LMS - EuclideanLab

EuclideanLab

- Introduction
- Variables, Data Types and Operators
- Control Flow Statements
- Function and Parameters
- Exception Handling And File Handling
- String Formatting and Comprehension
- Pandas
- Numpy
- Regular Expressions
- Seaborn
- Matplotlib
- Class And Object
- Introduction to Statistics
- Data Collection, Data Preparation and Inference
- Sampling, Types of Sampling and Variance
- Data Types
- Descriptive Statistics
- Data Distributions
- Probability Theory
- Central Limit Theorem
- Confidence Interval
- Hypothesis Testing
- Correlation and Covariance
- Data Preparation
- Introduction to Database
- Understanding The Architecture of the Database
- Data Types in SQL
- Command Types in SQL
- Operators in SQL
- Constraints in SQL
- Joins
- Sub Queries
- Index, Views and Union
- Common Table Expression
- Window Functions
- Triggers
- Prepare The Data
- — Get Or Connect To Data
- — Profile and Clean The Data
- — Transform and Load The Data
- Model The Data
- — Design And Implement The Data Model
- — Create Model Calculation Using Data Model
- — Optimize Model Performance
- Visualize And Analyze The Data
- — Enhance Reports For Usability And Storytelling
- — Identify Patterns And Trends
- Manage And Secure Power BI
- — Create And Manage Workspaces and Assets
- — Secure and Govern Power BI Items
- Mathematical Foundation
- Unsupervised Learning Techniques
- Hierarchical Clustering
- k-Means Clustering
- Dimension Reduction - PCA, SVD
- Association Rules
- Supervised Learning Techniques
- Linear Regression
- Logistic Regression
- Decision Tree
- Natural Language Processing
- Basics Of NLP
- Text Classification
- Sentiment Analysis
- Deep Learning
- Basics Of Neural Network
- Convolutional Neural Network
- Recurrent Neural Network
- Artificial Neural Network
- Convolutional Neural Network
- Region Based CNN (RCNN)
- Fast RCNN
- Faster RCNN
- Mask RCNN
- Cascade RCNN
- Long Short-Term Memory (LSTM)
- Transformers
- Generative Adversarial Networks
- Reinforcement Learning

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