Data Science Master Class – Day 8/30

The Objective of this Webinar Series is to facilitate the participants to get cognizance of the concepts dealt with for substantial utilization of the same into studying, teaching, Research work, and Upgrading. ?

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: Excel – 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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