Description

Module 1: Introduction to Machine Learning

• Understanding Artificial Intelligence, Machine Learning, and Deep Learning

• Applications of Machine Learning in real life

• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

• Overview of the Machine Learning pipeline

Module 2: Python for Machine Learning

• Python programming basics

• Data types, loops, functions, and file handling

• Introduction to NumPy for numerical operations

• Introduction to Pandas for data manipulation

Module 3: Data Preprocessing

• Importing datasets

• Handling missing data

• Encoding categorical variables

• Feature scaling techniques

Module 4: Data Visualization

• Creating plots using Matplotlib

• Visualizing data distributions and relationships

• Exploring data with Seaborn

Module 5: Supervised Learning

• Linear Regression

• Logistic Regression

• K-Nearest Neighbors (KNN)

• Support Vector Machines (SVM)

• Decision Trees and Random Forest

• Model evaluation: accuracy, precision, recall, F1-score, confusion matrix

Module 6: Unsupervised Learning

• K-Means Clustering

• Hierarchical Clustering

• Dimensionality reduction with Principal Component Analysis (PCA)

Module 7: Model Evaluation and Selection

• Train-test split and cross-validation

• Understanding bias and variance

• Grid search and hyperparameter tuning

Module 8: Feature Engineering

• Creating new features

• Selecting the most relevant features

• Dealing with imbalanced datasets

Module 9: Introduction to Deep Learning

• Basics of neural networks

• Activation functions

• Introduction to TensorFlow and Keras

• Building a simple neural network

Module 10: Real-World Projects

• Spam Email Detection

• House Price Prediction

• Customer Segmentation

• Handwritten Digit Recognition

• Stock Market Prediction

Tools and Libraries Used

• Python

• NumPy, Pandas, Matplotlib, Seaborn

• Scikit-learn

• TensorFlow / Keras

• Jupyter Notebook or Google Colab

Requirements