Free Demo On Artificial Intelligence Sep 29 2018

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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