AI & ML  Artificial Intelligence & Machine Learning Training with Python

Introduction to NumPy and array manipulation.

Descriptive statistics using NumPy.

Introduction to Pandas: DataFrames, Series, and data cleaning.

Hands-on: Data analysis using real datasets.

Introduction to Matplotlib and Seaborn.

Basic and advanced visualizations: line plots, histograms, box plots, and heatmaps.

Visualizing statistical distributions and correlations.

Introduction to machine learning and types of learning: supervised, unsupervised, reinforcement.

Basics of linear algebra, calculus, and probability for ML.

Tools and libraries: Scikit-learn, TensorFlow, Pandas.

Regression and Classification models (Linear Regression, Logistic Regression).

Evaluation metrics: R-squared, confusion matrix, precision, recall, etc.

Feature engineering and hyperparameter tuning.

Hands-on: Predicting house prices, cancer classification.

Clustering techniques (K-Means, DBSCAN, GMM).

Dimensionality reduction (PCA, t-SNE).

Hands-on: Customer segmentation, feature visualization.

Forecasting techniques using ARIMA and exponential smoothing.

Introduction to Spark MLlib and PySpark for large datasets.

Model deployment basics: Flask, FastAPI, cloud platforms (AWS, GCP, Azure).

Introduction to neural networks, CNNs, RNNs, and advanced architectures like BERT and GPT.

NLP applications: text pre-processing, sentiment analysis, text classification.

Hands-on: Implementing deep learning models for NLP tasks.

Addressing fairness, bias, and transparency in AI.

Exploring AI applications in healthcare, finance, and marketing.

Hands-on: Solving real-world business problems using AI and ML.

Introduction to Tableau and data preparation with Tableau Prep.

Creating advanced visualizations and dashboards.

Hands-on: Data visualization for business insights.