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Deep Learning Certification Training

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Reviews 4.9 (4.6k+)
4.7/5

Learners : 1080

Duration :  25 Days

About Course

The Deep Learning training course equips learners with skills to design, build, and deploy neural networks and AI models. This course covers deep learning fundamentals, supervised and unsupervised learning, neural networks, CNNs, RNNs, LSTMs, autoencoders, and transfer learning. Participants gain hands-on experience using TensorFlow, Keras, and PyTorch to develop real-world applications like image recognition, NLP, chatbots, and recommendation systems. With practical labs and projects, learners build expertise in training models, tuning hyperparameters, and optimizing performance. Ideal for data scientists, AI engineers, and ML developers, this program also prepares learners for Deep Learning certification and advanced careers in AI and data science. Enrol today to earn your certification.

Deep Learning Training Course Syllabus

Python for AI ML
  • Python for AI & ML
  • Business Statistics
  • Data Visualization (EDA)
Introduction
  • Regression & Classification
  • Training, Validation & Testing
  • Measures of Model Performance
Supervised Learning
  • Linear Regression
  • Logistic Regression
  • K-NN Classification
  • Naïve Bayes Classifiers
  • SVM
Unsupervised Learning
  • K-Means ClusteringHierarchical ClusteringCluster ProfilingDimensionality Reduction – PCA
Ensemble Technique
  • Decision Trees
  • Bagging
  • Random Forest
Model selection & Tuning
  • Feature Engineering
  • Model Development & Improvement
  • Model Validation & Diagnostics
  • Grid Search
  • Cross Validation
Recommender Systems
  • Content-based Recommender Systems
  • Collaborative Filtering (User & Item based)
  • Time Series Forecasting
  • Content-based Recommender Systems
Artificial Intelligence
  • Text Mining
  • CNN (Computer Vision)
  • RNN/LSTM
Deep Learning
  • TensorFlow
  • Keras
  • ANN
  • RNN
  • Big Data Analytics
Deep Learning Course Key Features

Course completion certificate

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Deep Learning Online Training FAQ'S

What is Deep Learning and how does it differ from traditional machine learning?

Deep Learning is a type of machine learning based on multi-layered neural networks. Unlike traditional ML, it automatically extracts features from raw data, enabling tasks like image and speech recognition.

Explain the role of Convolutional Neural Networks (CNNs).

CNNs are specialized deep learning models designed for image recognition, object detection, and pattern recognition, using convolutional layers to extract spatial features.

What is overfitting in deep learning and how can it be prevented?

Overfitting occurs when a model performs well on training data but poorly on test data. It can be reduced using techniques like dropout, regularization, and cross-validation.

What is transfer learning in deep learning?

Transfer learning reuses a pre-trained model (like ResNet or BERT) on a new dataset, saving time and improving accuracy in smaller data scenarios.

What are the most widely used deep learning frameworks?

Popular frameworks include TensorFlow (Google), PyTorch (Meta), and Keras, which provide libraries and tools for model building, training, and deployment.

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