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https://www.eventshigh.com/detail/bangalore/d70a4957648a1fb6ef490e277783769f-free-seminar-on-artificial-intelligence

Free Seminar on Artificial Intelligence

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Sat, 10 Nov 2018 3:00PM - 5:00PM
Vepsun Technologies
Free
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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 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 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 AUC-ROC curve to decide 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?
  • 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 neighbours using distance measures
  • Find optimal value of K in 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 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

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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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