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AI Machine Learning Deep Learning Essentials (TTAI3005) Course
This 3-day instructor-led online AI, Machine Learning & Deep Learning Essentials (TTAI3005) Certification Training in India equips learners to understand the fundamental concepts of AI and acquire practical skills to work in the fast-evolving domain. This course is best for professional
TTAI3005
Interactive Live Online Sessions
Extensive Learning Materials
Real-World Case Studies
Course Overview
This AI, Machine Learning & Deep Learning Essentials (TTAI3005) Course is designed meticulously to give an overview of the fundamental concepts of artificial intelligence and methods of applying them. This training is suitable for anyone who wants to begin or advance their career in the area of artificial intelligence. It covers significant topics such as machine learning algorithms, deep learning architectures, standards in AI development, etc. With the help of live sessions, learners will be able to understand topics including data pre-processing, linear regression, clustering, and creation of neural networks.
Learners will also learn about specific domains of AI, including Natural Language Processing (NLP) and Computer Vision, which are crucial in real-world applications. Hands-on exercises will allow learners to implement theoretical concepts to create and deliver models in different contexts.
This course follows the TTAI3005 certification exam curriculum and equips the learners with particular topics and skills that are tested in the exam. After the course, candidates will be able to design fundamental models in AI, machine learning, and deep learning, handle data appropriately, and have a good understanding of the ethical use of AI solutions. This training prepares them for other certifications and suitable jobs in AI-related fields, such as data analysts, AI developers, and machine learning specialists.
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Course Objectives
- Learn about the basics of AI and the most common types of Machine Learning and Deep Learning adapted to real-life scenarios.
- Understand how to optimize data before providing it to the models to increase the chances of an accurate model.
- Discover primary machine-learning techniques like regression, classification, and clustering.
- Find out about the design and the functioning of the most superficial neural networks and deep learning models.
- Understand what natural language processing (NLP) is as a tool for text analysis and language-based activities.
- Learn about computer vision methods to allow the machine to analyze data from a picture or video.
- Find out key measures following which AI can be implemented correctly and legally.
- Learn how to build and evaluate predictive models in hands-on labs and practical sessions.
- Develop skills for solving problems with critical thinking by applying theoretical knowledge to practical scenarios presented in the interactive labs and case studies.
- Gain an understanding of instruments and techniques encompassing AI, such as TensorFlow and Python.
Audience
- Data scientists and analysts
- IT professionals
- AI and ML beginners
- Software developers
- Business managers
- Professionals preparing for certification
- AI enthusiasts seeking practical knowledge
Prerequisite
- Fundamental understanding of mathematics.
- Familiarity with Python programming, data handling concepts, and AI and ML applications.
- Work experience in analytical and problem-solving environments.
Course Outline
Basic Concepts of Deep Learning, Machine Learning, and AI
- Introduction to data science
- Modern strategies to analyze and use data
- Handling challenges in data processing
- Technological tools for data science
- Effective data science methods
- How does data science function in AI?
- What is the connection between AI, ML, and deep learning?
- Integrating scientific methods with data science
- Difference between data science and data engineering
- Ways to present data to data findings
- Documenting experiments
- Key responsibilities of data science team members
- Building and maintaining data science infrastructure
- Latest innovations, trends, and tools in the data science field
- Optimizing data science strategies to fit your industry
What is AI?
- Journey of AI development
- New inventions in data and hardware
- Advanced research and AI applications
- Understanding core AI terms and concepts
What is Machine Learning?
- Who uses ML and why?
- Key differences between AI and ML
- ML algorithms examples & applications
- Understanding technologies like Python and Spark
- Supervised and Unsupervised types of machine learning
- Classification in ML
- What is a regression in ML?
- Basics of clustering
- Using dimensionality regression to simplify data
- Ensemble techniques
Introduction to Deep Learning
- What is deep learning?
- Differences between deep learning, AI, and ML
- Who can benefit from using deep learning?
- Algorithm of deep learning
- Understanding modern technologies like Python, TensorFlow, Keras
Expert Systems and Their Roles
- Rules Systems for making the right decisions
- Feedback loops for enhancing system precision
- Applications of RETE algorithm
- Hands-on knowledge of expert systems
Understanding Neural Networks
- What do we mean by neural networks?
- Use of recurrent neural networks
- What are long and short-term memory networks?
- How do we use neural networks practically?
Role of NLP (Natural Language Processing)
- What is NLP?
- Semantic relationships
- Exploring Bigrams, Trigrams, and n-Grams concepts
- Root stemming and branching techniques
- Real-world applications of NLP
Audio, Video, and Image Processing Techniques
- What is image processing?
- How to apply identification methods?
- Methods for audio processing
- How to stream video?
- Significance of streaming video
- Practical examples of audio-video processing
Introduction to Sentiment Analysis
- How does sentiment help in emotional recognition?
- Major sentiment indicators
- How to do sentiment sampling?
- Using sentiment in algorithmic trading
- Analyzing election with sentiment data
Latest Tools and Technologies in the AI, ML, and Deep Learning Industry
- Big Data technologies such as Hadoop and Spark
- Python, NumPy, Pandas, and SciKit for data processing
- TensorFlow, Keras, and NLTK for machine learning
- NoSQL databases for managing unstructured data
- Drools for business rule administration
- libraries and frameworks
- cloud services supporting AI and ML applications
Ways to Implement AI and ML in Organizations
- How do we create a suitable technology stack for AI and ML?
- Forming an AI/ML team
- Combining AI/ML into existing procedures
- Building and applying best practices for team success
About The Certification
The AI, Machine Learning & Deep Learning Essentials (TTAI3005) Certification is an industry-recognized credential that delivers a strong understanding of artificial intelligence. With this certification, learners will simultaneously be introduced to the fundamental concepts of AI that are practical and theoretical. They will dive into machine learning algorithms, data preparation techniques, neural network fundamentals, and deep learning architectures. Natural language processing (NLP) and computer vision are other specialized areas of the course that explore and showcase how AI is applied in real-world situations.
The certification focuses on hands-on learning through interactive labs and exercises to ensure learners get practical knowledge. This will validate your AI expertise, adding value to your career as an AI developer, machine learning engineer, or similar role.
Additionally, it's a gateway to other advanced certifications and career opportunities in fields like autonomous systems, AI strategy design, and ethical AI deployment.
Choose Your Preferred Mode
Online Training
- 3 days Instructor-led Online Training
- Experienced Subject Matter Experts
- Approved and Quality Ensured Training Material
- 24*7 Leaner Assistance And Support
Corporate Training
- Customized Training Across Various Domains
- Instructor-Led Skill Development Program
- Ensure Maximum ROI for Corporates
- 24*7 Learner Assistance and Support
FAQ’s
What is the AI, Machine Learning & Deep Learning Certification Course?
It is a fundamental certification focused on equipping beginners and experts with a theoretical and hands-on understanding of AI, machine learning, and deep learning.
What key topics are included in this certification?
The curriculum's key topics include AI basics, machine learning, data preparation, neural networks, natural language processing, computer vision, and ethical AI usage.
Why is AI useful in today’s world?
AI is driving innovations in automation, forecasting, and decision-making processes in different fields, such as healthcare, finance, and transportation, making it an essential factor in technology.
Are there any prerequisites for programming required to join this course?
This course does not require advanced programming knowledge, although basic Python skills would be beneficial.
What opportunities does this certification open for me?
This course prepares you for job opportunities as an AI developer, machine learning engineer, data analyst, or in higher strategic and research positions in the AI fields.
What other certifications can I get after this course?
After this certification, learners can pursue more advanced AI certifications, such as TensorFlow Developer, Deep Learning Specialization, or AWS Machine Learning Specialty.
What are the advantages of this certification?
It validates an AI professional's base-level skills, increases chances of employment, and offers working knowledge across sectors.
In what ways does Vinsys help learners during the training?
Vinsys offers subject-matter expert trainers, highly engaging live sessions, practical demonstrations through workshops, and comprehensive examination simulation and preparation.
Is the course appropriate for beginners in Artificial Intelligence?
Yes, it is specifically created for the audience with little or no prior AI experience. It is also helpful for experienced professionals who want to acquire a basic understanding of the subject.
Which sectors are relevant to AI skills?
Some fields where AI is crucial are IT, healthcare, finance, retail, manufacturing businesses, and autonomous systems.
Why Vinsys
Seasoned Instructors
Official Vendor Partnerships
Authorized Courseware
3,000+ Courses & 2,000+ Modules
In Synch with Tech-advancements
Customizable Blended Learning Options
Reviews
I have just joined the AI Machine Learning Deep Learning Essentials course in Vinsys, and it was really a great learning session. The course was well organized and started with an introduction to AI and then went to the basics of deep learning, neural networks, and so on. All the trainers were professionals and ensured each topic was well understood, especially for those new to the profession. I also enjoyed the hands-on labs as everything seemed very good. I have learned a lot about AI from this course.
Prachi ParihaarAI professionals
This course delivered by Vinsys was good for me as it helped me build a strong foundation in AI and deep learning. The trainers were professionals, friendly, and always ready to respond to questions. I particularly liked how they described machine learning algorithms and how one can use them in practice. The learning materials were great and the support from the Vinsys team was impressive.
Sakshi BhardwajSoftware developers
As I had expected, the AI Machine Learning Deep Learning Essentials course at Vinsys was quite useful. It included all the basic areas of interest. The trainers ensured that the sessions were very practical, and they provided real-life examples of whatever they were teaching. Vinsys ensured that the learning process was quite a pleasant experience. I will for sure come back for more classes!
Akash SiwachTechnology enthusiasts
I had a great experience taking this course at Vinsys, it was one of the best decisions I made for my career. The training was comprehensive, going way beyond theory, but showing how to put AI and deep learning models into practice. The instructors offered insights from the industry. Vinsys also offered great help throughout the course including questions and even extra resources. I am excited to teach them more about other advanced topics in AI with them next!
Shivani ChawlaBusiness analysts
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