Data Science Master Class - Day 9 /30

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Internship on Data Analytics With IETE Certification : https://rzp.io/l/pX9n1Q6T

Attendance Link :

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What you will Learn?
⭐DATA SCIENCE
✅Day-1: Introduction to Artificial Intelligence, Data Analytics & Road Map to become a Data Scientist

⭐EXCEL
✅Day-2: Data Preparation - Power Query & Tables
✅Day-3: Data analytics- Formula & Pivot Table
✅Day-4: Story Telling - Charts & Dashboard

⭐PYTHON
✅Day-5: Introduction to Python & Installing Python and its Libraries
✅Day-6: Basic Python Programming for Data Analytics

⭐STATISTICS & PROBABILITY
✅Day-7: Introduction to Statistics & Use Case of Statistics on Data
✅Day-8: Population and Sampling

⭐BI TOOLS - TABLEU
✅Day-9: Connect Tableau to a Variety of Datasets
✅Day-10: Visualize Data in the Form of Various Charts, Plots, and Maps

⭐BI TOOLS - POWERBI
✅Day-11: Connect Tableau to a Variety of Datasets
✅Day-12: Visualize Data in the Form of Various Charts, Plots, and Maps and Calculate Data

⭐NUMPY
✅Day-13: Python Numpy functions

⭐PANDAS
✅Day-14: Pandas for Data analytics in Python

⭐MATPLOTLIB - Data Visualization
✅Day-15: Matplotlib for data visualization

⭐SEABORN- Data Visualization
✅Day-16: Seaborn for data visualization

⭐DATABASE - SQL
✅Day-17: SQL basics for Data analytics

⭐DATABASE - MONGODB
✅Day-18: MongoDB basics for Data analytics

⭐MACHINE LEARNING
✅Day-19: Introduction to Machine Learning & its libraries

⭐Supervised Learning - Classification
✅Day-20: Salary Estimation using K-NEAREST NEIGHBOR - SUPERVISED LEARNING

⭐Supervised Learning - Regression
✅Day-21: House Price Prediction using LINEAR REGRESSION - SUPERVISED LEARNING

⭐UnSupervised Learning - Clustering
✅Day-22: Identifying the Pattern of the Customer spent using K-MEANS CLUSTERING

⭐UnSupervised Learning - Association
✅Day-23: Market Basket Analysis using APIRIORI

⭐Reinforcement Learning
✅Day-24: Web Ads. Click through Rate optimization using UPPER BOUND CONFIDENCE

⭐Natural Language Processing
✅Day-25: Sentimental Analysis using Natural Language Processing

⭐DEEP LEARNING
✅Day-26: Introduction to Deep Learning & its libraries

⭐Multi-Layer Perceptron
✅Day-27: Diabetes detection using Artificial Neural Network (MLP)

⭐Convolutional Neural Network
✅Day-28: Object Recognition using Pre Trained Model – Caffe
✅Day-29: Brain Tumor Detection using CNN

⭐Recurrent Neural Network
✅Day-30: Stock Price prediction using LSTM

Learn Faster & Easier than You Think ???? ????

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