mining
Applications of Text Mining
OS Module
Reading, Writing to text and word files
Setting the NLTK Environment
Accessing the NLTK Corpora
Hands-on:
Install NLTK Packages using NLTK Downloader
Accessing your operating system using the OS Module in Python
How to read json format, understand key-value pairs, and for that matter, understand uses of pkl files
Skills You Will Learn:
Reading & Writing .txt Files from/to your Local
Reading & Writing .docx Files from/to your Local
Working with the NLTK Corpora
Extracting, Cleaning and Preprocessing Text
9 Topics
Topics:
Tokenization
Frequency Distribution
Different Types of Tokenizers
Bigrams, Trigrams & Ngrams
Stemming
Lemmatization
Stopwords
POS Tagging
Named Entity Recognition
Hands-on:
Regex, Word, Blankline, Sentence Tokenizers
Bigrams, Trigrams & Ngrams
Stopword Removal
UTF encoding, dealing with URLs, hashtags
POS Tagging
Named Entity Recognition (NER)
Skills You Will Learn:
Tokenization
Stopword Removal
UTF encoding
POS Tagging
Named Entity Recognition (NER)
Analyzing Sentence Structure
5 Topics
Topics:
Syntax Trees
Chunking
Chinking
Context Free Grammars (CFG)
Automating Text Paraphrasing
Hands-on:
Parsing Syntax Trees
Chunking
Chinking
Automate Text Paraphrasing using CFG’s
Skills You Will Learn:
Chunking
Chinking
Automate Text Paraphrasing
Text Classification-I
5 Topics
Topics:
Machine Learning: Brush Up
Bag of Words
Count Vectorizer
Term Frequency (TF)
Inverse Document Frequency (IDF)
Hands-on:
Demonstrate Bag of Words Approach
Working with CountVectorizer()
Using TF & IDF
Skills You Will Learn:
Bag of Words
CountVectorizer()
TF-IDF
Text Classification-II
3 Topics
Topics:
Converting text to features and labels
Multinomial Naive Bayes Classifier
Leveraging Confusion Matrix
Hands-on:
Converting text to features and labels
Demonstrate text classification using Multinomial NB Classifier
Leveraging Confusion Matrix
Skills You Will Learn:
Converting text to features and labels
Text classification
Confusion Matrix
Introduction to Deep Learning
11 Topics
Topics:
What is Deep Learning?
Curse of Dimensionality
Machine Learning vs. Deep Learning
Use cases of Deep Learning
Human Brain vs. Neural Network
What is Perceptron?
Learning Rate
Epoch
Batch Size
Activation Function
Single Layer Perceptron
Hands-on:
Single Layer Perceptron
Skills You Will Learn:
Curse of Dimensionality
Single Layer Perceptron
Getting Started with TensorFlow 2.0
14 Topics
Topics:
Introduction to TensorFlow 2.x
Installing TensorFlow 2.x
Defining Sequence model layers
Activation Function
Layer Types
Model Compilation
Model Optimizer
Model Loss Function
Model Training
Digit Classification using Simple Neural Network in TensorFlow 2.x
Improving the model
Adding Hidden Layer
Adding Dropout
Using Adam Optimizer
Hands-on:
Classifying handwritten digits using TensorFlow 2.0
Skills You Will Learn:
Installing and Working with TensorFlow 2.0
TensorFlow for Deployment
13 Topics
Topics:
Use Case: Amazon’s Virtual Try-Out Room
Why Deploy models?
Model Deployment: Intuit AI models
Model Deployment: Instagram’s Image Classification Models
What is Model Deployment
Types of Model Deployment Techniques
TensorFlow Serving
Browser-based Models
What is TensorFlow Serving?
What are Servables?
Demo: Deploy the Model in Practice using TensorFlow Serving
Introduction to Browser based Models
Demo: Deploy a Deep Learning Model in your Browser
Hands-on:
Learn and build a program that Detects Faces using your webcam using OpenCV
Learn Hyper parameter tuning techniques in Keras on a Fashion Dataset
Build and deploy a model using TensorFlow Serving
Build a neural network model for Handwritten digits use activation function, batch size, Optimizer and learning rate for betterment of you model
Build a Object detection model and detection is done by providing a video the model accurately identifies the objects that are depicted in the video
Skills You Will Learn:
Deploying model with Tensorflow
Convolution Neural Network
12 Topics
Topics:
Image Classification Example
What is Convolution
Convolutional Layer Network
Convolutional Layer
Filtering
ReLU Layer
Pooling
Data Flattening
Fully Connected Layer
Predicting a cat or a dog
Saving and Loading a Model
Face Detection using OpenCV
Hands-on:
Saving and Loading a Model
Face Detection using OpenCV
Skills You Will Learn:
Image Classification using CNN
Face Detection using OpenCV
Regional CNN
20 Topics
Topics:
Regional-CNN
Selective Search Algorithm
Bounding Box Regression
SVM in RCNN
Pre-trained Model
Model Accuracy
Model Inference Time
Model Size Comparison
Transfer Learning
Object Detection – Evaluation
mAP
IoU
RCNN – Speed Bottleneck
Fast R-CNN
RoI Pooling
Fast R-CNN – Speed Bottleneck
Faster R-CNN
Feature Pyramid Network (FPN)
Regional Proposal Network (RPN)
Mask R-CNN
Hands-on:
Transfer Learning
Object Detection
Skills You Will Learn:
Transfer Learning
Object Detection
Mask R-CNN
Boltzmann Machine & Autoencoder
9 Topics
Topics:
What is Boltzmann Machine (BM)?
Identify the issues with BM
Why did RBM come into the picture?
Step-by-step implementation of RBM
Distribution of Boltzmann Machine
Understanding Autoencoders
Architecture of Autoencoders
Brief on types of Autoencoders
Applications of Autoencoders
Hands-on:
Implement RBM
Simple encoder
Skills You Will Learn:
RBM
Autoencoders
Generative Adversarial Network(GAN)
7 Topics
Topics:
Which Face is Fake?
Understanding GAN
What is Generative Adversarial Network?
How does GAN work?
Step by step Generative Adversarial Network implementation
Types of GAN
Recent Advances: GAN
Hands-on:
Implement Generative Adversarial Network
Skills You Will Learn:
Generative Adversarial Network
Emotion and Gender Detection
5 Topics
Topics:
Where do we use Emotion and Gender Detection?
How does it work?
Emotion Detection architecture
Face/Emotion detection using Haar Cascade
Implementation on Colab
Hands-on:
Implement Emotion and Gender Detection
Skills You Will Learn:
Emotion and Gender Detection
Introduction to RNN and GRU
14 Topics
Topics:
Issues with Feed Forward Network
Recurrent Neural Network (RNN)
Architecture of RNN
Calculation in RNN
Backpropagation and Loss calculation
Applications of RNN
Vanishing Gradient
Exploding Gradient
What is GRU?
Components of GRU
Update gate
Reset gate
Current memory content
Final memory at current time step
Hands-on:
Implement COVID RNN GRU
Skills You Will Learn:
RNN
GRU
LSTM
18 Topics
Topics:
What is LSTM?
Structure of LSTM
Forget Gate
Input Gate
Output Gate
LSTM architecture
Types of Sequence-Based Model
Sequence Prediction
Sequence Classification
Sequence Generation
Types of LSTM
Vanilla LSTM
Stacked LSTM
CNN LSTM
Bidirectional LSTM
How to increase the efficiency of the model?
Backpropagation through time
Workflow of BPTT
Hands-on:
Intent Detection using LSTM
Skills You Will Learn:
LSTM
Sequence Prediction
Sequence Generation
Auto Image Captioning Using CNN LSTM
9 Topics
Topics:
Auto Image Captioning
COCO dataset
Pre-trained model
Inception V3 model
The architecture of Inception V3
Modify the last layer of a pre-trained model
Freeze model
CNN for image processing
LSTM or text processing
Hands-on:
Auto Image Captioning
Skills You Will Learn:
Auto Image Captioning
CNN for image processing
LSTM or text processing
Developing a Criminal Identification and Detection Application Using OpenCV
4 Topics
Topics:
Why is OpenCV used?
What is OpenCV
Applications
Demo: Build a Criminal Identification and Detection App
Hands-on:
Build a Criminal Identification and Recognition app on Streamlit
Skills You Will Learn:
OpenCV
Project Implementation with OpenCV
Project
1 Topics
Topics:
Sentiment Classification on Movie Rating Dataset
Hands-on:
Implement all the text processing techniques starting with tokenization
Express your end to end work on Text Mining
Implement Machine Learning along with Text Processing
Skills You Will Learn:
Sentiment Analysis
Free Career Counselling
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About the Artificial Intelligence Complete Course
What is the Artificial Intelligence Complete Course?
Edureka’s Artificial Intelligence Course is well researched amalgamation of Natural Language Processing and Deep Learning, specifically designed for professionals and beginners to meet the industry standards. This course gives you an in-depth understanding of Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing using Python’s NLTK package, CNN, RCNN, RNN, LSTM, RBM, and their implementation using TensorFlow 2.0 package. You will learn to build real-time projects on NLP and Deep Learning, to make you industry-ready and help you to kickstart your career in this domain.
What are the prerequisites for this Artificial Intelligence Complete Course?
Prior knowledge of Python and Machine Learning will be helpful but not at all mandatory. To refresh your skills in Python and ML, we will provide self-paced videos absolutely free as prerequisites in your LMS.
Why take up the Online Artificial Intelligence Complete Course?
The demand for AI engineers is increasing rapidly and is expected to continue growing in the future, driven by the increasing adoption of AI technologies in various industries and the growing importance of AI skills in the job market.
There are many reasons why someone might want to take up an online artificial intelligence course. Here are a few:
Career advancement: Artificial intelligence is one of the fastest-growing fields in technology today, and there is a high demand for skilled professionals in this area. By taking an AI course, you can increase your skills and knowledge and make yourself more valuable to employers.
Stay up-to-date: The field of artificial intelligence is rapidly evolving, with new technologies and techniques being developed all the time. By taking an AI course, you can stay up-to-date with the latest trends and developments in the field.
Learn from experts: You will be taught by experts in the field, giving you access to their knowledge and experience. This can be invaluable in helping you understand complex topics and gain practical skills.
How will Artificial Intelligence help your career?
The field of artificial intelligence (AI) is rapidly growing and is expected to continue growing in the coming years. AI is being used in many industries such as healthcare, finance, education, and more. With the increasing adoption of AI, there is a high demand for professionals with the necessary skills and knowledge to work in this field.
Here are some ways AI career growth is happening:
Increased job opportunities: There is a growing demand for AI professionals in both technical and non-technical roles. Technical roles include AI engineers, data scientists, machine learning engineers, and software developers, while non-technical roles include AI project managers, AI consultants, and AI analysts.
Advancements in technology: As AI technology advances, there are new opportunities for AI professionals to develop new applications and solutions that can solve complex problems.
Emerging subfields: There are emerging subfields within AI, such as explainable AI, AI ethics, and AI security, which provide new opportunities for AI professionals to specialize and grow their careers.
Continuous learning and development: AI is a constantly evolving field, and AI professionals must continuously learn and develop new skills to stay up-to-date with the latest advancements.
Overall, the field of AI provides many opportunities for career growth and development, and there is a high demand for skilled professionals in this field. Enroll in this Artificial Intelligence Certification training today.
What are the essential concepts covered in this Artificial Intelligence Complete Course?
This Artificial Intelligence Complete Course provides in-depth knowledge of concepts such as Natural Language Processing, Text Classification, Text Processing, Image Processing, Object Detection, Deep Learning, TensorFlow, OpenCV, and many more.
Who should take up this Artificial Intelligence Complete Course?
The Artificial Intelligence Complete Course is suitable for anyone who wants to stay up-to-date with the latest advances in AI and wants to build the skills needed to develop and deploy intelligent systems.
This course will be ideal for the following professionals.
Freshers
Python Developers
Researchers
Data Scientists
Data Analysts
Machine Learning Engineers
NLP Engineers
Software Testers
Software Developers
What are the basic skills of a Artificial Intelligence Engineer?
Artificial Intelligence (AI) is a broad field with many subfields, and the skills required for an AI engineer can vary depending on the specific area of expertise. However, there are some basic skills that most AI engineers should possess:
Strong Programming Skills: AI engineers need to have a strong foundation in programming languages such as Python, C++, Java, or R. This includes knowledge of data structures, algorithms, and object-oriented programming.
Machine Learning: AI engineers must have a solid understanding of the concepts and algorithms of machine learning. This includes knowledge of supervised and unsupervised learning, deep learning, and natural language processing (NLP).
Data Structures and Algorithms: A deep understanding of data structures and algorithms is essential for designing and implementing efficient algorithms for large data sets. This also includes knowledge of big data technologies and distributed computing.
Statistics and Probability: Knowledge of statistics and probability is essential for understanding and designing machine learning algorithms. AI engineers need to know concepts like hypothesis testing, regression analysis, and Bayesian networks.
Problem Solving: AI engineers must have strong problem-solving skills to design and implement complex AI systems. They must be able to identify problems, break them down into smaller components, and develop solutions.
Creativity: AI engineers must be creative thinkers to develop novel solutions to complex problems. They should be able to think outside the box and come up with innovative ideas.
Ethics and Accountability: AI engineers must understand the ethical implications of their work and the impact it has on society. They must ensure their AI systems are transparent, explainable, and accountable.
What is the main focus of Edureka’s Artificial Intelligence Complete Course?
Edureka’s Artificial Intelligence Complete Course enables you to move ahead in your career by helping you get skilled with the fundamentals of AI. The AI course focuses on providing hands-on experience to make you ready for any AI related opportunity.
What will I learn from this Artificial Intelligence Complete Course?
Learn the fundamentals of Natural Language Processing (NLP), sentiment analysis, language translation, text summarization, deep learning, convolutional neural networks, recurrent neural networks, and autoencoders. Additionally, you will be working with the OpenCV library, object detection, image segmentation, and image classification along with various real-life projects.
What are the system requirements for this Artificial Intelligence Complete Course?
The system requirements for this AI Course
A system with an Intel i3 processor or above
A minimum of 4GB RAM (8GB or above is recommended for faster processing)
50 GB HDD Storage
Operating system: 32-bit or 64-bit
How will I execute the practicals in this Artificial Intelligence Complete Course?
You will execute your Assignments/Case Studies using Python Jupyter Notebook/Google Colab. Detailed step-by-step installation guides are available on the LMS. In case you come across any doubt, the 24*7 support team will promptly assist you.
Artificial Intelligence Complete Course Projects
Airline Sentiment Analysis
In this project, you will be provided with a dataset having tweets about 6 US Airlines along with their sentiments: positive, negative and neutral. You have to perform Sentiment ....
Face Mask Detection
The goal of the project is to create a Deep Learning model to detect in real time whether a person is wearing a face mask or not.
To unlock Edureka’s Artificial Intelligence Complete Course completion certificate, you must ensure the following:
Completely participate in this Artificial Intelligence Complete Course.
Evaluation and completion of the quizzes and projects listed.
Yes, Artificial Intelligence (AI) is a rapidly growing field with many opportunities for career growth and development. AI has the potential to transform industries such as healthcare, finance, transportation, and more, and as a result, there is a high demand for skilled professionals in this field.
There are many career paths available in AI, including machine learning engineer, data scientist, AI researcher, AI consultant, and more. These roles often require a combination of technical skills, such as programming and data analysis, as well as soft skills like communication and problem-solving.
Furthermore, the field of AI is constantly evolving, with new technologies and techniques emerging regularly. This means that AI professionals need to stay up-to-date with the latest developments and continue learning throughout their careers, which can make for an exciting and challenging work environment. Overall, if you have a strong interest in technology and a passion for problem-solving, a career in AI could be a great choice.
Artificial Intelligence (AI) is a complex and rapidly evolving field, learning its capabilities and functionality requires appropriate direction and a well-structured training path. Beginners interested in a career in Artificial Intelligence using Python can sign up for our training and earn certificates to demonstrate their expertise in this domain.
Artificial intelligence (AI) certification is becoming increasingly valuable in today's job market, as businesses across all industries are seeking to integrate AI technology into their operations. Obtaining an AI certification can demonstrate to potential employers that you possess the skills and knowledge necessary to work with AI and can help you stand out from other candidates.
Here are some potential benefits of obtaining an AI certification:
Demonstrates expertise: An AI certification can help demonstrate your proficiency in a specific area of AI technology, such as machine learning or natural language processing.
Increased job opportunities: Having an AI certification can help open doors to job opportunities that require knowledge of AI technology. This can include jobs in data analysis, software development, and other technical roles.
Higher salary potential: In general, individuals with specialized technical skills, such as AI, can command higher salaries than those without such skills. An AI certification can help increase your earning potential.
Competitive advantage: An AI certification can help you stand out from other candidates when applying for jobs or pursuing business opportunities.
Professional development: Pursuing an AI certification can help you continue to learn and grow in your field, keeping you up-to-date with the latest technology and trends.
Overall, obtaining an AI certification can be a valuable investment in your career and can help you stay competitive in the rapidly evolving job market.
Our Artificial Intelligence course is designed to develop skills and evaluate candidates' knowledge. Following the completion of this certification, you will have access to a wide range of job possibilities. Some of the most important employment roles include AI Engineer, Data Scientist, NLP Engineer, Deep Learning Engineer, Machine Learning Engineer, and others.
The Certificate ID can be verified at www.edureka.co/verify to check the authenticity of this certificate
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Artificial Intelligence Complete Course FAQs
What is Artificial Intelligence?
Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. It involves the development of algorithms and systems that enable machines to perform tasks that typically require human intelligence. These tasks can include reasoning, problem-solving, learning, perception, language understanding, and decision-making.
What is Deep Learning?
Deep Learning is a subset of machine learning that focuses on artificial neural networks and their ability to learn and make decisions in a manner similar to the human brain. It involves training deep neural networks with multiple layers to process and learn from large amounts of data, enabling these networks to perform complex tasks such as image and speech recognition, natural language processing, and more.
Can I attend a demo Artificial Intelligence session before enrollment?
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.
Who are the instructors for this Artificial Intelligence Complete Course?
All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.
How can I become an AI Engineer?
To become an AI engineer, you can follow these steps:
Learn programming: Start with languages like Python, Java, or C++, and gain proficiency in data structures and algorithms.
Understand mathematics and statistics: Study linear algebra, calculus, probability, and statistics to grasp the foundations of AI.
Master machine learning: Learn about various ML algorithms, techniques, and frameworks such as TensorFlow or PyTorch.
Gain practical experience: Work on real-world projects, participate in Kaggle competitions and build a portfolio to showcase your skills.
Specialize in AI subfields: Explore areas like natural language processing, computer vision, or reinforcement learning.
Continuous learning: Stay updated with the latest advancements and research in AI through online courses, tutorials, and academic papers.
What if I have more queries after this AI Course?
Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co
What if I miss a Artificial Intelligence Training class?
You will never miss a lecture at Edureka! You can choose either of the two options:
View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch.
Will I get placement assistance after this AI Course?
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
What online courses that are available on Edureka will help me to learn about Artificial Intelligence?
What is the Artificial Intelligence Complete Course fees?
The Artificial Intelligence Complete Course fee in India is INR 17995, and US is $449.
What is the Salary of an AI Engineer in India?
The salary of an AI engineer in India can vary depending on factors such as experience, skillset, location, industry, and the organization's size. On average, AI engineers in India can expect to earn a salary ranging from INR 5 lakh to INR 20 lakh per year. However, it's important to note that these figures are approximate and can vary significantly based on the factors mentioned earlier. Highly skilled and experienced AI engineers working in top tech companies or specialized fields may command higher salaries, potentially exceeding INR 20 lakh per year. Additionally, AI engineers with advanced degrees or certifications and those with expertise in specific subfields of AI, such as natural language processing or computer vision, may have better salary prospects. It's essential to refer to up-to-date salary surveys and resources for the most accurate and recent information on AI engineer salaries in India.
What kind of projects are included as part of the AI Course?
As part of the AI Course to become an AI engineer, you may work on various projects focusing on different aspects of AI. Some common project areas include:
Machine Learning: Projects involving training models for classification, regression, or recommendation systems. Examples could include building a spam email classifier or predicting house prices.
Natural Language Processing (NLP): Projects related to language understanding and generation, such as sentiment analysis, chatbots, or language translation systems.
Computer Vision: Projects that deal with image or video analysis, such as object detection, facial recognition, or autonomous driving systems.
Reinforcement Learning: Projects centred around training agents to make decisions in dynamic environments, like teaching a robot to navigate a maze or playing complex games.
Data Analysis: Projects involving exploratory data analysis, data preprocessing, and feature engineering, often using statistical techniques and visualizations.
These projects aim to provide hands-on experience in applying AI techniques to solve real-world problems and help you develop practical skills and understanding in the field of AI engineering.