What you'll get
- 153+ Hours
- 40 Courses
- Mock Tests
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Grasping fundamental concepts of neural networks and deep learning.
- Developing and training deep learning models from the ground up.
- Predictive analytics using structured and tabular data.
- Designing recommendation engines.
- Image classification, segmentation, and object detection techniques.
- Implementing style transfer and leveraging transfer learning.
- Processing and analyzing text: performing sentiment evaluation and generating content.
- Machine translation and textual similarity analysis.
- Forecasting time series data.
- Fundamentals of speech recognition.
- Building 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 program offers a thorough exploration of neural networks and advanced AI architectures. Inspired by the human brain’s approach to learning, deep learning models consist of interconnected layers of neurons that can identify patterns, make predictions, and solve complex computational problems. Learners develop a deep understanding of neural networks through practical exercises and real-world examples, exploring concepts such as weight propagation, activation functions, and gradient descent optimization.
This course explores diverse applications, including predictive modeling with structured data, creating recommendation engines similar to those used by Amazon and Netflix, performing image classification with datasets such as MNIST, and applying advanced computer vision techniques, including image segmentation, object detection, and style transfer.
In addition, participants will study natural language processing (NLP) applications, including sentiment analysis, text generation, machine translation, and text similarity, alongside time series forecasting, speech recognition, and image captioning systems. By the end of the course, learners will possess both a conceptual understanding of deep learning and the practical skills to deploy modern AI solutions across diverse domains.
Sample Certificate
Goals
- Build a strong conceptual foundation in deep learning.
- Master the construction and training of neural network models.
- Gain practical experience in implementing AI solutions across different data types.
- Understand the application of deep learning in computer vision, NLP, and time series analysis.
- Prepare learners for real-world AI and deep learning projects.
Objectives
By the end of this program, participants will gain the skills to:
- Explain neural network architectures and their functioning.
- Train deep learning models for tabular, image, and text data.
- Develop recommendation engines and predictive models.
- Implement advanced computer vision solutions, such as segmentation and object detection.
- Apply NLP techniques for sentiment analysis, machine translation, and text similarity.
- Create models for time series forecasting, speech recognition, and image captioning.
Highlights
- Hands-on projects using Python and popular deep learning frameworks (TensorFlow, PyTorch).
- Real-world datasets for practical application.
- Comprehensive coverage of computer vision, NLP, and time series tasks.
- End-to-end projects: recommendation engines, image classifiers, and text generation systems.
- Techniques for model optimization, transfer learning, and fine-tuning.
- Guidance on best practices for deploying deep learning models.
Requirements
- Basic knowledge of Python programming.
- Familiarity with fundamental machine learning concepts.
- Interest in AI, data science, or deep learning.
- Comfort with numerical and textual datasets.
- Willingness to explore advanced algorithms and workflows.
Target Audience
- Aspiring AI engineers and deep learning specialists.
- Data scientists and machine learning practitioners.
- Software developers are integrating AI into applications.
- Academic researchers focusing on neural networks and AI.
- Analysts working with text, image, or speech data.
- Professionals seeking practical, hands-on deep learning experience.
FAQ
Q1. Do I need prior experience in deep learning?
No. Although we recommend prior knowledge of Python and basic machine learning, this course guides beginners step by step.
Q2. Which programming languages or tools are required?
Python is the primary language, along with frameworks such as TensorFlow and PyTorch.
Q3. Are projects included in the course?
Yes. Learners will complete multiple hands-on projects covering computer vision, NLP, and recommendation systems.
Q4. Can this course help me in my career?
Absolutely. It provides practical skills to implement deep learning models in real-world scenarios, preparing learners for AI-focused roles.
Q5. Will I receive a certificate?
Yes, learners will earn a certificate upon completing the course.
Career Benefits
- Qualifications for roles such as AI Engineer, Deep Learning Specialist, and Machine Learning Engineer.
- Skills to develop AI solutions across industries, including e-commerce, healthcare, finance, and technology.
- Ability to work on projects in computer vision, NLP, and predictive analytics.
- Strong foundation for pursuing advanced AI research or specialized certifications.
- Competitive advantage in an AI-driven job market.
