What you'll get
- 153+ Hours
- 40 Courses
- Mock Tests
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Basics of neural networks and deep learning
- Building and training deep learning models
- Predictive modeling with structured/tabular data
- Creating recommendation systems
- Image classification, segmentation & object detection
- Style transfer and transfer learning
- Sentiment analysis and text generation
- Machine translation and text similarity
- Time series forecasting
- Speech recognition techniques
- Image captioning systems
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning with Tensorflow for Beginners | 13h 39m | ✔ | View Curriculum |
| Deep Learning Neural Network with R | 2h 56m | ✔ | View Curriculum |
| Deep Learning Heuristic using R | 4h 42m | ✔ | View Curriculum |
| Comprehensive Deep Learning Training | 11h 17m | ✔ | View Curriculum |
| Deep Learning Tutorials | 1h 34m | ✔ | View Curriculum |
| Tensorflow With Python | 1h 46m | ✔ | View Curriculum |
| Project on Deep Learning - Artificial Neural Network | 2h 29m | ✔ | View Curriculum |
| Project on Deep Learning - Convolutional Neural Network | 1h 06m | ✔ | View Curriculum |
| Project on Deep Learning: Handwritten Digits Recognition | 1h 02m | ✔ | View Curriculum |
| Project on Deep Learning: Stock Price Prognostics | 2h 17m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning with R | 20h 25m | ✔ | View Curriculum |
| Artificial Intelligence and Machine Learning Training Course | 12h 8m | ✔ | View Curriculum |
| Artificial Intelligence with Python | 6h 15m | ✔ | View Curriculum |
| Machine Learning with Scikit Learn | 8h 37m | ✔ | View Curriculum |
| Predictive Modeling with Python | 8h 26m | ✔ | View Curriculum |
| Matplotlib Basic | 4h 2m | ✔ | View Curriculum |
| Numpy and Pandas | 5h 9m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Pandas Project | 3h 14m | ✔ | View Curriculum |
| Sentiment Analysis with Python | 57m | ✔ | View Curriculum |
| Data Science with Python | 4h 14m | ✔ | View Curriculum |
| OpenCV for Beginners | 2h 28m | ✔ | View Curriculum |
| Seaborn | 2h 28m | ✔ | View Curriculum |
| Pyspark Beginner | 2h 16m | ✔ | View Curriculum |
| Machine Learning using Python | 3h 26m | ✔ | View Curriculum |
| Statistics for Data Science using Python | 3h 23m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Data Science with Python Project-Predict Diabetes on Diagnostic Measures | 1h 02m | ✔ | View Curriculum |
| ggplot2 Project | 2h 07m | ✔ | View Curriculum |
| Logistic Regression Project using SAS Stat | 4h 26m | ✔ | View Curriculum |
| Project on Linear Regression in Python | 2h 28m | ✔ | View Curriculum |
| Logistic Regression-Predicting the Survival of Passenger in Titanic | 2h 6m | ✔ | View Curriculum |
| Project on Term Deposit Prediction using R | 3h 2m | ✔ | View Curriculum |
| Card Purchase Prediction using R | 2h 28m | ✔ | View Curriculum |
| Develop Movie Recommendation Engine using Machine Learning | 51m | ✔ | View Curriculum |
| Employee Attrition Prediction Using Random Forest Technique | 1h 6m | ✔ | View Curriculum |
| Project on Term Deposit Prediction using Logistic Regression | 1h 38m | ✔ | View Curriculum |
| Credit-Default using Logistic Regression | 3h 3m | ✔ | View Curriculum |
| House Price Prediction using Linear Regression | 3h 2m | ✔ | View Curriculum |
| Poisson Regression Project using SAS Stat | 2h 21m | ✔ | View Curriculum |
| Machine Learning Project in Python | 1h 58m | ✔ | View Curriculum |
| Project on K-Means Clustering | 43m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| No courses found in this category. | |||
Description
This Deep Learning course provides a comprehensive introduction to neural networks and their advanced architectures. Inspired by how the human brain processes information, deep learning models use multiple layers of interconnected neurons to learn patterns, make predictions, and solve complex problems. Through practical examples and hands-on exercises, learners will understand how deep neural networks work, using concepts such as weight propagation, gradient descent, and activation functions.
The course covers a wide range of real-world applications, including predictions on tabular data, building recommendation engines like those used by Amazon and Netflix, image classification with datasets such as MNIST, and advanced tasks such as image segmentation, object detection, and style transfer. Learners will also explore deep learning techniques for natural language processing, including sentiment analysis, text generation, machine translation, question answering, and text similarity.
Additionally, the course dives into time series forecasting, speech recognition, and image captioning, showing how deep learning powers modern AI systems. By the end of this course, participants will have a strong foundation in deep learning concepts and the practical skills to implement state-of-the-art models across various domains.
Sample Certificate
Requirements
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Basic understanding of Python programming
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Familiarity with machine learning concepts
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Interest in AI, deep learning, or data science
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Ability to work with numerical and text data
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Willingness to learn advanced algorithms and workflows.
Target Audience
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Aspiring AI and deep learning professionals
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Data scientists and machine learning engineers
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Software developers integrating AI into applications
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Researchers in neural networks and AI
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Analysts working on image, text, or speech projects
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Anyone interested in practical deep learning applications.
