Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Course Overview
The Artificial Intelligence & Machine Learning Bootcamp by the Centre for Technology & Management Education (Caltech) via Simplilearn is an online Bootcamp. The course is designed to provide students with an understanding of the meaning, purpose, stages, applications, scope, and effects of Artificial Intelligence and Machine Learning. The Artificial Intelligence & Machine Learning Bootcamp certification syllabus covers key concepts of Machine Learning, Deep Learning, Statistics, Data Science with Python, Natural Language Processing, and Reinforcement Learning.
With Artificial Intelligence & Machine Learning Bootcamp online course, students will gain a deeper understanding of data science processes, data exploration, data visualization, data wrangling, hypothesis building, and testing. They will also acquire expertise in mathematical computing using the NumPy and scikit-learn package while mastering the concepts of supervised and unsupervised learning. Students and professionals from different industries and backgrounds who are eager to develop AI and ML expertise can join the Artificial Intelligence & Machine Learning Bootcamp certification course.
The Highlights
- Caltech's Academic Excellence
- Exclusive visit to Caltech’s Robotics Lab
- Hands-on Experience
- Certificate of completion
Programme Offerings
- masterclasses
- Caltech’s Robotics Lab
- Online convocation by Caltech CTME Program Director
- Access to hackathons
- Seamless access to integrated labs
- 25+ hands-on projects
- LevelUp Sessions
- Simplilearn’s Career Assistance
- Caltech CTME Circle Membership
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Caltech CTME |
Eligibility Criteria
Candidates must have a bachelor’s degree in a relevant discipline.
Work Experience
Aspirants are required to have 5+ years of formal work experience.
Certification Qualifying Details
- After successfully completing the Artificial Intelligence & Machine Learning Bootcamp certification course, students will receive a certificate from Caltech and an IBM certificate for IBM courses.
What you will learn
By pursuing the Artificial Intelligence & Machine Learning Bootcamp certification course, students will be able to:
- Gain an in-depth understanding of Artificial Intelligence and Machine Learning.
- Exposed to tools such as Keras to create computer vision applications.
- Become familiar with generative adversarial networks (GANs).
- Comprehend and appreciate Deep Learning and its applications.
- Acquire know-how of Neural Networks, and traverse the layers of data abstraction to understand data like never before.
Who it is for
- The Artificial Intelligence & Machine Learning online course is designed for professionals from a variety of industries and backgrounds.
- Students and professionals such as AI Engineers and ML Engineers who wish to advance their careers in the field of AI and ML can enrol in this course.
Admission Details
The admission process to the Artificial Intelligence & Machine Learning certification course includes the following steps:
Step 1: Filling and submission of Application Form
Step 2: Application Review
Step 3: Admission Confirmation
Application Details
Candidates can apply to this Bootcamp by following the below-mentioned instructions:
- Visit the official website - https://www.simplilearn.com/ai-machine-learning-bootcamp
- Complete the application and submit
- The application will be reviewed by a panel of admissions counselors
- Qualified candidates will be offered admission to the course
- Pay the course fee to block your seat
The Syllabus
- Understand procedural and OOP concepts
- Explore the advantages of Python
- Install Python and IDE
- Learn the usage of Jupyter Notebook
- Implement identifiers, indentations, and comments
- Identify Python data types, operators, and strings
- Comprehend different types of Python loops
- Explore variable scope in functions
- Explain the characteristics of OOP
- Describe methods, attributes, and access modifiers
- Learn essential data science concepts
- Understand fundamental Python elements: strings, Lambda functions, lists
- Explore tools and libraries: Utilizing NumPy for arrays and linear algebra
- Understanding statistical concepts: central tendency, dispersion, skewness
- Learn methods for hypothesis testing: Z-test, T-test, ANOVA
- Focus on leveraging pandas for efficient data manipulation
- Develop data visualization skills with Matplotlib, Seaborn, Plotly, Bokeh
- Understand the machine learning pipeline
- Explore supervised learning: regression, classification
- Practice unsupervised learning: clustering, ensemble modeling
- Assess frameworks: TensorFlow, Keras
- Apply PyTorch for recommendation engines
- Understand of machine learning methodologies and their use
- Distinguish between deep learning and machine learning
- Explore neural networks, propagation, TensorFlow 2, Keras, and methods for improving performance
- Understand the interpretation of models
- Learn about CNNs, transfer learning, object detection, RNNs, autoencoders
- Utilize PyTorch to construct neural networks
- Establishing a robust understanding of deep learning fundamentals
- Create and enhance models through Keras and TensorFlow
- Latest AI insights: Gain exposure to Generative AI, prompt engineering, and ChatGPT
- Applied Learning : Acquire hands-on exposure of these technologies
- Utilizing AI: Learn the effective application of these technologies
- Emphasis on prompt engineering: Grasp its influence on producing precise outputs
CB AI: Academic Masterclass
CB AI: Project Hours
CB AI: Office Hours
CB AI: ADL & Computer Vision
- Attain high-level proficiency in computer vision and deep learning
- Gain comprehensive understanding and hands-on skills
- Explore subjects, including image formation, CNNs, object detection, and segmentation
- Understand generative models, OCR, and distributed computing
- Explore Explainable AI (XAI) methodologies
- Develop deployment strategies for deep learning models
- Learn tackling intricate vision-related challenges
- Build and deploy deep learning models
CB AI: NLP and Speech Recognition
- Leverage machine learning algorithms to analyze natural language data
- Investigate feature engineering techniques
- Explore methods for generating natural language
- Comprehend automated speech recognition
- Learn speech-to-text and text-to-speech
- Construct voice assistance devices
- Develop skills for Alexa devices
CB AI: Reinforcement Learning
- Explore fundamental RL concepts
- Tackle RL issues using Python and TensorFlow
- Learn theoretical RL principles
- Get hands-on practice with RL algorithms
- Develop problem-solving strategies
- Gain expertise in various RL applications
- Implement RL in different scenarios effectively
CB AI: Advanced Generative AI
- Understand the crucial contribution of transformers in contemporary AI
- Identify various types of generative models: VAEs, GANs, transformers, and autoencoders
- Determine appropriate contexts for different model types
- Understand the significance and efficacy of attention mechanisms
- Examine the architectural intricacies of GPT and BERT
- Compare the objectives of well-known generative AI models