ML

What is Machine Learning, and why is it important? .

Different types of ML: Supervised, Unsupervised, Reinforcement Learning

Setting up Python, Jupyter Notebook, and essential libraries

Cleaning and handling missing data

Feature scaling and transformation

Splitting data into training and testing sets

Using graphs and charts to analyze datasets

Identifying patterns and trends in data

Popular tools: Matplotlib, Seaborn, Pandas

Basics of linear regression

Evaluating model performance with MAE, MSE, and R-squared

Hands-on: Predicting house prices using regression

Introduction to Random Forest and Support Vector Machines (SVM)

Tuning models for better performance

Hyperparameter optimization techniques

Basics of clustering and when to use it

K-Means clustering explained with examples

Principal Component Analysis (PCA) for dimensionality reduction

How neural networks work

Introduction to TensorFlow and Keras

Building and training a simple neural network

ARIMA and LSTMs for trend predictions

Practical applications: Stock market and weather forecasting

Tokenization, stemming, and lemmatization

Sentiment analysis and text classification

Choosing a real-world problem to solve

Collecting and preparing data

Building, training, and deploying your final model