Data Science (AI/ML)
Data Science (AI/ML)
Artificial Intelligence (AI) and Machine Learning (ML) are transformative technologies that are reshaping industries by enabling machines to perform tasks that typically require human intelligence. While AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart,” ML is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make decisions based on data.
Training Highlights:
- Fundamentals of Python Syntax and Structure
- Control Flow and Looping Constructs
- Introduction to Functions and Lambda Expressions
- Exception Handling in Python
- Advanced Exception Handling: Custom Exceptions, Logging, and Debugging
- Object-Oriented Programming Concepts in Python
- Advanced OOP: Inheritance, Abstraction, and Polymorphism
- Database Interaction with Python
- Data Manipulation with Numpy and Pandas
- Data Visualization with Matplotlib and Seaborn
- Introduction to Descriptive Statistics
- Introduction to Inferential Statistics
- Exploratory Data Analysis (EDA)
- Supervised Machine Learning I: Regression Techniques
- Supervised Machine Learning II: Classification Algorithms
- Advanced Machine Learning: SVM, PCA, and MLOps Projects
- Unsupervised Learning Techniques
- Deep Learning Basics: Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN) and Architectures
- Natural Language Processing (NLP) and Recurrent Models
- Capstone Projects in Machine Learning and AI
- Generative AI and Large Language Models
Data Science (AI/ML)
Technical FAQs & Mastery Modules
2. Machine Learning (ML)
ML is a subset of AI that focuses on the development of algorithms that allow computers to learn from data, identify patterns, and make decisions with minimal human intervention. ML systems improve their performance over time as they are exposed to more data.
3. Deep Learning
Deep Learning is a subset of ML that uses neural networks with many layers (deep neural networks) to model complex patterns in data. Deep learning has been particularly successful in tasks like image and speech recognition.
Module-1: Fundamentals of Python Syntax and Structure
- Identifiers
- Comments
- Indentation
- Statements
- Variables
- Primitive Data types
- Data types List
- Tuple Dictionary Sets
Module-2: Control Flow and Looping Constructs
- Standard Input and Output
- Operators
- If Statement For-Loop
- Nested For Loop
- While Nested For and IF
Module-3: Introduction to Functions and Lambda Expressions
- Introduction to User
- Defined Functions
- Functions
- Lambda Functions
Module-4: Exception Handling in Python
- Exception Handling
Module-5: Advanced Exception Handling: Custom Exceptions, Logging, and Debugging
- User defined exceptions
- Logging
- Debugging
Module-7: Advanced OOP: Inheritance, Abstraction, and Polymorphism
- OOPS
- Inheritance
- Abstraction
- Polymorphism
Module-8: Database Interaction with Python
- Python interaction with data bases
Module-9: Data Manipulation with Numpy and Pandas
- Numpy
- Pandas libraries of python
Module-10: Data Visualization with Matplotlib and Seaborn
- Matplotlib
- Seaborn etc
- Visualisation libraries
Module-11: Introduction to Descriptive Statistics
- Descriptive Statistics
Module-12: Introduction to Inferential Statistics
- Inferential statistics
Module-13: Exploratory Data Analysis (EDA)
- Exploratory Data Analysis
Module-14: Supervised Machine Learning I: Regression Techniques
- Machine learning algorithms
- Linear Regression
- Logistic Regression
Module-15: Supervised Machine Learning II: Classification Algorithms
- Machine learning Algorithms
- KNN
- Decision tree
- Random forest
- Boosting algorithms
Module-16: Advanced Machine Learning: SVM, PCA, and MLOps Projects
- SVM
- PCA
- Machine learning projects with MLOPS
Module-17: Unsupervised Learning Techniques
- Unsupervised Algorithms
Module-18: Deep Learning Basics: Artificial Neural Networks (ANN)
- Deep learning ANN :
- Single layered perceptron
- Multi layer Propagation
- Forward Propagation
- Back propagation
- Activation Functions
- Loss functions
- Optimizers
- Regularization techniques and weight initialization techniques
Module-19: Convolutional Neural Networks (CNN) and Architectures
- CNN basics
- Vanilla cnn
- Leenet
- Alex net
- VGG
- Resnet
- Inception net
- Google net
- Detection
- Segmentation techniques
Module-20: Natural Language Processing (NLP) and Recurrent Models
- NlP
- RNN
- LSTM
- Bidirectional lstm
- Text preprocessing techniques
- Encoder and decoder
- Transformers
- GPT
Module-22: Generative AI and Large Language Models
- Generative AI
- Large Language Models
- RAG --> Vector Data bases
- Hugging Face
- Prompt Engineering
- Lang chain
- Llama Index
Why Choose Data Science (AI/ML)
- 100% Real-Time and Practical
- Concept wise FAQs
- TWO Real-time Case Studies, One Project
- 24/7 LIVE Server Access
- Realtime Project FAQs
- Course Completion Certificate
- Placement Assistance
- Job Support