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Machine Learning is omnipresent right now. Organizations are splurging to integrate machine learning solutions into their daily processes. It’s a great time to learn data science and get ready for your first industry role!
300+ Lessons curated specifically to start your Journey in Data Science and Machine Learning.
Explore Python for Data Science
Important libraries and functions in Python
Reading files and manipulating data in Python
Working with data frames, lists, and dictionary
Working with pandas and other python libraries for data exploration
Use Matplotlib and Seaborn for data visualization
Creating charts to visualize data and generate insights
Univariate and Bivariate analysis using python
Perform Statistical Analysis on real-world datasets
Build and Validate Hypothesis using statistical tests
Generating useful insights from the data
Learn Important Machine Learning concepts
Perform data cleaning and Preprocessing
In-depth understanding of Basic ML models
Linear Models, Decision Tree, k-NN
Math Behind each Machine Learning Algorithm
Building Classification and Regression Models
Talks by ML Experts, Practitioners
Learn from Professionals and Leaders with decades of experience
Framework to manage end-to-end ML projects
Building an End-to-End Credit Risk Model
Feature Selection in High-Dimensional Spaces
Introduction to the World of Reinforcement Learning
-Data Exploration -Feature Engineering Techniques -Machine Learning Model Implementation
Various Banking Institutes
-Implementating ML Models -Feature Importance
Supermarket and Grocery Stores
Data Preprocessing Implementing ML Models
Based on any classification methods
-Understand the importance of Data Exploration -Learn the power of Feature Engine to improve your models -Learn implementation of a Machine Learning model
Uber, Lyft, Ola and other cab aggregators
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