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DeepSeek AI and Coding Projects: From Prompts to Automation
2026-02-11
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 4.97 GB | Duration: 10h 13m​

Master DeepSeek AI for coding, automation, and real-world apps-build projects with Python, prompts, and local AI tools

What you’ll learn
Understand how to craft effective prompts to get accurate and helpful coding responses from DeepSeek AI.
Gain hands-on experience using DeepSeek to write, debug, and optimize code in languages like Python and JavaScript.
Learn to automate repetitive development tasks, documentation, and testing using DeepSeek’s AI capabilities.
Build complete AI-assisted projects from concept to deployment, applying practical coding and automation techniques.

Requirements
No prior experience with AI tools or programming is required.
A basic understanding of how to use a computer and web browser.
Stable internet connection to access DeepSeek and other online tools
A willingness to learn and experiment with AI-assisted coding.

Description
Welcome to DeepSeek AI & Coding Essentials: From First Prompts to Full Automation-a complete beginner’s guide to unlocking the potential of AI-assisted programming.In this course, you’ll discover how DeepSeek AI can become your personal coding assistant. Whether you’re completely new to programming or have some experience, this course will teach you how to write smart prompts, understand AI-generated code, and automate real-world development tasks. You’ll explore how to use DeepSeek to generate code in languages like Python, JavaScript, and HTML/CSS, debug errors, create simple tools, and even build complete applications. You’ll also learn how to run the DeepSeek R1 model locally using Ollama and integrate it with Python for advanced, offline automation workflows.We’ll start with the basics-what DeepSeek AI is, how it works, and how to use it effectively. From there, you’ll move on to practical exercises, guided examples, and hands-on projects that show how AI can streamline your coding workflow. You’ll also learn how to use AI for documentation, testing, and other time-saving tasks.This course is perfect for students, self-learners, freelancers, and professionals looking to explore the future of software development. All you need is a computer, internet access, and a willingness to learn.By the end of this course, you’ll be confident using DeepSeek AI to solve coding challenges, automate tasks, and bring your programming ideas to life faster than ever before.

Who this course is for:
Beginners with no coding or AI experience, Students and self-learners exploring programming and automation, Freelancers and entrepreneurs wanting to boost productivity with AI tools, Developers curious about integrating DeepSeek AI into their workflow, Tech enthusiasts looking to learn prompt engineering and AI-assisted coding

For More Courses Visit & Bookmark Your Preferred Language Blog
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Free Download Deepfakes and Synthetic Media Threats
Released 4/2026
By Christopher Rees
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 21m 59s | Size: 109 MB

Convincing fake videos, voices, and images are becoming easier to create and harder to spot, increasing the risk of scams, disinformation, and reputational damage for individuals and organizations.
What you’ll learn
Convincing fake videos, voices, and images are becoming easier to create and harder to spot, increasing the risk of scams, disinformation, and reputational damage for individuals and organizations.
In this course, Deepfakes and Synthetic Media Threats, you’ll learn to identify deepfakes and synthetic media, understand how voice and video fakes are created, and verify authenticity before being misled or spreading false information.
First, you’ll explore how deepfakes are produced and why they are becoming more realistic and accessible.
Next, you’ll discover practical techniques to identify warning signs and validate whether the media is authentic.
Finally, you’ll learn how to pause, question, and verify information to avoid scams, disinformation, and accidental dissemination of misinformation.
When you’re finished with this course, you’ll have the skills and knowledge of deepfake and synthetic media awareness needed to recognize manipulated content and make informed decisions before trusting or sharing it.
Homepage
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https://app.pluralsight.com/ilx/video-courses/deepfakes-synthetic-media-threats/course-overview

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Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python) by LazyProgrammer
English | 8 Aug 2016 | ASIN: B01K31SQQA | 86 Pages | AZW3/MOBI/EPUB/PDF (conv) | 1.44 MB​

Like Markov models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades.

In the first section of the course we are going to add the concept of time to our neural networks.

I’ll introduce you to the Simple Recurrent Unit, also known as the Elman unit.

We are going to revisit the XOR problem, but we’re going to extend it so that it becomes the parity problem – you’ll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

In the next section of the book, we are going to revisit one of the most popular applications of recurrent neural networks – language modeling.

One popular application of neural networks for language is word vectors or word embeddings. The most common technique for this is called Word2Vec, but I’ll show you how recurrent neural networks can also be used for creating word vectors.

In the section after, we’ll look at the very popular LSTM, or long short-term memory unit, and the more modern and efficient GRU, or gated recurrent unit, which has been proven to yield comparable performance.

We’ll apply these to some more practical problems, such as learning a language model from Wikipedia data and visualizing the word embeddings we get as a result.

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey.

See you in class!

"Hold up. what’s deep learning and all this other crazy stuff you’re talking about?"

Deep Learning: Advanced Natural Language Processing and RNNs
MP4 | Video: AVC 1280×720 | Audio: AAC 44KHz 2ch | 67 lectures | 8h 20m Duration | 2.82 GB
Genre: eLearning | Language: English​

Natural Language Processing (NLP) with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!

Ever wondered how AI technologies like
OpenAI

ChatGPT
,
GPT-4
,
DALL-E
,
Midjourney
, and
Stable Diffusion
really work? In this course, you will learn the foundations of these groundbreaking applications.
It’s hard to believe it’s been been over a year since I released my first course on
Deep Learning
with
NLP
(natural language processing).
A lot of cool stuff has happened since then, and I’ve been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you.
So what is this course all about, and how have things changed since then?
In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (
recurrent neural networks
), CNNs (
convolutional neural networks
), and
word embedding
algorithms such as word2vec and GloVe.
This course takes you to a higher systems level of thinking.
Since you know how these things work, it’s time to build systems using these components.
At the end of this course, you’ll be able to build applications for problems like:
text classification (examples are sentiment analysis and spam detection)
neural machine translation
question answering
We’ll take a brief look
chatbots
and as you’ll learn in this course, this problem is actually no different from machine translation and question answering.
To solve these problems, we’re going to look at some advanced Deep NLP techniques, such as:
bidirectional RNNs
seq2seq (sequence-to-sequence)
attention
memory networks
All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as
Keras
,
Numpy
,
Tensorflow
, and
Matpotlib
to make things super easy and focus on the high-level concepts. I am always available to answer your questions and help you along your data science journey.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about "remembering facts", it’s about "seeing for yourself" via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
See you in class!
"If you can’t implement it, you don’t understand it"
Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times.
Suggested Prerequisites:
Decent Python coding skills
Understand RNNs, CNNs, and word embeddings
Know how to build, train, and evaluate a neural network in Keras
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
UNIQUE FEATURES
Every line of code explained in detail – email me any time if you disagree
No wasted time "typing" on the keyboard like other courses – let’s be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math – get important details about algorithms that other courses leave out

Deep Learning Model Deployment
Duration: 1h 9m | .MP4 1920×1080 30 fps(r) | AAC, 48000 Hz, 2ch | 168.76 MB
Genre: eLearning | Language: English​

Turn your trained models into production services. This course will teach you how to deploy, serve, and monitor deep learning models in real-world applications.

More Info:
Deep Learning Model Deployment

Deep Learning : Convolutional Neural Networks With Python
Published 3/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.41 GB | Duration: 4h 13m​

Deep Learning and Computer Vision using Convolutional Neural Networks with Python, Pytorch. Train, Test, Deploy Models

What you’ll learn

Deep Convolutional Neural Networks with Python and Pytorch Basics to Expert

Introduction to Deep Learning and its Building Blocks Artificial Neurons

Define Convolutional Neural Network Architecture from Scratch with Python and Pytorch

Hyperparameters Optimization For Convolutional Neural Networks to Improve Model Performance

Custom Datasets with Augmentations to Increase Image Data Variability

Training and Testing Convolutional Neural Network using Pytorch

Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Advanced CNNs for Segmentation, Object tracking, and Pose Estimation.

Pretrained Convolutional Neural Networks and their Applications

Transfer Learning using Convolutional Neural Networks Models

Convolutional Neural Networks Encoder Decoder Architectures

YOLO Convolutional Neural Networks for Computer Vision Tasks

Region-based Convolutional Neural Networks for Object Detection

Requirements

A Google Gmail account is required to get started with Google Colab to write Python Code

Python Programming experience is an advantage but not required

Description

Are you ready to unlock the power of deep learning and revolutionize your career? Dive into the captivating realm of Deep Learning with our comprehensive course Deep Learning: Convolutional Neural Networks (CNNs) using Python and Pytorch. Discover the power and versatility of CNNs, a cutting-edge technology revolutionizing the field of artificial intelligence. With hands-on Python tutorials, you’ll unravel the intricacies of CNN architectures, mastering their design, implementation, and optimization. One of the key advantages of deep CNN is its ability to automatically learn features at different levels of abstraction. Lower layers of the network learn low-level features, such as edges or textures, while higher layers learn more complex and abstract features. This hierarchical representation allows deep learning models to capture and understand complex patterns in the data, enabling them to excel in tasks such as image recognition, natural language processing, speech recognition, and many others.Introducing our comprehensive deep CNNs with python course, where you’ll dive deep into Convolutional Neural Networks and emerge with the skills you need to succeed in the modern era of AI. Computer Vision refers to AI algorithms designed to extract knowledge from images or videos. Computer vision is a field of artificial intelligence (AI) that enables computers to understand and interpret visual information from digital images or videos. It involves developing deep learning algorithms and techniques that allow machines to analyze, process, and extract meaningful insights from visual data, much like the human visual system. Convolutional Neural Networks (CNNs) are most commonly used Deep Learning technique for computer vision tasks. CNNs are well-suited for processing grid-like input data, such as images, due to their ability to capture spatial hierarchies and local patterns.In today’s data-driven world, Convolutional Neural Networks stand at the forefront of image recognition, object detection, and visual understanding tasks. Understanding CNNs is not only essential for aspiring data scientists and machine learning engineers but also for professionals seeking to leverage state-of-the-art technology to drive innovation in various domains. From self-driving cars and medical imaging to facial recognition and augmented reality, CNNs find applications across diverse industries. Whether you’re interested in revolutionizing healthcare, enhancing autonomous systems, or developing cutting-edge computer vision applications, this course equips you with the knowledge and skills to excel in any CNN-related endeavor.Course Key Learning Outcomeseep Convolutional Neural Networks with Python and Pytorch Basics to ExpertIntroduction to Deep Learning and its Building Blocks Artificial NeuronsDefine Convolutional Neural Network Architecture from Scratch with Python and PytorchHyperparameters Optimization For Convolutional Neural Networks to Improve Model PerformanceCustom Datasets with Augmentations to Increase Image Data VariabilityTraining and Testing Convolutional Neural Network using PytorchPerformance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNsVisualize Confusion Matrix and Calculate Precision, Recall, and F1 ScoreAdvanced CNNs for Segmentation, Object tracking, and Pose Estimation.Pretrained Convolutional Neural Networks and their ApplicationsTransfer Learning using Convolutional Neural Networks ModelsConvolutional Neural Networks Encoder Decoder ArchitecturesYOLO Convolutional Neural Networks for Computer Vision TasksRegion-based Convolutional Neural Networks for Object DetectionIn this comprehensive course you will start from building Deep Convolutional Neural Networks architecture from scratch with Dataset Augmentation with different transformations to increase image variability , HyperParameteres Optimization before training the model to improve performance, Model validation on Test Images, Performance metrics calculation including Accuracy, Precision, Recall, F1 score and Confusion matrix visualization to see detailed insights into the model’s performance, beyond simple metrics. Then you will move forward to advanced CNN Architectures Including RESNT, ALEXNET for Images Classification, UNET, PSPNET encoder decoder Architectures for semantic segmentation, Region based CNN for OD and YOLO CNNs for real time object Detection, classification instance segmentation, object tracking, and pose estimation.Join us on this exciting journey, where you’ll not only grasp the core concepts but also unlock the door to advanced CNN architectures, equipping yourself with the skills needed to conquer the most challenging computer vision tasks with confidence and expertise. You will follow a complete pipeline to deep dive into CNN for real world applications. I will provide you the complete python code to build, train, test, and deploy CNN from scratch for different Artificial Intelligence tasks.Don’t miss out on this incredible opportunity to take your skills to the next level. Enroll now and join the thousands of students who’ve already transformed their careers with our courses. " Thank you and see you inside the class" !

Overview

Section 1: Introduction to Course

Lecture 1 Introduction

Section 2: Artificial Neurons – The building blocks of Deep Learning

Lecture 2 Introduction to Deep Learning and Artificial Neurons

Section 3: Introduction to Convolutional Neural Networks (CNNs)

Lecture 3 Introduction to Convolutional Neural Networks (CNNs)

Section 4: Google Colab Environment Set-up for Writing Python Code

Lecture 4 Google Colab Environment for Writing Python and Pytorch Code

Section 5: Convolutional Neural Networks from Scratch using Python

Lecture 5 Define Convolutional Neural Network Architecture from Scratch using Python

Section 6: Dataset and its Augmentation

Lecture 6 Dataset and its Augmentation

Section 7: Hyperparameters Optimization For Convolutional Neural Networks

Lecture 7 Hyperparameters Optimization For Training Models

Section 8: Training Convolutional Neural Network from Scratch

Lecture 8 Training Convolutional Neural Network from Scratch

Section 9: Validating Convolutional Neural Network on Test Images

Lecture 9 Validating Convolutional Neural Network on Test Images

Section 10: Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Lecture 10 Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Section 11: Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Lecture 11 Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Section 12: Resources: Python Code for Convolutional Neural Networks from Scratch

Lecture 12 Resources: Python Code for Convolutional Neural Networks from Scratch

Section 13: Pretrained Convolutional Neural Networks

Lecture 13 Pretrained Convolutional Neural Networks with Python

Lecture 14 Python Code to use the Pretrained CNN Models

Section 14: Transfer Learning using Convolutional Neural Networks

Lecture 15 What is Transfer Learning

Lecture 16 Transfer Learning by Fine Tuning CNNs Models

Lecture 17 Transfer Learning with CNNs Models as Fixed Feature Extractor

Lecture 18 Transfer Learning Python, Pytorch Code and Dataset

Section 15: Convolutional Neural Networks Encoder Decoder Architectures

Lecture 19 Convolutional Neural Networks Based Encoders

Lecture 20 Convolutional Neural Networks Based Decoders

Lecture 21 Multi-Task Contextual Encoder Decoder Network

Section 16: YOLO Convolutional Neural Networks

Lecture 22 YOLO Convolutional Neural Networks Architecture

Lecture 23 How YOLO Works to Identify Objects

Section 17: Region-based Convolutional Neural Networks

Lecture 24 Region-based Convolutional Neural Networks (RCNN, FAST RCNN, FASTER RCNN)

Lecture 25 Detectron2 for Ojbect Detection with PyTorch

Lecture 26 Perform Object Detection using Detectron2 Models

Lecture 27 Resources: Python and PyTorch Code for Object Detection

This course is designed for individuals with a keen interest in Deep Learning and Convolutional Neural Networks (CNNs) with Python and Pytorch to solve Real-World AI Problems.,Whether you’re a beginner looking to build a strong foundation in Computer Vision, Object Tracking, Segmentation, Pose Estimation, Classification, Object Detection or an experienced professional aiming to enhance your skills, this course provides valuable insights and hands-on experience with CNNs.

Deep Learning – Recurrent Neural Networks with TensorFlow
English | 2023 | h264, yuv420p, 1920×1080 | 48000 Hz, 2channels | Duration: 4h 6m | 729 MB​

Recurrent Neural Networks are a type of deep learning architecture designed to process sequential data, such as time series, text, speech, and video. RNNs have a memory mechanism, which allows them to preserve information from past inputs and use it to inform their predictions.
TensorFlow 2 is a popular open-source software library for machine learning and deep learning. It provides a high-level API for building and training machine learning models, including RNNs.
In this compact course, you will learn how to use TensorFlow 2 to build RNNs. We will study the Simple RNN (Elman unit), the GRU, and the LSTM, followed by investigating the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We will apply RNNs to both time series forecasting and NLP. Next, we will apply LSTMs to stock "price" predictions, but in a different way compared to most other resources. It will mostly be an investigation about what not to do and how not to make the same mistakes that most blogs and courses make when predicting stocks.
By the end of this course, you will be able to build your own build RNNs with TensorFlow 2.
What You Will Learn
Learn about simple RNNs (Elman unit)
Covers GRU (gated recurrent unit)
Learn how to use LSTM (long short-term memory unit)
Learn how to preform time series forecasting
Learn how to predict stock price and stock return with LSTM
Learn how to apply RNNs to NLP
Audience
This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement recurrent neural networks in TensorFlow 2. One must have decent Python programming skills, should know how to build a feedforward ANN in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.

Debugging and Error Handling in ASP.NET Core 10
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 51m | Size: 296 MB​

This course will teach you how to leverage error handling and debugging skills to make your code comprehensible and resilient.

What you’ll learn
You won’t get very far working with ASP.NET before you’ll have to plan for trouble and use tools to track problems down. In this course, Debugging and Error Handling in ASP.NET Core 10, you’ll gain the ability to debug effectively and how to structure your code to handle problems. First, you’ll explore debugging with an attached debugger. Next, you’ll discover troubleshooting your end-user interface with client-side debugging. Finally, you’ll learn how to use the ASP.NET Core middleware pipeline to automate your error handling. When you’re finished with this course, you’ll have the skills and knowledge of debugging and error handling needed to make your code comprehensible and resilient.

Free Download Debating 101 Master the Art of Arguing, Rebuttal & Winning
Published 4/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 16m | Size: 1.25 GB
Think Fast, Speak Smart, Win Arguments with Confidence, Logic, and Powerful Rebuttals!

What you’ll learn
Build clear, structured, and persuasive arguments from scratch
Understand the fundamentals of logic, reasoning, and critical thinking
Deliver confident and impactful speeches in debates
Master the art of crafting powerful rebuttals in real time
Analyze opponents’ arguments and find weaknesses
Adapt debating style for different audiences and situations
understand different debate formats and rules
Handle objections and counterarguments with confidence
Build credibility and authority while speaking
Improve body language, tone, and delivery for maximum impact
Apply debating skills in real-life situations (work, school, social settings)
Build confidence in expressing opinions clearly and logically
Turn complex ideas into simple, convincing points
Develop lifelong communication and critical thinking skills
Develop quick-thinking skills for impromptu debates
Requirements
No Prerequisites Required
Description
This course contains the use of artificial Intelligence.
Debating 101: Master the Art of Arguing, Rebuttal & Winning is a practical, step-by-step course designed to help you become a confident, clear, and persuasive communicator. Whether you are a beginner or someone looking to sharpen your speaking skills, this course will teach you how to think critically, structure powerful arguments, and respond effectively in any discussion.
You will learn how to break down complex ideas into simple, convincing points, use logic and evidence to support your views, and identify weaknesses in opposing arguments. The course also focuses heavily on rebuttal techniques, helping you think on your feet and respond with clarity and confidence under pressure.
Beyond theory, this course is highly practical. You will explore real-world examples, proven frameworks, and actionable techniques that can be applied in debates, workplace discussions, interviews, negotiations, and everyday conversations. You will also develop essential skills such as public speaking, active listening, persuasion, and critical thinking.
By the end of this course, you will be able to present your ideas with confidence, challenge others respectfully, and handle any argument with calmness and control. You will not just learn how to debate, but how to communicate effectively, influence others, and stand out in any conversation.
Who this course is for
Anyone who wants to learn how to win arguments flawlessly

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