Machine Learning

Curriculum

Module 01 – Machine Learning-Introduction

• Introduction of Machine Learning
• Evolution of Machine Learning
• Application of Machine Learning

Module 02 – Machine Learning in Different Sectors

• Introduction
• AI & ML for Products or Services
• How Google Uses Artificial Intelligence and Data Science
• How LinkedIn Uses Artificial Intelligence and Data Science
• How Amazon Uses Artificial Intelligence and Data Science
• Netflix: Using Artificial Intelligence and Data Science to Drive Engagement
• Media and Entertainment Industry
• Education Industry
• Healthcare Industry
• Government
• Weather Forecasting
• Key Takeaways

Module 03 – Machine Learning-Fundamental

• Difference between Traditional Programming and ML Programming
• Requirements for Machine Learning Practical Implementation
• Required software and tools for Machine Learning implementations.
• Setup Anaconda
• Installation of PyCharm or Spyder or Jupyter
• Configure PyCharm/Spyder/Jupyter with Anaconda

Module 04 – Python Programming Foundation for Data Science

• Introduction
• Variables
• Data Types with Python
• Assisted Practice: Data Types in Python
• Keywords and Identifiers
• Expressions
• Basic Operators
• Operators in Python
• Functions
• Search for a Specific Element from a Sorted List
• String Operations
• String Operations in Python
• Tuples
• Tuples in Python
• Lists
• Lists in Python
• Sets
• Sets in Python
• Dictionaries
• Dictionary in Python
• Dictionary and its Operations
• Conditions and Branching
• While Loop
• For Loop
• Break and Continue Statements

Module 05 – File handling and Package handling using Python.

• Learning Objectives
• File Handling
• File Opening and Closing
• Reading and Writing Files
• Directories in File Handling
• Assisted Practice: File Handling
• Modules and Packages
• Assisted Practice: Package Handling

Module 06 – Mathematical Computing using NumPy

• Learning objectives
• Introduction of NumPy
• Create and Print Numpy Arrays
• Operations
• Executing Basic Operations in Numpy Array
• Performing Operations Using Numpy Array
• Demonstrate the Use of Copy and Use
• Manipulate the Shape of an Array

Module 07 – Data Manipulation with Pandas

• Learning Objectives
• Introduction to Pandas
• Data Structures
• Create Pandas Series
• DataFrame
• Create Pandas DataFrames
• Missing Values
• Handle Missing Values
• Various Data Operations
• Data Operations in Pandas DataFrame

Module 8 – Data visualization with Python

• Learning objectives
• Data Visualization
• Considerations of Data Visualization
• Factors of Data Visualization
• Python Libraries
• Create Your First Plot Using Matplotlib
• Line Properties
• Multiple Plots and Subplots
• Create a Plot with Annotation
• Create Multiple Subplots Using plt.subplots
• Types of plots
• Create a Stacked Histogram
• Create a Scatter Plot
• Create a Pie Chart
• Create a Bar Chart
• Create Box Plots
• Analysing Variables Individually
• Key Takeaways

Module 9 – Steps of Machine Learning Implementations

• Types of Machine Learning
• Labelled Data and Unlabelled Data
• Steps of Machine Learning
• Concept of Collecting the historic training Data for ML
• Concept of Pre-process data for Machine Learning
• Concept of Train the ML model
• Concept of Test the ML Algorithm
• Concept of using the ML Algorithm

Module 10 – Data Collection for Machine Learning

• Introduction
• Types of Data collection- Offline Data and Online Data
• Practical implementations of Reading the offline dataset using Numpy
• Practical implementations of Reading the online dataset using Numpy
• Practical implementations of Reading the offline dataset using Pandas
• Practical implementations of Reading the online iris dataset using Pandas

Module 11 – Concept of Supervise & Unsupervised Machine Learning

• Introduction
• Types of Machine Learning
• Labelled Data and Unlabeled Data
• Concept of Supervised Machine Learning
• Concept of Unsupervised Machine Learning
• Regression and Classification
• Linear Regression and Logistic Regression

Module 12 – Data Plotting for Machine Learning 

• Introduction
• Concept of Univariate plots
• Univariate Histogram Plots.
• Univariate Density Plots.
• Univariate Box and Whisker Plots.
• Concept of Multivariate plots
• Correlation Matrix Plot
• Scatter Matrix Plot

Module 13 – Prepare Data for ML using Data Transformation Methods

• Introduction
• Need for Data Pre-processing
• Data Transforms Steps
• Types of Data Transformation Methods
• Rescale Data
• Standardize Data
• Normalize Data
• Binarize Data

Module 14 – Practical implementation of Supervised ML Algorithm

• Introduction
• Implementation Foundation of Supervised Machine Learning Algorithms
• Regression and Classification
• Linear Regression and Logistic Regression
• Practical implementations of Supervised ML Algorithms- Linear Regression
• Practical implementations of Supervised ML Algorithms- Logistic Regression
•  Concept of Sigmoid Function
• k-NN Algorithm
• Naive Bayes Classifiers
• Decision trees etc.
• Concept of Support vector machines
• Introduction to concepts of forecasting

Module 15- Ensemble Learning and Advanced ML Techniques

• Introduction to Ensemble Learning

• Why use ensemble methods?

• Bias-Variance tradeoff in ensembles

• Bagging Technique

• Random Forest Algorithm (Theory + Implementation)

• Boosting Techniques

• AdaBoost

• Gradient Boosting Machines (GBM)

• XGBoost (Theory + Practical Implementation)

Module 16 – Data Resampling Methods for Evaluation of ML Models

• Introduction
• Evaluate Machine Learning Algorithms
• Split into Train and Test Sets
• K-fold Cross Validation
• Leave One Out Cross Validation
• Repeated Random Test-Train Splits
• What Techniques to Use When

Module 17 – Machine Learning Algorithm Performance Evaluation Metrics

• Introduction
• Algorithm Evaluation Metrics
• Logistic Regression Algorithm Performance Evaluation Metrics
• Classification Accuracy (Default).
• Logarithmic Loss.
• Area Under ROC Curve (AUC).
• Confusion Matrix.
• Classification Report.
• Linear Regression Algorithm Performance Evaluation Metrics
• Mean Absolute Error.
• Mean Squared Error.
• R2 Error

Module 18 – Practical implementation of Unsupervised Machine Learning 

• Introduction
• Concepts and Steps of Unsupervised Machine Learning Algorithm
• Concept of Clustering,
• Practical implementations of Machine Learning Unsupervised Algorithms
• K-Means Clustering.

Module 19 – Spot-Check Machine Learning Algorithms

• Concept of Algorithm Spot-Checking
• Algorithms Overview
• Linear Machine Learning Algorithms Spot-check
• Nonlinear Machine Learning Algorithms Spot-check
  

Module 20 – Save and Load Machine Learning Models

• Introduction
• Finalize Your Model with pickle.
• Finalize Your Model with Joblib

Module 21-Explainability in ML (Interpretable ML)

• SHAP and LIME for model interpretability
• Feature Importance in Tree-Based Models
• Fairness in ML models

Module 22– Conclusion and Future of Machine Learning

• Summary of machine learning concepts and techniques
• Machine learning trends and future developments
• Machine learning and society
• Machine learning ethics and safety.