Artificial Intelligence (AI) Course Training
Kode Campus presents to you a comprehensive up-to-date Artificial Intelligence certification program. This course will empower you with job-relevant skills and power you ahead in your career.
With this course, learn artificial intelligence by understanding key AI concepts, studying natural language processing, reinforcement learning, supervised and unsupervised learning, predictive analytics, deep neural networks, image processing, the human brain, and more today! By the end of the course, you'll be able to design your own intelligent systems using machine learning libraries, and artificial intelligence principles.
This course is suitable for everyone, from those just learning to code to experienced machine learning practitioners.
What you'll learn
- Understand the basics of AI and terms like Machine Learning, Deep Learning, and Neural Networks
- Explore practical application of AI, its strengths, limitations, and ethical issues surrounding the technology. Learn how to develop an AI application plan for businesses.
- Master the essential concept of Python programming. Learn how to write Python scripts and perform data analysis. Enable Natural Language Processing (NLP) with RNN's and implement Deep Learning solutions with CNN's.
- Understand neural network architectures, supervised and unsupervised learning models, including linear regression, logistic regression with a neural network mindset, Decision Tree Learning, KNN, K-Means, Hierarchical Clustering.
- Build and deploy Machine Learning powered applications with the thorough knowledge of AI frameworks TensorFlow and Keras.
- Deal with unstructured data types such as images, videos, text, etc.
Humans and animals display natural intelligence, involving consciousness and emotionality. While it's not possible for machines to portray such natural intelligence, machines can be programmed to simulate some aspects of human intelligence, using the science of Artificial Intelligence.
A lot of artificial intelligence systems are powered by machine learning. Some of them may be powered by deep learning.
According to Google's AI researcher Francois Chollet, intelligence isn't the skill itself, but it's the system's ability to learn new things, generalize its knowledge, and apply it to unfamiliar scenarios.
AI's traditional goals include reasoning, knowledge representation, planning, learning, prescription, NLP (natural language processing), and the ability to move and manipulate objects. To develop AI systems, a cross-disciplinary approach involving mathematics, computer science, linguistics, psychology, and more is required.
There are broadly two types of AI.
Weak AI (or Narrow AI):These AI systems are designed to solve a single problem and focus on performing a single task extremely well. They're called narrow as these systems can only learn or be taught to do well-defined tasks.
A few examples of Narrow AI include:
- Google search
- Siri, Alexa, and other personal assistants
- Image recognition software
- Website chatbots
- Recommendation engines behind Amazon, Netflix and Spotify
Strong AI (or General AI):Strong AI systems are complex and complicated systems capable of carrying out human-like tasks. While movies like Westworld, Star Trek, or Terminator have shown far beyond what's possible with present AI technologies, mentioned below are some fields where strong AI is being applied.
- Self-driving cars
- Disease mapping and prediction tools
- Manufacturing and drone robots
- Personalized healthcare treatment recommendations
- Robots capable of performing operations and surgeries
There is another AI type, called Artificial Super Intelligence (ASI), that would surpass all human capabilities. This AI would be able to make rational decisions ( and be able to act on them), and even things like making better art and building emotional relationships.
Artificial Intelligence growth has been primarily due to 3 reasons:
- Variety and Volume of digital data being produced every day, and the ability to capture it
- Cheaper and powerful computational processing availability
- Affordable data storage and Cloud Technology
Machine learning and deep learning are parts of the Artificial Intelligence domain. Machine learning is a subset of AI while deep learning itself is a subset of Machine learning itself.
Machine learning is all about giving machines the ability to learn on their own. It allows computer algorithms to automatically improve and get more accurate with experience.
Deep learning involves artificial neural networks and algorithms which learn from large amounts of data. Inspired by the human brain, these deep learning algorithms perform a task repeatedly and tweak it continually to improve the outcome.
To have a great career and earn a handsome salary! You want that, right?
AI jobs now account for an average of 15-20% of jobs in most companies, with more than 130 million roles available across all major sectors. Top companies like Amazon, Microsoft, Google, Nokia, and others continually recruit AI talent.
Listed below are a few reasons why you should learn Artificial Intelligence:
- AI is witnessing explosive growth. Some estimates suggest that the AI market will contribute as much as $15.7 trillion to the world economy by 2030. From Healthcare to finance, industries from all sorts of domains are looking to apply AI (and machine learning) to improve their efficiency or personalize their marketing campaigns, creating huge demands for data-skilled professionals. But there is a huge gap between demand and supply, with only a few skilled workers to meet the ever-growing demand.
- Learning AI will expand your problem-solving skills, a skill that's not useful for the professional world, but also in everyday life as well. Develop a better understanding of systems and tools that you interact with on a daily basis.
- Artificial Intelligence is a lucrative career option with an abundance of high-paying job opportunities (with the average package of an AI professional ranging between $100,000 to $150,000 in the USA and 14 - 15 lacs in India)
- You can generate some side income after learning AI (with things like Freelancing, an informative blog/YouTube channel, selling a data science course, or creating something innovative with your data knowledge)
- Get to make the world a better place with AI-powered solutions. Get to work on cutting-edge AI applications, like the Google Self Driving Car, or IBM's Watson. Develop and improve farming solutions that provide information about the weather, fields, and soil to farmers and help them make a profit. Or Improve online recommendation systems. Whichever field you work in, your AI works can make a real difference to the world.
No matter what your background is, you can take this AI course. You just need to be passionate about numbers, and love challenging problems.
But your journey to becoming a successful AI (or data) scientist would be much easier if:
- - You have a background in analytical disciplines such as mathematics, physics, computer science, or engineering.
- - You love coding and have a basic understanding of programming languages.
- - You are patient enough to keep working on the project even when it seems to have hit a roadblock.
- - Most comprehensive and well-structured course covering basics to advanced topics, allowing you to master the complete niche.
- - Certified Trainers with extensive real-time experience in the Data Science domain and an immense passion for teaching.
- - Top-notch course with a perfect blend of theory, case studies, and capstone projects, along with an assignment for every taught concept.
- - 100% Job Placement assistance. Frequent mock interviews to evaluate and improve your knowledge and expertise. Facilitation of interviews with various top companies. Help in building a great resume, optimizing LinkedIn profile, and improving your marketability.
AI development is possible in two domains - engineering and programming. Listed below are some of the leading AI-based career options you can break into after completing the Artificial Intelligence course:
- AI Engineer
- AI Data Analyst
- Big Data Engineer
- Big Data Engineer/Architect
- Data Mining Analyst
- Data Scientist
- Business Intelligence Developer
- Machine Learning Engineer
- Product Manager
- Robotic Scientists
- UX Designers
- Video Game Programmer
- Software Engineer
- Train,Test & Validation Distribution
- ML Strategy
- Computation Graph
- Evaluation Metric
- Human Level Performance
- Logistic Regression
- Gradient Descent
- Decision Tree
- Random Forest
- Bagging & Boosting
- Hierarichal Clustering
- Basic Programming
- NLP Libraries
- Sampling & Sampling Statistics
- Hypothesis Testing
- Vector Operation
- Deep Learning Importance [Strength & Limiltation]
- SP | MLP
Feed Forward & Backward Propagation
- Neural Network Overview
- Neural Network Representation
- Activation Function
- Loss Function
- Importance of Non-linear Activation Function
- Gradient Descent for Neural Network
- Train, Test & Validation Set
- Vanishing & Exploding Gradient
- Bias Correction
- RMS Prop
- Learning Rate
- Scikit Learn
- Spacy & Gensim
- Data Cleaning
- Data Preprocessing
- Image Transformation
- Noise Removal
- Correlation & Convolution
- Edge Detection
- Non Maximum Suppression & Hysterisis
- Fourier Domain
- Video Processing
Speech Data Analytics
- Image Feature
- Detection & Classification
- Computer Vision
- Why Convolution
Deep Convolution Model
- Case Studies
- Classic Networks
- Open Source Implementation
- Transfer Learning
- Object Localization
- Landmark Detection
- Object Detection
- Bounding Box Prediction
- What is Face Recognition
- One Shot Learning
- Siamese Network
- Triplet Loss
- Face Verification
- Neural Style Transfer
- Deep Conv Net Learning
- Why Sequence Model
- RNN Model
- Backpropogation through time
- Different Type of RNNs
- Biderectional LSTM
- Deep RNN
- Word Embedding
- Negative Sampling
- Elmo & Bert
- Beam Search
- Attention Model
- Autoencoders & Decoders
- Adversial Network
- Active Learning
- Q Learning
- Exploration & Exploitation
|AI Live Project Online Training||15/03/2021||8:00 am|
|AI Live||25/03/2021||10:00 am|
Listed below are the five most popular algorithms that all AI scientist should know (we cover all of these):
- Logistic Regression
- Naive Bayes
- K-Nearest Neighbours
- Support Vector Machines
- Random Forest
Yes! This course covers all the widely used popular Neural Networks. We also make sure to update our curriculum if new neural algorithms get developed and train our students on the same.
No! Just a basic laptop should be sufficient for most of your personal AI projects.
Here are a few datasets sources you can rely on:
- Non-profit research group websites
We’ll provide you with more sources for getting datasets in this comprehensive AI course.
Our team has compiled a list of the best data science resources including study materials, cheat sheets, data sets, videos, which you get access to when you join our course.
Kode Campus has its dedicated Placement Assistance Team(PAT). The team helps you in all the aspects of securing your dream job, from improving your marketability to conducting mock interviews.
NO! Our assistance program will only maximize your chances of landing a successful job as the final selection decision is always dependent on the recruiter.
Mohammad Aazam27 Oct 2019
"Kode Campus's AI program is brilliantly organized and was easy to follow. Though I was a beginner when I opted for the course, still I was able to grasp the core concepts easily. I'm happy as I have been able to significantly expand my portfolio after joining the course!"
Sourav Ganguly2 May 2019
"As an AI professional, you must know right from organizing and selecting the data to build and test your own models. I'm glad that this course has allowed me to master all the AI aspects. What I also loved was that the trainer gave some great tips about data-based careers."
Jasmeet Singh 2 Dec 2019
"Great course and delivers what it promises. I'm a computer engineer and decided to opt for this course to build my AI skills. One thing I want to clear to all course takers is that you need some knowledge of coding before deciding to opt for this course. Knowledge of TensorFlow would be beneficial as many projects in the course use a method from that package."
- Fully Programming
- Help Code to Code
- Free Trial 7 Days
- Unlimited Videos
- 24x7 Support
- Student Enrolled:1740
- Duration:60 hours
- Skill Level:Beginner