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Data Science Training in Bangalore - With Ms Shambhavi Shukla

Sat, 22 Jun 4:00PM - Thu, 8 Aug 6:00PM
Vepsun Technologies
Rs 15000
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Module 1: Introduction to Python :

  Concepts of Python programming

  •  Configuration of Development Environment
  •  Using the Python Interpreter
  •  Numbers and Strings

 Module 2: More on Python:

 Tuples and Lists

  •  Functions
  •  Control Flow and Loops
  •  Dictionaries

 Module 3: Datascience Fundamentals :

 Introduction to Datascience

  • Real world use-cases of Datascience
  • Walkthrough of data types
  • Datascience project lifecycle

 Module 4: Introduction to NumPy:

 Basics of NumPy Arrays

  • Mathematical operations in NumPy
  • NumPy Array manipulation
  • NumPy Array broadcasting

 Module 5: Data Manipulation with Pandas :

 Data Structures in Pandas-Series and DataFrames

  • Data cleaning in Pandas
  • Data manipulation in Pandas
  • Handling missing values in datasets
  • Hands-on: Implement NumPy arrays and Pandas DataFrames

 Module 6: Data Visualization in Python :

  Plotting basic charts in Python

  •  Data visualization with Matplotlib
  •  Statistical data visualization with Seaborn
  •  Hands-on: Coding sessions using Matplotlib, Seaborn packages

 Module 7: Exploratory Data Analysis :

  Introduction to Exploratory Data Analysis (EDA) steps

  •  Plots to explore relationship between two variables
  •  Histograms, Box plots to explore a single variable
  •  Heat maps, Pair plots to explore correlations
  •  Perform EDA to explore survival using titanic dataset

 Module 8: Introduction to Machine Learning :

  What is Machine Learning?

  •  Use Cases of Machine Learning
  •  Types of Machine Learning - Supervised to Unsupervised methods
  •  Machine Learning workow

 Module 9: Linear Regression :

  Introduction to Linear Regression

  •  Use cases of Linear Regression
  •  How to t a Linear Regression model?
  •  Evaluating and interpreting results from Linear Regression models
  •  Predict Bike sharing demand

 Module 10: Logistic Regression :

  Introduction to Logistic Regression

  •  Logistic Regression use cases
  •  Understand the use of odds & Logit function to perform logistic regression
  •  Predicting credit card default cases

 Module 11: Decision Trees & Random Forest :

  Introduction to Decision Trees & Random Forest

  •  Understanding criterion(Entropy & Information Gain) used in Decision Trees
  •  Using Ensemble methods in Decision Trees
  •  Applications of Random Forest
  •  Predict passenger survival using Titanic Data set

 Module 12: Model Evaluation Techniques :

 Introduction to evaluation metrics and model selection in Machine Learning

  • Importance of Confusion matrix for predictions
  • Measures of model evaluation - Sensitivity, specificity, precision, recall & f-score
  • Use the AUC-ROC curve to decide the best model
  • Applying model evaluation techniques to Titanic dataset

 Module 13: Dimensionality Reduction using PCA :

  Unsupervised Learning: Introduction to Curse of Dimensionality

  •  What is dimensionality reduction?
  •  The technique used in PCA to reduce dimensions
  •  Applications of Principle Component Analysis (PCA)
  •  Optimize model performance using PCA on SPECTF heart data

 Module 14: K Nearest Neighbours :

 Introduction to KNN

  • Calculate neighbors using distance measures
  • Find the optimal value of K in the KNN method
  • Advantage & disadvantages of KNN
  • Classify phishing site data using a close neighbor technique

 Module 15: Naive Bayes Classifier :

  Introduction to Naive Bayes Classification

  •  Refresher on Probability theory
  •  Applications of Naive Bayes Algorithm in Machine Learning
  •  Classify spam emails based on probability

 Module 16: K-means Clustering :

 Introduction to K-means clustering

  • Decide clusters by adjusting centroids
  • Find the optimal 'k value' in K-means
  • Understand applications of clustering in Machine Learning
  • Segment hands in Poker data and segment flower species in Iris flower data

 Module 17: Support Vector Machines :

 Introduction to SVM

  • Figure decision boundaries using support vectors
  • Identify hyperplane in SVM
  • Applications of SVM in Machine Learning
  •  Predicting wine quality using SVM

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Map & Directions

Map & Directions
Vepsun Technologies 100 & 104, SR Arcade, 6th Cross Thulasi Theater Road, Marathahalli, Opposite Viceroy Boulevard, Marathahalli Village, Marathahalli, Bengaluru, Karnataka 560037, India
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