Machine Learning with AI

Machine learning and AI training involve the process of teaching computer systems to perform tasks without explicitly programming them. Instead, these systems learn from data and improve their performance over time through experience.

Requirements:

•  Programming Skills
•  Mathematics and Statistics
•  Knowledge of Machine Learning and AI Concepts
•  Data Handling and Analysis
• Familiarity with Machine Learning Algorithms
•  Experience with Neural Networks and Deep Learning
•  GPU Computing
• Tools and Libraries
•  Problem-Solving and Analytical Thinking
• Continuous Learning

It's important to note that machine learning and AI training require expertise in mathematics, statistics, programming, and domain knowledge. The success of the training process heavily depends on the quality and representativeness of the data, the choice of model, and the skill of the practitioner in designing and optimizing the learning process.

Curriculum:

• Module 1: Introduction
• Overview
• Introduction tomachine learning and AI
• What is Data Structures
• Role of machine learning and AI Developers
•  Data collection:
• Installation
• Set up a programming environment
• Install libraries and frameworks
• Additional libraries and frameworks
• Integrated Development Environments (IDEs)
• Module 2: Architecture, Scenarios, and Admin Tools Machine Learning, AI
• Architecture & scenarios
•  Data Acquisition
• Feature Engineering
• Model Training
•  Model Deployment
• Infrastructure
• Image and Object Recognition
• Model Training
•  Natural Language Processing (NLP)
• Recommender Systems
• Model Management
• Model Monitoring
• Data Management
• Experimentation and Collaboration
• Module 3: Preprocessing Machine Learning, AI .
• Variables
• Data Cleaning
• Data Transformation
• Data Integration and Selection
• Data Splitting
• Module 4: Supervised Learning
• Data Cleaning
• Data Transformation
• Data Integration and Selection
• Data Splitting
• Cross-Validation
• Module 5: Unsupervised Learning Machine Learning, AI
• Unlabeled Data
• Clustering
• Dimensionality Reduction
• Anomaly Detection
• Association Rule Learning
• Module 6: Model Selection and Evaluation Machine Learning, AI
• Reading from and writing to files
• Working with different file formats (text files, CSV, JSON)
• Module 7: Object-Oriented Programming Machine Learning, AI
• Introduction to OOP concepts (classes, objects, inheritance)
• Implementing classes and objects in Machine Learning, AI
• Encapsulation, inheritance, and polymorphism Machine Learning, AI
• Module 8: Additional Topics Machine Learning, AI (Optional)
• Deep Learning
• AutoML (Automated Machine Learning)
• Time Series Analysis
• Bayesian Machine Learning
• Transfer Learning
• Module 9: Advanced Machine Learning, AI Concepts
• Deep Generative Models
• Transfer Learning in Deep Learning
• Context Managers
• Metaprogramming
• Concurrency and Parallelism
• Asynchronous Programming
• Advanced Data Structures
• Functional Programming
• Debugging and Profiling
• Advanced I/O Operations
• Module 10:Prerequisites
• Proficiency in at least one programming language is essential. Python is widely used in the machine learning and AI community due to its rich ecosystem of libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
• Understanding concepts like variables, data types, loops, conditionals, functions, and file handling is crucial
• Module 11: Placement Guide
• Tips to clear an Interview
• Common Interview questions and answers
• Machine Learning, AI Interview Questions and Answers
• Resume Building Guide
• Career roadmap and certifications
• Attempt for Machine Learning, AICertification Exam
• Start applying for Jobs