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What Will You Learn?
Practice your new skills with coding challenges (solutions included)
Organize and structure your code using software patterns like modules
Get friendly and fast support in the course Q&A
Downloadable lectures, code and design assets for all projects
Course Content
Introduction to Data Science
What is data Science?
Applications of Data Science
Why choose Data Science as a carrier?
Key Components of Data Science
Introduction to Machine Learning
Machine Learning Life Cycle
Applications of Machine Learning
Types of Machine Learning
Installation and Setup
Overview of Anaconda and Jupyter notebook
Anaconda Installation
Introduction to Python
Python Fundamentals. (Operators, Conditionals, Loops, Functions)
Python Data Structures. (Lists, Tuples, Dictionary, Set, Arrays)
Python for Data Science
File Handling in Python
NumPy
Pandas
Matplotlib
Seaborn
Data Collection
Data Collection from files (CSV, Excel)
Data Collection from Database (MySQL, Sqlite)
Data Collection from API’s
Data Wrangling and Visualization
Data Cleaning and Preprocessing using Numpy and Pandas
Data Visualization using Matplotlib, Seaborn, Tableau
Statistics for Machine Learning
Machine Learning Algorithms
Population Vs Sample
Linear Regression
Logistic Regression
Decision Tree
Random Forest
Naive Bayes
K Nearest Neighbours
SVM
Clustering
KMeans
PowerBI
Deep Learning
NLP
Neural Networks
TensorFlow
This course is full right now. We limit the number of students to create an optimized and productive group dynamic.
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Requirements
No Pre-requisites.
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Practice your new skills with coding challenges.
Organize and structure your code using software.
Get friendly and fast support in the course.
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