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Machine Learning and AI Fundamentals
Empower Your Future with Machine Learning and AI Fundamentals: Unlocking the Potential of Tomorrow’s Technology Today.
Enroll now for early access of e-LMS
Online/ e-LMS
Self Paced
Advanced
12 Weeks
About
The Machine Learning and AI Fundamentals course offers a comprehensive introduction to the core principles of machine learning and artificial intelligence. Designed for AI professionals, this course covers supervised and unsupervised learning techniques, neural networks, deep learning, and natural language processing. Through a blend of engaging video lectures, interactive coding sessions, and real-world projects, participants will gain hands-on experience and practical skills, preparing them to excel in the AI industry.
Aim
To equip AI professionals with essential machine learning and AI skills, enabling them to innovate and excel in their careers.
Program Structure
Introduction to Machine Learning and AI
- Overview of Machine Learning and AI.
- Historical Context and Evolution.
- Key Terminologies and Concepts.
Supervised Learning
- Linear Regression and Classification.
- Decision Trees and Random Forests.
- Support Vector Machines (SVM).
- Model Evaluation and Performance Metrics.
Unsupervised Learning
- Clustering Algorithms (K-means, Hierarchical).
- Dimensionality Reduction Techniques (PCA, LDA).
- Anomaly Detection.
Neural Networks and Deep Learning
- Introduction to Neural Networks.
- Deep Learning Fundamentals.
- Convolutional Neural Networks (CNN).
- Recurrent Neural Networks (RNN).
- Transfer Learning.
Natural Language Processing (NLP)
- Text Preprocessing and Tokenization.
- Sentiment Analysis.
- Topic Modeling.
- Sequence Models and LSTM.
- Transformer Models and BERT.
Practical Machine Learning
- Working with Python and Jupyter Notebooks.
- Using TensorFlow and Keras for Model Building.
- Implementing PyTorch for Advanced Deep Learning.
- Utilizing scikit-learn for Machine Learning Algorithms.
Participant’s Eligibility
- Senior undergraduates and graduate students in Computer Science and related fields.
- Professionals in IT, data science, and software development looking to enhance their AI skills.
Program Outcomes
- Gain a strong foundation in machine learning and AI principles.
- Develop proficiency in supervised and unsupervised learning techniques.
- Acquire hands-on experience with neural networks and deep learning models.
- Learn to implement natural language processing applications.
- Master the use of key machine learning libraries and frameworks such as TensorFlow, Keras, PyTorch, and scikit-learn.
- Complete real-world projects that demonstrate your ability to apply machine learning concepts.
- Earn a certificate of completion recognized by industry leaders, enhancing your professional credentials.
Fee Structure
Standard Fee: INR 14,998 USD 258
Discounted Fee: INR 7499 USD 129
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of CurrenciesBatches
Live
Certificate
Program Assessment
Certification to this program will be based on the evaluation of following assignment (s)/ examinations:
| Exam | Weightage |
|---|---|
| Mid Term Assignments | 50 % |
| Project Report Submission (Includes Mandatory Paper Publication) | 50 % |
To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- Machine Learning Engineer: Design and implement machine learning models and algorithms.
- Data Scientist: Analyze and interpret complex data to help companies make informed decisions.
- AI Researcher: Conduct research to advance the field of artificial intelligence.
- AI Developer: Build and deploy AI applications and systems.
- NLP Specialist: Develop solutions for natural language processing tasks such as text analysis and speech recognition.
- Business Intelligence Analyst: Use AI and machine learning to drive business strategies and optimize operations.
Enter the Hall of Fame!
Take your research to the next level!
Achieve excellence and solidify your reputation among the elite!
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Recent Feedbacks In Other Workshops
The course was more theoretical than practical.
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