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https://www.eventshigh.com/detail/bangalore/2f4c58c2b9c7338e47b3d23bd1f35855-free-seminar-on-artificial-intelligence

Free Seminar On Artificial Intelligence By Professsional

0.0,0.0
Sat, 16 Feb 3:00PM - 5:00PM
Vepsun Technologies - Best AWS, Azure, DevOps, Python, VMware, Google Cloud training
Free
417 people viewed this event.

Pricing & Offers

Pricing & Offers
Zero convenience fee, ticket price shown below is the final price.
Sat, 16 Feb
, 3:00PM
- 5:00PM
Per Person
Free
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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 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

Map & Directions
Vepsun Technologies - Best AWS, Azure, DevOps, Python, VMware, Google Cloud training 100 & 104, SR Arcade, 6th Cross Thulasi Theater Road, Marathahalli, Opposite Viceroy Boulevard, Marathahalli Village, Marathahalli, Bengaluru, Karnataka 560037, India
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Frequently Asked Questions

FAQs
  • Q. Hi, Came across your scheduled event of Free seminar on AI on 16th Feb and I’m really impressed by the program you’ve put together. I’m the Ops Manager at Auquan - a data science platform for financial services based in London and we would would like to propose a talk by our CTO if CFP is still open. This can be a beginner to intermediate level talk on how deep learning techniques can used for time series forecasting to improve profitability of trading strategies. Speaker Bio: Shub Jain is the CTO and founder of Auquan. He has been working on Auquan, an early stage fintech startup bridging the gap between data science and finance. At Auquan, he is employing new and cutting edge ML and Deep Learning techniques to solve financial prediction problems. Linkedin Profile: https://www.linkedin.com/in/shubj/
    6:28 AM, 31 Jan, 2019
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