NodeBook Private Limited

Python with Machine Learning

Python with Machine Learning

This course is designed to equip students with the skills and knowledge to use Python for data analysis
and to build a strong foundation in machine learning. Through hands-on projects and interactive
sessions, students will learn to work with Python libraries, handle data, and apply machine learning
algorithms to real-world problems.

No prior knowledge is required. Learning will start from scratch.

Course Title Python with Machine Learning
Days per week Sundays only
Number of hours per week 3 hours per day
Total study time 12 classes – 36 credit hours
Requirements / pre-requisites
  •  Introduction to Python
  •  Intermediate Python
  •  Data Science
  •  Data Analysis with Python
  •  Introduction to Machine Learning
  •  Classification Algorithms
  •  Unsupervised Learning and Beyond
  •  Deep Learning
  •  Capstone Project

Software to be learn:
• Google Colab
• Jupyter Notebook

Week 1: Python Basics

  • Overview of Python
  • Variables and Data Types
  • Basic Input and Output
  • Control Structures (if, else, loops)

Week 2: Functions and Data Structures

  • Functions and Modules
  • Lists, Tuples, and Dictionaries
  • File Handling

Week 3: Python Libraries

  • NumPy for Numerical Operations
  • Pandas for Data Manipulation
  • Matplotlib and Seaborn for Data Visualization

Week 4: Data Cleaning and Preprocessing

  • Handling Missing Data
  • Data Transformation
  • Data Aggregation

Week 5: Exploratory Data Analysis (EDA)

  • Data Visualization
  • EDA Techniques

Week 6: Machine Learning Fundamentals

  • Introduction to Machine Learning
  • Types of Machine Learning
  • Supervised Learning vs. Unsupervised Learning

Week 7: Linear and Logistic Regression

  • Simple and Multiple Linear Regression
  • Model Evaluation
  • Feature Selection
  • Binary and Multiclass Classification
  • Regularization

Week 8: Decision Trees and Random Forest

  • Decision Tree Concepts
  • Random Forest Ensembles
  • Model Evaluation and Feature Importance

Week 9: Clustering Algorithms

  • K-Means Clustering
  • Hierarchical Clustering
  • Evaluation Metrics

Week 10: Introduction to Deep Learning

  • Introduction to deep learning: What is deep learning, its applications, and significance.
  • Basics of Neural Networks: Neurons, activation functions, and models.
  • Introduction to TensorFlow and Keras
  • Building your first neural network using a library (TensorFlow).
  • Training an Artificial Neural Network (ANN) for a classification task.
  • Understanding loss functions and optimization algorithms (SGD, Adam).

Week 11: Convolutional Neural Network (CNN)

  • Introduction to CNNs and their role in image analysis.
  • Convolutional layers, filters, and feature maps.
  • Pooling layers and spatial down sampling.

Week 12: Capstone Project

  • Project Introduction and Data Selection
  • Project Development, Implementation, and Presentation
  • Final Assessment and Course Review
  • Recap of Key Concepts
  • Final Assessment and Evaluation
  • Course Review and Feedback
  • Flower Species Analysis
    • Task 1: Data Collection – Gather a dataset containing information about various flower species.
    • Task 2: Data Exploration – Explore the dataset to understand its structure, features, and distributions.
    • Task 3: Data Analysis – Analyze the characteristics and relationships among different flower species using Python.
    • Task 4: Visualization – Create visualizations (e.g., histograms, scatter plots) to illustrate insights gained from the analysis.
  • Car Insurance Analysis
    • Task 1: Data Acquisition – Obtain a dataset related to car insurance, including information about premiums, claims, and policyholders.
    • Task 2: Data Cleaning – Clean the dataset by handling missing values, outliers, and inconsistencies.
    • Task 3: Exploratory Data Analysis (EDA) – Perform EDA to identify patterns and relationships in the data.
    • Task 4: Statistical Analysis – Conduct statistical analyses (e.g., regression, correlation) to understand factors influencing car insurance premiums and claims.
  • Predicting House Prices
    • Task 1: Data Preprocessing – Preprocess real estate data, including feature scaling, encoding categorical variables, and handling missing values.
    • Task 2: Model Selection – Choose appropriate machine learning models (e.g., linear regression, decision trees) for predicting house prices.
    • Task 3: Model Training – Train the selected models using the preprocessed data.
    • Task 4: Model Evaluation – Evaluate the performance of the trained models using appropriate metrics (e.g., RMSE, R-squared).
  • Salaries Prediction by Position of Employees
    • Task 1: Data Preparation – Prepare salary data by extracting relevant features (e.g., employee position, years of experience) and the target variable (salary).
    • Task 2: Model Building – Build predictive models (e.g., linear regression, random forest) to predict salaries based on employee positions.
    • Task 3: Cross-Validation – Perform cross-validation to assess the generalization performance of the models.
    • Task 4: Model Deployment – Deploy the best-performing model for predicting salaries of employees in different positions.
  • Handwritten Digit Recognition using Artificial Neural Networks (ANN) on MNIST
    • Task 1: Data Loading – Load the MNIST dataset containing images of handwritten digits.
    • Task 2: Data Preprocessing – Preprocess the image data by normalizing pixel values and splitting it into training and testing sets.
    • Task 3: Model Development – Build an Artificial Neural Network (ANN) model using TensorFlow/Keras for digit recognition.
    • Task 4: Model Training and Evaluation – Train the model using the training data and evaluate its performance on the test data.
  • Cats and Dogs Image Classification using Convolutional Neural Networks (CNN)
    • Task 1: Data Preparation – Prepare a dataset consisting of images of cats and dogs, ensuring proper labeling and data augmentation.
    • Task 2: CNN Architecture Design – Design a Convolutional Neural Network (CNN) architecture suitable for image classification tasks.
    • Task 3: Model Training – Train the CNN model using the prepared dataset, adjusting hyperparameters as needed.
    • Task 4: Model Evaluation – Evaluate the performance of the trained model on a separate validation dataset and visualize the results.

Overview

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Trainer Info

Wardha Arshad – Trainer Nodebook Private Limited
Experienced data scientist, Microsoft Ambassador, Stanford Section Leader. Skilled in Azure, ML, Python, R. Expertise in data modeling, ML algorithms. B.S. Computer Science from NED University.

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