Introduction to Machine Learning - Day 1
● Overview of machine learning concepts and applications.
● Historical background and evolution of machine learning.
● Importance and Impact of machine learning in various industries.
Foundations of Machine Learning - Day 1
● Basic concepts: data, features, labels, models, predictions, etc.
● Types of machine learning: supervised, unsupervised, semi-supervised,
reinforcement learning.
● Key components of a machine learning pipeline: data preprocessing, feature
engineering, model training evaluation and deployment
Machine Learning Algorithms - Day 1
● Supervised learning algorithms:
○ Linear Regression
○ k-nearest neighbour
● Unsupervised learning algorithms:
○ Clustering (K-means, hierarchical clustering)
○ Dimensionality Reductions
● Reinforcement learning algorithms:
○ Q-learning: basics and convergence properties
○ Deep QNetworks DQN and extensions.
Model evaluation and validation - Day 1
● Performance metrics for classification, regression, and clustering models.
● Cross-validation techniques.
● Hyperparameter tuning and model selection
Applications of Machine Learning - Day 1
● Natural language processing (NLP) and Text mining:
○ Sentiment analysis, text classification, topic modelling
● Computer Vision:
○ Image classification, entity detection, image segmentation
● Recommendation Systems:
○ Collaborative filtering, content-based recommendation
● Time Series Analysis:
○ Forecasting, anomaly detection
● Healthcare Applications:
○ Medical image analysis predictive modelling for diagnosis
● Financial Applications:
○ Market prediction, fraud detection
● Industry-specific use cases:
○ Manufacturing, retail, marketing, etc
Introduction to Neural Networks - Day 1
● Basics of artificial neural networks (ANNs): neurons, layers, activation
functions.
● Feedforward propagation and backpropagation algorithms.
● Training neural networks using gradient descent and optimization algorithms.
Introduction to Data Preprocessing - Day 2
● Discuss the importance of data cleaning, handling missing values, and feature
scaling.
● Loading Data: Use pandas to load a dataset (e.g., Titanic, Iris, or a dataset
relevant to your audience).
● Exploratory Data Analysis (EDA): Visualize the dataset using libraries like
matplotlib and seaborn.
Data Cleaning - Day 2
● Handle missing values using techniques like imputation or removal.
● Remove or encode categorical variables.
Feature Scaling - Day 2
● Normalize or standardize the features.
Introduction to Machine Learning Models - Day 2
● Brief overview of supervised learning (classification and regression).
● Splitting the Dataset: Train-test split.
Training Models - Day 2
● Train a simple supervised model (e.g., Linear Regression for regression tasks
or Logistic Regression for classification).
● Train a simples unsupervised model (e.g., k-means for clustering)
Model Evaluation - Day 3
● Evaluate the model using appropriate metrics (e.g., accuracy, precision, recall
for classification).
Introduction to Advanced Models - Day 3
● Brief overview of ensemble methods, support vector machines, and neural
networks.
● Ensemble Methods: Train and evaluate a Random Forest or Gradient
Boosting model.
Hyperparameter Tuning - Day 3
● Use GridSearchCV or RandomizedSearchCV to find the best parameters.
Saving the Model - Day 3
● Save the trained model using joblib or pickle.
Neural Networks - Day 3
● Build and train a simple neural network using TensorFlow/Keras.