AI & Machine Learning for Cloud Deployment Certification Course
This comprehensive 6-month program equips you with the knowledge and skills to build, train, and deploy machine learning models on cloud platforms. The course starts with the fundamentals of AI and Machine Learning and progresses to hands-on experience deploying models in the cloud. Delivered over a 12-week period (2 hours daily, 5 days per week), the program offers a blend of theory, coding exercises, and cloud-based labs.
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BOX – Maximise your expertise in AI and Machine Learning with our leading-edge course, harnessing the latest advancements in technology.
- Level : Beginner
- Flexible schedule
- Trainer from MNC’s
SELECTION BEGINS
Apr-May 2024
LAST DATE TO ENROL
1st July 2024
TENTATIVE START DATE
15th July 2024
COHORT DURATION
26 WEEKS
PROGRAM HIGHLIGHTS
6 months of immersive Live lectures
Learn from top-tier, industry-leading technology experts.
Earn your certification from the prestigious Indian Institute of Code Network and Security (IICNS).
Master the essential AI/ML tools including Python, PyTorch, Keras, NumPy, Jupyter Notebook, and Pandas within the AI/ML curriculum
ROLES AND CAREER PATH IN AI/ML
WHAT YOU’LL LEARN
Core AI & ML Concepts: Understand fundamental concepts of AI, including types and applications, and learn basic Python programming for ML.
Cloud Computing Basics: Gain knowledge of cloud computing models and major providers, along with hands-on experience in setting up and exploring cloud accounts.
Data Preparation & Model Building: Acquire techniques for data exploration, preprocessing, and feature engineering, and delve into supervised and unsupervised learning algorithms.
Cloud Deployment & Monitoring: Learn to train and deploy ML models on cloud platforms, monitor model performance, and manage versions effectively for production use.
COURSE STRUCTURE
- Introduction to Artificial Intelligence (AI): Core concepts of AI, types of AI (Machine Learning, Deep Learning), applications of AI.
- Introduction to Machine Learning (ML): Supervised vs. Unsupervised Learning, common ML algorithms (Linear Regression, Decision Trees)
- Introduction to Python Programming: Learn basic Python syntax, data structures, control flow for working with ML libraries. Hands-on labs with coding exercises in Python.
- Introduction to Cloud Computing: Understand cloud concepts (IaaS, PaaS, SaaS), major cloud providers (AWS, Azure, GCP). Hands-on labs setting up cloud accounts and exploring basic functionalities.
- Data Understanding & Exploration: Techniques for understanding data (descriptive statistics, visualization), identifying missing values, outliers. Hands-on labs with data exploration tools in Python (pandas, matplotlib).
- Data Preprocessing: Techniques for cleaning and preparing data (handling missing values, normalization, feature scaling). Hands-on labs with data preprocessing libraries in Python (scikit-learn).
- Feature Engineering: Feature selection techniques, creating new features from existing ones to improve model performance. Hands-on labs with feature engineering techniques in Python (scikit-learn).
- Introduction to Supervised Learning Algorithms: In-depth exploration of common supervised learning algorithms (Linear Regression, Logistic Regression, Decision Trees, Random Forests). Hands-on labs with implementing these algorithms in Python using scikit-learn library.
- Introduction to Unsupervised Learning Algorithms: K-Means Clustering, Principal Component Analysis (PCA). Hands-on labs with implementing these algorithms in Python using scikit-learn library.
- Model Selection & Evaluation: Techniques for selecting the best model (cross-validation, hyperparameter tuning). Evaluating model performance using appropriate metrics (accuracy, precision, recall). Hands-on labs with model selection and evaluation techniques in Python.
- Introduction to Cloud Machine Learning Services: Explore cloud-based machine learning platforms offered by major providers (AWS SageMaker, Azure Machine Learning Service, Google Cloud AI Platform). Hands-on labs setting up and exploring these services.
- Training & Deploying Models on Cloud: Learn how to train your machine learning models on cloud platforms. Hands-on labs with training and deploying models using chosen cloud platform service.
- Model Monitoring & Versioning: Techniques for monitoring model performance in production, version control for managing different versions of your model. Hands-on labs with model monitoring and versioning tools available on chosen cloud platform.
Choose one out of the following based on your preference
- Deep Learning Fundamentals: Introduction to Deep Learning architectures (Artificial Neural Networks, Convolutional Neural Networks), their applications in computer vision and natural language processing. Hands-on labs with building simple Deep Learning models using libraries like TensorFlow or PyTorch (optional).
- Case Studies & Project: Work on a capstone project applying the learned concepts to build, train, and deploy a machine learning model on a cloud platform. Analyze results and iterate on your model.
LEARNING OUTCOME
Introduction to AI & Machine Learning: Understand core AI concepts, including supervised and unsupervised learning, and their applications in real-world scenarios.
Python Programming for ML: Learn essential Python syntax and data structures needed for implementing machine learning algorithms and working with ML libraries.
Cloud Computing Fundamentals: Explore cloud concepts such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), and gain hands-on experience with major cloud providers.
Model Deployment on Cloud: Acquire skills to train and deploy machine learning models on cloud platforms, ensuring scalability, reliability, and efficient monitoring of model performance.
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Frequently
Asked Question
An AI ML course is designed to provide comprehensive training in artificial intelligence and machine learning technologies, covering topics such as data preprocessing, predictive modeling, deep learning, natural language processing, and computer vision. The course aims to equip learners with the skills and knowledge needed to develop AI-powered applications and solutions.
The duration of an AI ML course is typically 6 months. This timeframe allows for comprehensive coverage of foundational concepts, hands-on practical exercises, projects, and in-depth exploration of advanced topics in artificial intelligence and machine learning.
Many AI ML courses offer career services, workshops, and resources to help students prepare for job interviews, build their resumes, develop professional networks, and explore career opportunities in the field. Additionally, participating in online coding challenges, contributing to open-source projects, and pursuing internships can enhance your practical skills and marketability to potential employers.
While a background in computer science or programming can be beneficial, it is not always a strict requirement for enrolling in an AI ML course. Many introductory courses are designed to accommodate students from diverse academic backgrounds and provide foundational knowledge before delving into more advanced topics.
Artificial intelligence (AI) is a broader field that encompasses the development of intelligent systems capable of performing tasks that typically require human intelligence. Machine learning (ML) is a subset of AI focused on developing algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed.
AI ML courses are often structured to include a combination of lectures, hands-on labs, projects, case studies, and interactive discussions to cater to different learning styles. This multimodal approach allows students to engage with the material in ways that best suit their individual preferences and learning needs.
Yes, many AI ML courses offer specialization tracks or elective modules that allow students to focus on specific areas of interest within the field, such as natural language processing, computer vision, deep learning, reinforcement learning, or healthcare informatics, among others.
AI ML courses are regularly updated to incorporate the latest advancements, research findings, and industry trends in the field. Faculty members often engage in ongoing professional development, attend conferences, and collaborate with industry partners to ensure that course content remains current and relevant.
Yes, many AI ML courses provide opportunities for students to collaborate with peers on group projects, participate in online forums and discussion groups, attend guest lectures by industry professionals, and network at conferences and workshops to build connections within the AI ML community.
AI ML courses often include practical exercises, projects, and internships that allow students to apply their knowledge and skills to real-world problems and scenarios. These hands-on experiences help students develop practical problem-solving abilities and gain insights into the challenges and opportunities in the field.
