Introduction to Data Science:

Introduction to Python and Jupiter Notebook. Python Basics: Variables, data types, loops, conditions, & functions. Introduction to libraries like NumPy, Pandas, & Matplotlib

Data Acquisition:

Data sources, data formats, Methods to collect , clean data. Data Exploration: Descriptive statistics, data visualization, & correlation analysis. Data Preparation: Data cleaning, feature scaling,

Introduction to Machine Learning

Overview of ML types of machine learning algorithms, & supervised Linear Regression: Simple linear regression, multiple linear regression, & model evaluation. Classification: Logistic regression,

Introduction to decision trees

Gini index, & Information gain. Random Forest: Introduction to random forests, bagging, & boosting. Outcome-driven Project Students will work on a supervised learning project using scikit-learn

Introduction to unsupervised learning,

clustering algorithms, and K-Means clustering. Dimensionality Reduction: Introduction to principal component analysis (PCA) & t-Distributed Stochastic Neighbour Embedding (NLP)

Introduction Natural Language Processing

stemming, & lemmatization. Sentiment Analysis: Introduction to sentiment analysis, preprocessing, feature extractionmodel building. Text Classification bag-of-words model

Introduction artificial neural networks

perceptron, activation & backpropagation Outcome-driven Project Students will work on a neural network project using TensorFlow during the live Introduction to CNN, convolutional layers pooling layers

Introduction to RNN, LSTM, and GRU.

Outcome-driven Project: Students will work on an RNN project using TensorFlow during the live session. Time Series Analysis: trend, seasonality Students work analysis project during the live session.