Free Seminar on Artificial Intelligence

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Vepsun Technologies -

100 & 104, SR Arcade, 6th Cross Thulasi Theater Road, Marathahalli, Opposite Viceroy Boulevard

Bengaluru, KA 560037


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Event description


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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Vepsun Technologies -

100 & 104, SR Arcade, 6th Cross Thulasi Theater Road, Marathahalli, Opposite Viceroy Boulevard

Bengaluru, KA 560037


View Map

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