Deep Learning: Python Deep Learning Masterclass
Unlock the Secrets of Deep Learning into CNNs, RNNs, NLP, Chatbots, and Recommender Systems
Updated on Jun, 2025
Language - English
Development ,Data Science,Deep Learning
Lectures -576
Duration -58.5 hours
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Course Description
Welcome to the ultimate Deep Learning masterclass! This comprehensive course integrates six modules, each providing a deep dive into different aspects of Deep Learning using Python. Whether you're a beginner looking to build a strong foundation or an intermediate learner seeking to advance your skills, this course offers practical insights, theoretical knowledge, and hands-on projects to cater to your needs.
Who Should Take This Course?
- Beginners interested in diving into the world of Deep Learning with Python
- Intermediate learners looking to enhance their Deep Learning skills
- Anyone aspiring to understand and apply Deep Learning concepts in real-world projects
Why This Course?
This course offers an all-encompassing resource that covers a wide range of Deep Learning topics, making it suitable for learners at different levels. From fundamentals to advanced concepts, you will gain a comprehensive understanding of Deep Learning using Python through practical applications.
What you'll learn in this course?
Module 1: Deep Learning Fundamentals with Python
Introduction to Deep Learning.
Python Basics for Deep Learning.
Data preprocessing for Deep Learning algorithms.
General machine learning concepts.
Module 2: Convolutional Neural Networks (CNNs) in Depth
In-depth understanding of CNNs.
Classical computer vision techniques.
Basics of Deep Neural Networks.
Architectures like LeNet, AlexNet, InceptionNet, ResNet.
Transfer Learning and YOLO Case Study.
Module 3: Recurrent Neural Networks (RNNs) and Sequence Modeling
Exploration of RNNs.
Applications and importance of RNNs.
Addressing vanishing gradients in RNNs.
Modern RNNs: LSTM, Bi-Directional RNNs, Attention Models.
Implementation of RNNs using TensorFlow.
Module 4: Natural Language Processing (NLP) Fundamentals
Mastery of NLP.
NLP foundations and significance.
Text preprocessing techniques.
Word embeddings: Word2Vec, GloVe, BERT.
Deep Learning in NLP: Neural Networks, RNNs, and Advanced Models.
Module 5: Developing Chatbots Using Deep Learning
Building Chatbot systems
Deep Learning fundamentals for Chatbots.
Comparison of conventional vs. Deep Learning-based Chatbots.
Practical implementation of RNN-based Chatbots.
Comprehensive package: Projects and advanced models.
Module 6: Recommender Systems using Deep Learning
Application of Recommender Systems.
Deep Learning's Role in Recommender Systems.
Benefits and challenges.
Developing Recommender Systems with TensorFlow.
Real-world project: Amazon Product Recommendation System.
Final Capstone Project
Integration and application.
Hands-on project: Developing a comprehensive deep-learning solution.
Final assessment and evaluation.
This comprehensive course merges the essentials of Deep Learning, covering CNNs, RNNs, NLP, Chatbots, and Recommender Systems, offering a thorough understanding of Python-based implementations. Enroll now to gain expertise in various domains of Deep Learning through hands-on projects and theoretical foundations.
Keywords and Skills:
Deep Learning Mastery
Python Deep Learning Course
CNNs and RNNs Training
NLP Fundamentals Tutorial
Chatbot Development Workshop
Recommender Systems with TensorFlow
AI Course for Beginners
Hands-on Deep Learning Projects
Python Programming for AI
Comprehensive Deep Learning Curriculum
Who is this course for?
- Aspiring Data Scientists: Individuals aiming to specialize in deep learning and expand their knowledge in AI applications.
- Programmers and Developers: Those seeking to venture into the field of artificial intelligence and harness Python for deep learning projects.
- AI Enthusiasts and Learners: Anyone passionate about understanding CNNs, RNNs, NLP, chatbots, and recommender systems within the realm of deep learning.
- Students and Researchers: Those pursuing academic endeavours or conducting research in machine learning and AI-related fields.
- Professionals Exploring Career Shifts: Individuals interested in transitioning or advancing their careers in artificial intelligence and deep learning.
- Tech Enthusiasts: Individuals keen on exploring cutting-edge technologies and applications within the AI domain.
Goals
Hands-on Projects: Engage in practical projects spanning image analysis, language translation, chatbot creation, and recommendation systems.
Deep Learning Fundamentals: Understand the core principles of deep learning and its applications across various domains.
Convolutional Neural Networks (CNNs): Master image processing, object detection, and advanced CNN architectures like LeNet, AlexNet, and ResNet.
Recurrent Neural Networks (RNNs) and Sequence Modeling: Explore sequence processing, language understanding, and modern RNN variants such as LSTM.
Natural Language Processing (NLP) Essentials: Dive into text preprocessing, word embeddings, and deep learning applications in language understanding.
Integration and Application: Combine knowledge from different modules to develop comprehensive deep-learning solutions through a capstone project.
Prerequisites
Understanding Python fundamentals is recommended for implementing deep learning concepts covered in the course.
Curriculum
Check out the detailed breakdown of what’s inside the course
Deep Learning: Deep Neural Network for Beginners Using Python
85 Lectures
-
Introduction : Introduction to Instructor
02:53
02:53
-
Introduction : Introduction to course
03:36
03:36
-
Basics of Deep Learning: Problem to Solve Part 1
02:00
02:00
-
Basics of Deep Learning: Problem to Solve Part 2
02:26
02:26
-
Basics of Deep Learning: Problem to Solve Part 3
01:42
01:42
-
Basics of Deep Learning: Linear Equation
03:18
03:18
-
Basics of Deep Learning: Linear Equation Vectorized
03:00
03:00
-
Basics of Deep Learning: 3D Feature Space
03:46
03:46
-
Basics of Deep Learning: N Dimensional Space
02:30
02:30
-
Basics of Deep Learning: Theory of Perceptron
01:46
01:46
-
Basics of Deep Learning: Implementing Basic Perceptron
05:37
05:37
-
Basics of Deep Learning: Logical Gates for Perceptrons
02:46
02:46
-
Basics of Deep Learning: Perceptron Training Part 1
01:40
01:40
-
Basics of Deep Learning: Perceptron Training Part 2
03:40
03:40
-
Basics of Deep Learning: Learning Rate
03:14
03:14
-
Basics of Deep Learning: Perceptron Training Part 3
03:31
03:31
-
Basics of Deep Learning: Perceptron Algorithm
01:00
01:00
-
Basics of Deep Learning: Coading Perceptron Algo (Data Reading & Visualization)
07:22
07:22
-
Basics of Deep Learning: Coading Perceptron Algo (Perceptron Step)
06:43
06:43
-
Basics of Deep Learning: Coading Perceptron Algo (Training Perceptron)
06:43
06:43
-
Basics of Deep Learning: Coading Perceptron Algo (Visualizing the Results)
03:53
03:53
-
Basics of Deep Learning: Problem with Linear Solutions
02:32
02:32
-
Basics of Deep Learning: Solution to Problem
01:03
01:03
-
Basics of Deep Learning: Error Functions
02:21
02:21
-
Basics of Deep Learning: Discrete vs Continuous Error Function
02:25
02:25
-
Basics of Deep Learning: Sigmoid Function
03:01
03:01
-
Basics of Deep Learning: Multi-Class Problem
01:18
01:18
-
Basics of Deep Learning: Problem of Negative Scores
03:02
03:02
-
Basics of Deep Learning: Need of Softmax
01:22
01:22
-
Basics of Deep Learning: Coding Softmax
04:06
04:06
-
Basics of Deep Learning: One Hot Encoding
02:41
02:41
-
Basics of Deep Learning: Maximum Likelihood Part 1
05:30
05:30
-
Basics of Deep Learning: Maximum Likelihood Part 2
03:48
03:48
-
Basics of Deep Learning: Cross Entropy
04:06
04:06
-
Basics of Deep Learning: Cross Entropy Formulation
07:38
07:38
-
Basics of Deep Learning: Multi Class Cross Entropy
03:51
03:51
-
Basics of Deep Learning: Cross Entropy Implementation
04:14
04:14
-
Basics of Deep Learning: Sigmoid Function Implementation
00:57
00:57
-
Basics of Deep Learning: Output Function Implementation
02:10
02:10
-
Deep Learning: Introduction to Gradient Decent
05:21
05:21
-
Deep Learning: Convex Functions
02:31
02:31
-
Deep Learning: Use of Derivatives
03:13
03:13
-
Deep Learning: How Gradient Decent Works
03:34
03:34
-
Deep Learning: Gradient Step
01:54
01:54
-
Deep Learning: Logistic Regression Algorithm
01:37
01:37
-
Deep Learning: Data Visualization and Reading
06:10
06:10
-
Deep Learning: Updating Weights in Python
04:14
04:14
-
Deep Learning: Implementing Logistic Regression
12:44
12:44
-
Deep Learning: Visualization and Results
08:43
08:43
-
Deep Learning: Gradient Decent vs Perceptron
04:35
04:35
-
Deep Learning: Linear to Non Linear Boundaries
04:42
04:42
-
Deep Learning: Combining Probabilities
02:07
02:07
-
Deep Learning: Weighted Sums
03:01
03:01
-
Deep Learning: Neural Network Architecture
12:09
12:09
-
Deep Learning: Layers and DEEP Networks
04:44
04:44
-
Deep Learning:Multi Class Classification
02:48
02:48
-
Deep Learning: Basics of Feed Forward
07:51
07:51
-
Deep Learning: Feed Forward for DEEP Net
04:57
04:57
-
Deep Learning: Deep Learning Algo Overview
01:57
01:57
-
Deep Learning: Basics of Back Propagation
06:32
06:32
-
Deep Learning: Updating Weights
02:46
02:46
-
Deep Learning: Chain Rule for BackPropagation
05:53
05:53
-
Deep Learning: Sigma Prime
02:23
02:23
-
Deep Learning: Data Analysis NN Implementation
05:25
05:25
-
Deep Learning: One Hot Encoding (NN Implementation)
03:11
03:11
-
Deep Learning: Scaling the Data (NN Implementation)
01:48
01:48
-
Deep Learning: Splitting the Data (NN Implementation)
04:55
04:55
-
Deep Learning: Helper Functions (NN Implementation)
02:18
02:18
-
Deep Learning: Training (NN Implementation)
12:25
12:25
-
Deep Learning: Testing (NN Implementation)
03:21
03:21
-
Optimizations: Underfitting vs Overfitting
05:19
05:19
-
Optimizations: Early Stopping
03:51
03:51
-
Optimizations: Quiz
00:58
00:58
-
Optimizations: Solution & Regularization
05:59
05:59
-
Optimizations: L1 & L2 Regularization
03:12
03:12
-
Optimizations: Dropout
02:59
02:59
-
Optimizations: Local Minima Problem
02:55
02:55
-
Optimizations: Random Restart Solution
04:27
04:27
-
Optimizations: Vanishing Gradient Problem
04:16
04:16
-
Optimizations: Other Activation Functions
03:19
03:19
-
Final Project: Final Project Part 1
11:19
11:19
-
Final Project: Final Project Part 2
13:16
13:16
-
Final Project: Final Project Part 3
12:58
12:58
-
Final Project: Final Project Part 4
12:19
12:19
-
Final Project: Final Project Part 5
08:06
08:06
Deep Learning CNN : Convolutional Neural Network With Python
78 Lectures
Deep Learning: Recurrent neural networks with Python
136 Lectures
NLP- Natural Language Processing In python(Theory & Projects)
223 Lectures
Advanced Chatbots with Deep Learning & Python
26 Lectures
Recommender Systems: An Applied Approach Using Deep Learning
27 Lectures
Bonus
1 Lectures
Instructor Details
AI Sciences
Welcome to the epicenter of innovation, where a collective of visionaries, PhDs, and leading practitioners in Artificial Intelligence, Computer Science, Machine Learning, and Statistics unite. Our team hails from the tech titans - Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.
In our commitment to demystify the complex world of tech, we've crafted an extensive series of courses. Tailored primarily for beginners and newcomers, these courses are your gateway into the realms of Machine Learning, Statistics, Artificial Intelligence, and Data Science. We embarked on this journey with a simple goal: to make these advanced concepts accessible, minimizing theory and lengthy texts, allowing eager minds to dive straight into practice.
As our mission evolved, so did our offerings. We now present comprehensive courses that cater to a broader audience, ensuring everyone can navigate and master these fields with ease.
The impact of our courses has been nothing short of remarkable. We've empowered over 100,000 students, transforming them into masters of AI and Data Science. Join us, and be part of this journey of learning and empowerment, where your mastery of the future begins today.
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