Data Science and Machine Learning
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Course Overview
Unlock the power of Data with Our 5 Month Data science Program and make data driven decision. Master data exploration, preprocessing, and visualization, Machine Learning using both code and no-code tools.
Course Curriculum
1 Subject
Data Science and Machine Learning
37 Learning Materials
No Code Data Science
No code Data Science Tools
Data Cleaning and Preprocessing
Statistical Analysis and Feature Engineering
Machine Learning Basics, Supervised Machine Learning, Cross Validation and Model Evaluation
Feature Importance and Model Interpretation, Unsupervised Machine Learning
Model Deployment and Maintenance
Introduction to Python For Data Science
Introduction to Python and Virtual Environment
Python Basics
NumPy and Pandas Basics
Cleaning and Preprocessing
Data Wrangling
Data Visualization with Matplotlib and seaborn
Introduction to Data Visualization
EDA (Exploratory Data Analysis)
Deep Dive into Plots and its attributes
Statistical Analysis and Outlier detection
Data Pre-Processing Advanced - Feature Engineering, Dimensionality Reduction (PCA, t-SNE)
Pre-Processing Tools: Column Transformer and Pipeline
Case Study
Case Study 1: Program Guidance Implementing all the above process
Case Study 2: Program Guidance Implementing all the above process
Machine Learning Basics
Introduction to Machine Learning
Scikit-Learn Basics
Supervised Machine Learning
Model Evaluation and Cross-Validation
Unsupervised Learning and Supervised Learning
Unsupervised Learning
Introduction to Neural Networks
Model Deployment Basics
Case Study: Predictive Modeling
Advanced Topics
Natural Language Processing (NLP) with NLTK and SpaCy
Time Series Forecasting
BONUS
Cloud Introduction / CI/CD TOOLS
Cache / Data streming / API Network
Fundamentals of Power BI | Assignment 6 submission
Fundamentals of Tableau
Fundamentals of MySQL
Jenkins or Concourse Tool
Github
Final Project
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