bangalore
Date
  • weekwid: false
  • widHeader: false
  • orgName: priya@vepsun.com
  • orwid: false
https://www.eventshigh.com/detail/bangalore/f826b6a9fd162b44a197c36d744f556c-new-artificial-intelligence-batch-in

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

0.0,0.0
Sat, 22 Jun 1:30PM - Sun, 1 Sep 4:00PM
Vepsun Technologies
Rs 15000
17 people viewed this event.
I Am Interested

Get notified when the event happens next time.

Details

Details

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

Login to View Organizer Details
Like this event ? Share it with your friends !!
Show

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
Reviews
Reviews
No reviews available
Write a review
Be the first one to review! Share your experience.
Show

Frequently Asked Questions

FAQs
Have any query? Drop your questions here !!
Show
EventsHigh Specials, technology, classes and workshops, tech workshops,