Machine Learning With Python

Curriculum

The Course Curriculam

Week 1: Introduction to Machine Learning and Python

What is Machine Learning? Types: Supervised, Unsupervised, and Reinforcement Learning.
Setting up the Python environment: Jupyter Notebook, Anaconda, and essential libraries (NumPy, Pandas, Matplotlib).
Overview of data processing and visualization.
Hands-on Practice: Explore datasets and visualize trends using Matplotlib and Seaborn.

Week 2: Data Preprocessing

Week 3: Supervised Learning – Regression

Week 4: Supervised Learning – Classification

Week 5: Unsupervised Learning

Week 6: Model Optimization and Regularization

Week 7: Neural Networks and Deep Learning Basics

Week 8: Final Project and Deployment

₹1000

Course Information

Instructor: vedupskilling@gmail.com
Lesson: 28
Duration: 40h
Quizze: 0

Share This Course

Machine Learning With Python

The Course Curriculam

Week 1: Introduction to Machine Learning and Python

What is Machine Learning? Types: Supervised, Unsupervised, and Reinforcement Learning.

Setting up the Python environment: Jupyter Notebook, Anaconda, and essential libraries (NumPy, Pandas, Matplotlib).

Overview of data processing and visualization.

Hands-on Practice: Explore datasets and visualize trends using Matplotlib and Seaborn.

Week 2: Data Preprocessing

Understanding datasets: Features, labels, and datasets structure.

Handling missing values, encoding categorical data, and feature scaling.

Splitting datasets into training and testing sets.

Hands-on Practice: Preprocess a real-world dataset like Titanic or Boston Housing.

Week 3: Supervised Learning – Regression

Introduction to Linear Regression and Polynomial Regression.

Evaluation metrics: MSE, RMSE, and R² score.

Hands-on Practice: Build and evaluate a linear regression model using scikit-learn.

Week 4: Supervised Learning – Classification

Logistic Regression and Support Vector Machines (SVMs).

Decision Trees and Random Forests.

Evaluation metrics: Confusion matrix, precision, recall, and F1 score.

Hands-on Practice: Implement a classification model on the Iris or MNIST dataset.

Week 5: Unsupervised Learning

Introduction to clustering: K-Means and Hierarchical Clustering.

Dimensionality reduction using Principal Component Analysis (PCA).

Hands-on Practice: Perform clustering and PCA on a real-world dataset.

Week 6: Model Optimization and Regularization

Techniques to prevent overfitting: L1, L2 regularization, and cross-validation.

Hyperparameter tuning: Grid Search and Random Search.

Hands-on Practice: Optimize hyperparameters of a machine learning model.

Week 7: Neural Networks and Deep Learning Basics

Introduction to Artificial Neural Networks (ANNs).

Building ANNs using libraries like TensorFlow and Keras.

Basics of Convolutional Neural Networks (CNNs).

Hands-on Practice: Train a simple neural network on image data.

Week 8: Final Project and Deployment

Final Project: Develop an end-to-end machine learning solution (e.g., predictive analytics, image classification, or recommendation system).

Model deployment: Introduction to Flask or Streamlit for serving machine learning models.

Presentation of the project and feedback session.