Artificial Intelligence(Python, Data Science, ML and AL)

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