Course Syllabus

Day 1: Introduction to Python and Machine Learning Concepts

• Python Fundamentals.
• • Introduction to Python: Syntax, variables, and data types.
• • Control structures: Conditionals and loops.
• • Functions and modules: Writing reusable code.
• • Working with libraries: NumPy and Pandas for data manipulation.
• Introduction to Machine Learning.
• • Overview of machine learning (ML) and its importance in predictive analytics.
• • Types of ML: Supervised, Unsupervised, and Reinforcement Learning.
• • Common use cases in business.
• Hands-On Lab: Python Basics.
• • Setting up the Python environment.
• • Practicing data manipulation with NumPy and Pandas.
• • Simple exercises on functions and control structures.

Day 2: Data Preparation and Visualization

• Data Preparation.
• • Understanding data: Types and structures.
• • Data cleaning: Handling missing data, duplicates, outliers.
• • Data normalization and standardization.
• • Feature engineering: Creating relevant features for ML models.
• Data Visualization.
• • Importance of data visualization in machine learning.
• • Introduction to Matplotlib and Seaborn.
• • Creating various types of visualizations: histograms, scatter plots, box plots.
• Hands-On Lab: Data Preparation and Visualization.
• • Importing datasets and practicing data cleaning techniques.
• • Visualizing data using Matplotlib and Seaborn.
• • Practical exercises on feature engineering and exploratory data analysis (EDA).

Day 3: Supervised Learning – Regression and Classification Models

• Regression Models.
• • Concept of regression analysis.
• • Building simple and multiple linear regression models.
• • Evaluating regression models: Mean Squared Error, R².
• • Practical applications of regression models in prediction.
• Classification Models Overview.
• • Binary vs. multi-class classification.
• • Key algorithms: Logistic Regression, k-Nearest Neighbors (k-NN).
• • Performance evaluation: Confusion matrix, Precision, Recall, F1-Score.
• Hands-On Lab: Regression and Classification.
• • Building regression models with Scikit-Learn.
• • Implementing logistic regression and k-NN classifiers.
• • Model evaluation and performance metrics.

Day 4: Decision Trees, Random Forests, and Introduction to Deep Learning

• Decision Trees and Random Forests.
• • Understanding decision trees: Gini Impurity and Information Gain.
• • Random Forest: Concept of bagging and aggregation.
• • Comparing Decision Trees and Random Forests for better performance.
• Introduction to Deep Learning.
• • Understanding deep learning and its applications.
• • Overview of neural networks, perceptrons, activation functions.
• • Introduction to TensorFlow and Keras.
• Hands-On Lab: Decision Trees and Deep Learning.
• • Building decision tree and random forest models with Scikit-Learn.
• • Developing a simple neural network using TensorFlow and Keras.
• • Evaluating and tuning model performance.

Day 5: Advanced Deep Learning, Model Deployment, and Streamlit for Visualization

• Advanced Deep Learning Techniques.
• • Introduction to Convolutional Neural Networks (CNNs) for image classification.
• • Basics of Recurrent Neural Networks (RNNs) for sequence data.
• • Transfer learning concepts.
• strong>Model Deployment Concepts.
• • Overview of model deployment in production environments.
• • Exporting models using joblib, pickle, and TensorFlow SavedModel.
• • Introduction to Streamlit for creating interactive web apps.
• Hands-On Lab: Advanced Deep Learning and Model Deployment.
• • Building a CNN for image classification using TensorFlow.
• • Deploying deep learning models using Streamlit.
• • Creating an interactive dashboard to visualize predictions and results.
• Final Review and Q&A.
• • Recap of key concepts learned during the course.
• • Open Q&A session for participants to clarify any doubts.
• • Discussion on further learning and project ideas.