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- Jannilen Sebastian's Credentials
- Jannilen Sebastian's Wallet
- Cleaning Data in R
- R Classification & Clustering
- R Regression Methods
- R for Data Science: Data Visualization
- Linear Algebra & Probability: Advanced Linear Algebra
- Linear Algebra and Probability: Fundamentals of Linear Algebra
- Linear Regression Models: Introduction
- Data Exploration using R
- Importing & Exporting Data using R
- Python for Data Science: Advanced Data Visualization Using Seaborn
- Python - Manipulating & Analyzing Data in Pandas DataFrames
- NLP for ML with Python: NLP Using Python & NLTK
- NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn
- R Data Structures
- Data Driven Organizations
- Data Sources: Integration from the Edge
- Data Analysis Concepts
- Clustering, Errors, & Validation
- Math for Data Science & Machine Learning
- Deep Learning & Neural Network Implementation
- Machine Learning & Data Analytics
- Python - Pandas Advanced Features
- Supervised, Unsupervised & Deep Learning
- ML Algorithms: Machine Learning Implementation Using Calculus & Probability
- Machine & Deep Learning Algorithms: Regression & Clustering
- Linear Regression Models: Introduction to Logistic Regression
- Machine & Deep Learning Algorithms: Introduction
- Raw Data to Insights: Data Management & Decision Making
- Python - Using Pandas to Work with Series & DataFrames
- Machine & Deep Learning Algorithms: Data Preparation in Pandas ML
- Python - Using Pandas for Visualizations and Time-Series Data
- Linear Models & Gradient Descent: Managing Linear Models
- Linear Models & Gradient Descent: Gradient Descent and Regularization
- ML Algorithms: Multivariate Calculation & Algorithms
- Model Management: Building Machine Learning Models & Pipelines
- Python - Introduction to NumPy for Multi-dimensional Data
- Python - Advanced Operations with NumPy Arrays
- Python - Introduction to Pandas and DataFrames
- Python for Data Science: Basic Data Visualization Using Seaborn
- Applied Data Analysis
- Building ML Training Sets: Introduction
- Simplifying Regression and Classification with Estimators
- Linear Regression Models: Building Models with Scikit Learn & Keras
- Building ML Training Sets: Preprocessing Datasets for Classification
- Building ML Training Sets: Preprocessing Datasets for Linear Regression
- Linear Regression Models: Multiple & Parsimonious
- Data Communication & Visualization
- Machine Learning Introduction
- Using Data to Find Data: Data Discovery & Exploration
- Raw Data to Insights: Data Ingestion & Statistical Analysis
- Using Data to Find Data: Correction & Categorization
- Data Insights, Anomalies, & Verification: Handling Anomalies
- Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools
- Data Science Statistics: Applied Inferential Statistics
- Inferential Statistics
- Estimates & Measures
- Python Classes & Inheritance: Working with Inheritance in Python
- Python Classes & Inheritance: Advanced Functionality Using Python Classes
- Python Classes & Inheritance: Getting Started with Classes in Python
- Data Exploration
- Data Science Overview
- Data Filtering
- Data Gathering
- Data Integration
- Data Transformation
- Simple Descriptive Statistics
- Data Science Statistics: Using Python to Compute & Visualize Statistics
- Common Approaches to Sampling Data
- Python Classes & Inheritance: Introduction
- Excel with Python: Constructing Data Visualizations
- Excel with Python: Working with Excel Spreadsheets from Python
- Conditional Statements & Loops: Advanced Operations Using for Loops in Python
- Conditional Statements & Loops: While Loops in Python
- Functions in Python: Introduction
- Conditional Statements & Loops: The Basics of for Loops in Python
- Functions in Python: Gaining a Deeper Understanding of Python Functions
- Functions in Python: Working with Advanced Features of Python Functions
- Excel with Python: Performing Advanced Operations
- Data Structures & Algorithms in Python: Implementing Trees & Graphs
- Conditional Statements & Loops: If-else Control Structures in Python
- Data Structures & Algorithms in Python: Trees & Graphs
- Statistics & Data Visualization
- Data Structures & Algorithms in Python: Implementing Data Structures
- Data Structures & Algorithms in Python: Sorting Algorithms
- Data Structures & Algorithms in Python: Implementing Sorting Algorithms
- Finding & Analyzing Information with Formulas in Excel Microsoft<br>365 for Windows
- Managing Data in Excel Microsoft 365 for Windows
- Configuring Options & Settings in Excel Microsoft 365<br> for Windows
- Data Structures & Algorithms in Python: Fundamental Data Structures
- Python Requests: HTTP Requests with Python
- Working with Data in PivotTables in Excel Microsoft 365 for Windows
- Using Conditional Formulas in Excel Microsoft 365 for Windows
- Inserting PivotTables in Excel Microsoft 365 for Windows
- Complex Data Types in Python: Shallow & Deep Copies in Python
- Getting Started with Python: Introduction
- Complex Data Types in Python: Working with Dictionaries & Sets in Python
- Complex Data Types in Python: Working with Lists & Tuples in Python
- Data Structures
- Functions, Storage Classes, & Data Modifiers
- The C Preprocessor & Optimization
- Unions & File Management
- Operators, Conditional Statements, & Branching
- Common Functions
- Looping & Functions
- Basics, Data Types, Constants, & Variables
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