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INDUSTRIAL INTERNSHIP on Image Pogramming using Matlab

Carrier-Based Complete Data Science Training: Algorithms, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

We are here to help you make the right decisions to achieve your financial goals

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13+ Core Conepts

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13+ Projects

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Live / Recorded

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Community Suported

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6 Months

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Download PPT & Project Metrials

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Pre-Process & Understanding Data​

Step 1

Data Preparation

  • ✓ The course provides the entire Knowledge  you need to become a data scientist
  • ✓ Fill up your resume with in demand  data science tools & skills: Statistical analysis, 
  • Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • ✓ Learn how to pre-process data Understand the mathematics behind Machine Learning
  • ✓ Start coding in Python and learn how to use it for statistical analysis

Step 2

Data Processing

  • ✓ Perform OOPS Concepts in Python
  • ✓ Carry out cluster and factor analysis
  • ✓ Be able to understand Machine Learning algorithms in Python, 
  • using NumPy, stats models, and sci-kit-learn
  • ✓ Apply your skills to real-life cases & examples

Step 3

Data Science Execution

  • ✓ Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlow & Keras
  • ✓ Develop a Model while coding and solving tasks with Algorithms
  • ✓ Unfold the power of deep neural networks
  • ✓ Improve Machine Learning algorithms by studying underfitting, overfitting, training, 
  • validation, n-fold cross-validation, testing, and how hyperparameters could improve performance
  • ✓ Warm up your fingers as you will be eager to apply everything you have learned here to 
  • more and more real-life situations

Benefits of Learning Course

Tools Covered

  • > Essential Python concepts for data science, including   

  • data types, variables, loops, and functions

  • > How to work with data using Python’s powerful data manipulation libraries, 

  • such as NumPy and Pandas

  • > How to visualize data using Python libraries such as Matplotlib and Seaborn 

  • > How to visualize data using tools such as Tableau and Power bi

  • > Machine learning with Python: Supervised, Unsupervised 

  •  Reinforcement Learning 

  • > Techniques and best practices for effective data analysis and data storytelling

Course Curriculum (45 Hrs)

Python Basics 
Python Data Structures
Python Fundamentals
Advance Python (oops)
Pandas  for Data Science
NumPy for Data Science
Matplotlib
Seaborn
Scikit-Learn
NLTK
Keras
Tableau – Data visualization
Tableau – Data Sources, Worksheet
Introduction power BI 
Visualize Data in the Form of Various Charts, Plots, and Maps BI tools – Power BI
Google Colab Notebook
Python – Date and Time, Data Wrangling
Python – Data Aggregation
Python – Word Tokenization, Stemming, and Lemmatization
Python – Data Visualization
Python – Statistical Analysis
Python – Types Of Distribution
Python – Correlation, Chi-Square Test, Linear Regression
Supervised Learning – Classification and Regression
Liver disease Prediction – (Logistic Regression)
Crime Analysis – (KNN Algorithm)
Classifying muffins and cupcakes – (SVM Algorithm)
Fake news detection – (Naïve Baye’s)
Android malware – (Decision tree)
Credit card Fraud detection – (Random Forest)
Evaluating the classification model – (Confusion matrix) Calculating the accuracy score.
Classification model selection For Breast Cancer – (Classification)
Employee Salary Prediction – (Linear Regression Single Variable)
Advertisement and Sales Prediction – (Multiple Linear Regression)
Generating Data points based on some equation – (Polynomial Regression)
Ice – Cream shop revenue prediction from temperature – (Decision Tree Regression)
Agriculture Price Prediction – (Random Forest Algorithm)
Evaluating the performance of my regression model – ( Root mean square error and R2 score)
Regression Model selection for sales forecasting.
Unsupervised Learning – Clustering
Crime Pattern Analysis – (K-Means Clustering)
Customer Spending Analysis – (Hierarchical Clustering)
Flower Species – Data Visualization
Image Compression Using – SVD (Singular Value Decomposition)
Unsupervised Learning – Association
Market Basket Analysis – (APRIARI)
Market Basket Optimization / Analysis – (ECLAT)
Reinforcement Learning
Web Ads click-through rate optimization – (Upper Bound Confidence) 
Natural Language Processing
Hate Speech Detection – (NLTK)
Loan Prediction Problem – (XGBoost)
Deep Learning
Movie Review Classification – (RNN)
Digits Classification – (CNN)
AI – Cart Pole (Reinforcement Learning)

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