2 to 3 Months
Online + Offline
15 Students Only Batch Size
Anyone
Beginner to Advance Training
Introduction to Machine Learning Basics of Machine Learning Types of Machine Learning: Supervised and Unsupervised Learning Linear Regression Feature Engineering and Data Preparation Logistic Regression Binary Logistic Regression Multi-Class Logistic Regression K-Nearest Neighbours (KNN) KNN - K Nearest Neighbours KNN for Classification KNN for Regression Support Vector Machines (SVM) Support Vector Machines (SVM) for Classification Support Vector Machines (SVM) for Regression
Decision Trees Decision Trees Random Forests Random Forests Random Forest Model Random Forest for Classification & Regression Boosting Methods Boosting Methods AdaBoost Gradient Boosting XGBoost (Extreme Gradient Boosting) Naive Bayes Classification Naive Bayes Classification Naive Bayes Variants: Gaussian, Multinomial, Bernoulli Applying Supervised Machine Learning Techniques on Preprocessed Data
Introduction to Artificial Intelligence Basics of Artificial Intelligence Module 2: Introduction to Deep Learning Significance, Applications, and Trends of Deep Learning Introduction to Neural Networks - Neurons, Layers, Activation Functions
Basic Activation Functions Basic Activation Functions Neural Network Architecture Neural Network Architecture Advanced Neural Network Techniques Weight Initialization Strategies Forward Propagation and Backpropagation Building a Simple Feedforward Neural Network with Backpropagation Loss Functions and Regularization Application in CNNs.
Text Processing Basics Preprocessing Techniques NLTK and spaCy Libraries Text Representation: Bag-of-Words, TF-IDF, Word Embeddings Advanced NLP Sentiment Analysis Sequence Labeling: NER, POS Tagging, Grammar Parsing Text Generation and Language Models RNNs, LSTMs, and GRUs Transformers and BERT
Text Processing Basics Image Basics and Manipulation ML-based Image Classification Keypoint Detectors, Descriptors, and CNN Understanding CNN Architecture, Transfer Learning, and Fine-tuning Object Detection, Segmentation with U-Net GANs: Architecture, Applications, Ethical Considerations Advanced Techniques: Image Super-resolution, Neural Style Transfer, Deep Fakes' Risks and Ethical Concerns
Once you have completed the course, assignments, exercise and submit the projects you will be able to generate the certificate and be eligible for placements
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