This course is designed to provide students with the knowledge and skills needed to perform data science and machine learning tasks using Python programming language. Students will learn to apply machine learning and deep learning algorithms using the libraries such as scikit-learn, Kears, and Tensorflow. The course will cover topics such as data cleaning, exploratory data analysis, machine learning algorithms, model evaluation, and selecting the appropriate model for business use.

Learning Objectives:

• Understand the fundamental concepts of data science and machine learning
• Perform exploratory data analysis and visualization using Matplotlib and Seaborn libraries
• Build predictive models using machine learning algorithms such as linear regression, logistic regression, decision trees, and random forests
• Understand the basics of deep learning and its applications in Python

1. Introduction to Data Science and Machine Learning in Python
   • Overview of data science and machine learning
   • Introduction to Python for data science and machine learning
   • Introduction to Google Colab Notebook
2. Exploratory Data Analysis and Feature Selection
   • Data visualization techniques
   • Data exploration with Pandas and Matplotlib
   • What are features and how it is important
3. Machine Learning Basics
   • Introduction of Overfitting and Underfitting
   • Class Imbalance and its solution
   • Download the dataset from Kaggle
   • Data splitting and data preparation for the Mchine learning model
   • Machine learning Basics and Types
   • Supervised, Unsupervised, Reinforcement learning with example
4. Supervised Learning Algorithms
   • Linear regression
   • Logistic regression
   • Decision trees and random forests
   • Project: Wine Quality Predictions
5. Unsupervised Learning Algorithms
   • K-means clustering
   • Hierarchical clustering
   • Dimensionality reduction
   • Project: Iris Classification
6. Model Evaluation and Selection
   • Model evaluation metrics
   • Cross-validation
   • Model selection techniques
   • Ensemble Model
   • Model save and load
7. Deep Learning with Keras and TensorFlow
   • Introduction to deep learning
   • Neural networks
   • Keras and TensorFlow
8. Convolutional Neural Networks (CNN)
   • CNN basics
   • CNN implementation using Keras
   • Project: Cats and Dogs Classification
9. Recurrent Neural Networks (RNN)
   • RNN basics
   • RNN implementation using Keras
   • Project: Sentiment analysis using text data
10. Real-world Examples
    • Some real-life business applications of Machine learning
    • Q&A and course feedback