The Training is a comprehensive 120-hour program designed to equip participants with practical skills in machine learning and data science. The course covers fundamental concepts of machine learning, data preprocessing, model development, and evaluation techniques. Participants will learn how to work with real-world datasets, apply various machine learning algorithms such as regression, classification, clustering, and deep learning models, and utilize Python libraries like Pandas, NumPy, and Scikit-learn. By the end of the program, attendees will be able to independently analyze data, develop predictive models, and implement solutions for data-driven decision-making across various domains. 30 hour from the training soft skills for freelance

A 120-hour AI training program provides a comprehensive introduction to the fundamentals of Artificial Intelligence (AI), aimed at beginners or early professionals looking to understand and apply AI concepts.AI Fundamentals (15-20 hours)History of AI: Brief history, evolution, and key breakthroughs in AI.AI Terminology: Key terms like machine learning, deep learning, neural networks, and data science.Applications of AI: Examples from industries like healthcare, finance, robotics, and autonomous systems.Mathematical Foundations (20-25 hours)Linear Algebra: Vectors, matrices, and transformations used in AI algorithms.Probability & Statistics: Basics of probability theory, distributions, and statistical methods used in model evaluation.Calculus: Gradients, optimization techniques, and backpropagation in deep learning.Machine Learning Basics (25-30 hours)Supervised Learning: Linear regression, decision trees, random forests, and support vector machines.Unsupervised Learning: Clustering techniques like K-means and hierarchical clustering.Evaluation Methods: Cross-validation, confusion matrices, precision, recall, and F1 score.Deep Learning Foundations (20-25 hours)Neural Networks: Introduction to perceptrons, multi-layered networks, and activation functions.Backpropagation & Gradient Descent: How models learn and improve.Convolutional Neural Networks (CNNs): Used in image processing and computer vision tasks.Recurrent Neural Networks (RNNs): For sequence modeling, like natural language processing (NLP).Hands-on Practice (20-30 hours)Programming Languages: Python and its libraries (NumPy, pandas, TensorFlow, PyTorch).Model Building: Step-by-step projects, such as creating simple image classifiers or recommendation systems.Data Handling: Working with datasets, data preprocessing, feature selection, and data augmentation.

this training 45 hourEntrepreneurship training is designed to equip aspiring entrepreneurs with the knowledge, skills, and tools necessary to start, manage, and grow a business. This type of training typically covers a range of topics, from ideation and business planning to marketing, finance, and leadership.