What you will learn?

Understand the foundational concepts of Artificial Intelligence (AI) and Machine Learning (ML), including their principles, methodologies, and applications.

Demonstrate proficiency in key ML algorithms, such as linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.

Develop the ability to preprocess and clean data, perform feature engineering, and select appropriate features for Machine Learning( ML) models

Apply supervised learning techniques to build predictive models and solve classification and regression problems.

Implement unsupervised learning algorithms, such as clustering and dimensionality reduction, to discover patterns and structures in data.

Utilize advanced ML techniques, including ensemble methods, deep learning, and reinforcement learning, to tackle complex real-world problems.

Understand the trade-offs and limitations of different ML models and techniques, such as bias-variance trade offs, overfitting, and underfitting.

Gain proficiency in popular ML libraries and frameworks, such as scikit-learn, TensorFlow, and PyTorch, and learn how to leverage them for model development and evaluation.

Develop skills in data preprocessing, including data cleaning, feature extraction, and normalization, to ensure high-quality inputs for ML models.

Know how to deal with text data and build natural language processing (NLP) model