CERTIFICATION COURSE IN AI & ML

This module lays the groundwork for understanding Artificial Intelligence (AI) and Machine Learning (ML) concepts, including their history, applications, and basic principles.

Explore key concepts and techniques in AI and ML, such as supervised and unsupervised learning, neural networks, deep learning, and reinforcement learning.

Learn the fundamentals of Python programming language, including syntax, data types, control structures, functions, and object-oriented programming concepts.

Dive into various data types and structures in programming languages, including lists, tuples, dictionaries, arrays, and linked lists, and understand their usage and manipulation

Gain skills in manipulating and analyzing data using Python libraries like NumPy and Pandas, including tasks such as filtering, sorting, merging, and aggregating datasets

Learn techniques for cleaning and preprocessing raw data, including handling missing values, removing duplicates, standardizing data formats, and transforming categorical variables.

Explore methods for gaining insights into data through visualization and statistical analysis, including distribution plots, correlation matrices, and summary statistics.

Familiarize yourself with popular data visualization tools such as Matplotlib, Seaborn, and Plotly, and learn how to create various types of plots and charts to communicate insights effectively.

Understand the core principles of machine learning, including model training, evaluation, and validation techniques, as well as bias-variance tradeoff and overfitting.

Dive into regression and classification algorithms, including linear regression, logistic regression, decision trees, and support vector machines, and learn how to apply them to solve real-world problems

Explore unsupervised learning techniques such as clustering (e.g., K-means clustering) and dimensionality reduction (e.g., PCA) for data exploration and feature extraction.

Delve into advanced topics in machine learning, such as ensemble methods, neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and natural language processing (NLP).

Apply the knowledge and skills acquired throughout the training program to complete a hands-on capstone project, demonstrating proficiency in AI/ML concepts, programming, data analysis, and problem-solving.

For further details on each topic or to enroll in specific courses or training programs related to IT, you can explore online platforms, university websites, or consult with professionals in the field to find the most suitable options for your learning goals.