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Machine Learning

Machine Learning

About The Course

Our Machine Learning course offers a comprehensive introduction to the world of data-driven decision-making and predictive analytics. Designed for both beginners and professionals, the course covers key concepts such as supervised and unsupervised learning, neural networks, and deep learning. You’ll learn to build and deploy powerful models using popular tools like Python, TensorFlow, and Scikit-learn. With hands-on projects, real-world applications, and expert guidance, this course will equip you with the skills needed to excel in today’s data-centric job market and drive innovation in any industry.

The Course Curriculam

MODULE 1: Introduction to Machine LearningOverview of machine learning concepts and applications.Types of machine learning: supervised, unsupervised, reinforcement learning Introduction to Python programming language and libraries (NumPy, Pandas, Matplotlib) .

MODULE 2: Supervised LearningLinear regression: theory and implementation.Logistic regression: theory and implementationModel evaluation metrics: MSE, RMSE, MAE, confusion matrix, ROC curve.

MODULE 3: Supervised Learning (cont.).Decision trees and ensemble methods (bagging, boosting).Random forests: theory and implementation.Gradient boosting machines (GBM). Curriculum included

MODULE 4: Unsupervised LearningClustering algorithms: K-means, hierarchical cIustering,DimensionaIity reduction techniques: PCA (Principal Component AnaIysis),t-SNE (t-distributed Stochastic Neighbor Embedding).

MODULE 5: Neural Networks and Deep Learning,lntroduction to artificial neural networks (ANNs).Deep learning fundamentals: activation functions, backpropagation Building and training deep neural networks using TensorFlow/Keras.

MODULE 6: Convolutional Neural Networks (CNNs),Introduction to CNNs CNN architecture and layers Image classification and object detection using CNNs. Curriculum included

MODULE 7: Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP) Introduction to RNNs Long Short-Term Memory (LSTM) networks Text classification and sentiment analysis using RNNs.

MODULE 8: Reinforcement Learning and Model Deployment Introduction to reinforcement learning (RL), Markov decision processes (MDP) and Q-learning Model deployment: basics of deploying machine learning models in production environments.

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