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New Artificial Intelligence Batch in Bangalore by Expert Trainer - With Ms Shambhavi Shukla

Sat, 22 Jun 2019 1:30PM - Sun, 1 Sep 2019 4:00PM
Vepsun Technologies
Rs 15000
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Introduction to Python :

  Concepts of Python programming

  • Configuration of Development Environment
  •  Variable and Strings
  •  Functions, Control Flow and Loops
  •  Tuple, Lists, and Dictionaries
  • Standard Libraries

 Module 2: Data Science Fundamentals :

  Introduction to Data Science

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

 Module 3: Introduction to NumPy:

  Basics of NumPy Arrays

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

 Module 4: 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 5: 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 6: Exploratory Data Analysis :

 Introduction to Exploratory Data Analysis (EDA) steps

  • Plots to explore the 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 7: Introduction to Machine Learning :

 What is Machine Learning?

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

 Module 8: Linear Regression :

  Introduction to Linear Regression

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

 Module 9: 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 10: 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 11: 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 12: 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 13: KNearestNeighbours:

  Introduction to KNN

  • Calculate neighbors using distance measures
  • Find the optimal value of K in the KNN method
  •  Advantage & disadvantages of KNN

 Module 14: 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 15: 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 16: 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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