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
Course Modality
- On-site
- Online
Course Duration
- 36 hours
Course
- Python with Machine Learning
Course Support
- 24/7 Support and Recording Available
Course Language
- English
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.