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Network Troubleshooting with Wireshark
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 27m | Size: 278 MB​

Master network troubleshooting with Wireshark. Learn to identify and diagnose high latency, packet loss, and TCP resets through practical, fault-injected simulations. Pinpoint client, network, or server bottlenecks efficiently.

What you’ll learn
Unlock the secrets of network diagnostics with this hands-on course, leveraging the power of Wireshark and practical simulation techniques. This course, Network Troubleshooting with Wireshark, provides scenario-driven approaches to identifying and resolving common network connectivity and performance issues that plague modern applications. First, you’ll explore identifying and analyzing symptoms of high network latency and packet loss.
Next, you’ll discover how to pinpoint the source of slow application response times, distinguishing between client, network, and server bottlenecks.
Finally, you’ll learn to investigate network and application disconnects caused by abrupt TCP resets.
By the end of this course, you will be proficient in using Wireshark to interpret packet captures, understand the tell-tale signs of various network faults through practical exercises, and confidently diagnose whether performance problems originate from the client, the network path, or the server infrastructure. You’ll gain the practical skills to quickly isolate and troubleshoot even the most elusive network issues.

NetDevOps: Declarative Configuration Management with Ansible
.MP4, AVC, 1920×1080, 30 fps | English, AAC, 2 Ch | 1h 13m | 178 MB
Instructor: Andrew Mallett​

Network engineers often configure devices manually through the command line, repeating the same tasks across many routers and switches. As networks grow, this process becomes slow, inconsistent, and prone to human error. In this course, NetDevOps: Declarative Configuration Management with Ansible, you’ll gain the ability to automate network configuration using Ansible and a declarative, desired-state approach.

First, you’ll explore how to design YAML-based inventories and create playbooks that manage network devices in a structured way. Next, you’ll discover how Jinja2 templating can dynamically generate network configurations from reusable data models. Finally, you’ll learn how to deploy and validate configurations safely using Ansible modules, check mode, and reusable roles.

When you’re finished with this course, you’ll have the skills and knowledge of Ansible-based network automation needed to deploy consistent, repeatable configurations across multiple network devices.

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Navigating Change, Risk, and Delivery in Software Projects
Duration: 1h 1m | .MP4 1280×720 30 fps(r) | AAC, 48000 Hz, 2ch | 138.69 MB
Genre: eLearning | Language: English​

Learn to anticipate, adapt, and communicate through uncertainty. This course teaches you how to manage change and risk in software projects while maintaining control, delivery, and stakeholder confidence when plans evolve.

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Navigating Change, Risk, and Delivery in Software Projects

Natural Language Processing – Transformers with Hugging Face
MP4 | Video: AVC 1280×720 | Audio: AAC 44KHz 2ch | Duration: 4 Hour | 908 MB
Genre: eLearning | Language: English​

This course begins with a warm welcome and an overview of the curriculum, setting the stage for an exciting journey into the world of Natural Language Processing (NLP). The initial section ensures you are well-prepared, guiding you on how to access the necessary code and providing tips for succeeding in the course.

We delve deep into the realm of Transformers, starting from the basics of Recurrent Neural Networks (RNNs) and advancing to the attention mechanisms that power modern NLP models. The course covers a wide range of practical applications, including sentiment analysis, text generation, embeddings, semantic search, and named entity recognition. Each concept is paired with hands-on Python tutorials, enabling you to implement these techniques in real-world scenarios.

By the end of this course, you will have a solid understanding of various NLP tasks and how to approach them using Hugging Face’s powerful library. Whether you are analyzing sentiment, summarizing text, or translating languages, this course equips you with the skills to tackle these challenges efficiently. The comprehensive coverage ensures you gain both theoretical knowledge and practical experience, making you proficient in applying Transformers to solve complex NLP problems.
What you will learn

Understand the principles of RNNs and Attention mechanisms
Implement sentiment analysis and text generation in Python
Apply embeddings and semantic search techniques
Develop masked language models and NER systems
Create text summarization and neural machine translation models
Utilize zero-shot classification for advanced NLP tasks

Natural Language Processing – Transformers with Hugging Face
MP4 | Video: AVC 1280×720 | Audio: AAC 44KHz 2ch | Duration: 4 Hour | 908 MB
Genre: eLearning | Language: English​

This course begins with a warm welcome and an overview of the curriculum, setting the stage for an exciting journey into the world of Natural Language Processing (NLP). The initial section ensures you are well-prepared, guiding you on how to access the necessary code and providing tips for succeeding in the course.

We delve deep into the realm of Transformers, starting from the basics of Recurrent Neural Networks (RNNs) and advancing to the attention mechanisms that power modern NLP models. The course covers a wide range of practical applications, including sentiment analysis, text generation, embeddings, semantic search, and named entity recognition. Each concept is paired with hands-on Python tutorials, enabling you to implement these techniques in real-world scenarios.

By the end of this course, you will have a solid understanding of various NLP tasks and how to approach them using Hugging Face’s powerful library. Whether you are analyzing sentiment, summarizing text, or translating languages, this course equips you with the skills to tackle these challenges efficiently. The comprehensive coverage ensures you gain both theoretical knowledge and practical experience, making you proficient in applying Transformers to solve complex NLP problems.
What you will learn

Understand the principles of RNNs and Attention mechanisms
Implement sentiment analysis and text generation in Python
Apply embeddings and semantic search techniques
Develop masked language models and NER systems
Create text summarization and neural machine translation models
Utilize zero-shot classification for advanced NLP tasks

Natural Language Processing – Probability Models in Python
MP4 | Video: AVC 1280×720 | Audio: AAC 44KHz 2ch | Duration: 5H 11M | 1.04 GB
Genre: eLearning | Language: English​

Beginning with an introduction and course outline, students are offered a special offer to kickstart their learning journey in this course. Initial sections guide you through the setup process and provide the necessary code resources. The course emphasizes strategies for success, ensuring learners are well-prepared from the start.

The course delves into spam detection, beginning with a problem description and intuition behind Naive Bayes. Participants engage in exercises and learn to address class imbalance while evaluating models using ROC, AUC, and F1 scores. Practical implementation in Python solidifies understanding. Following this, the course transitions to sentiment analysis, covering problem description, logistic regression, and training. Exercises and Python-based projects enable learners to apply concepts effectively.

Text summarization is thoroughly explored with sections on vector-based methods and TextRank, from basic to advanced levels. Practical Python sessions ensure learners can implement these techniques. The course culminates with topic modeling, introducing LDA and NMF methods, complemented by Python coding exercises. A deep dive into Latent Semantic Analysis and applying SVD in NLP wraps up the curriculum, ensuring a well-rounded expertise in NLP.
What you will learn

Identify and implement spam detection algorithms
Conduct sentiment analysis using logistic regression
Perform text summarization using various methods
Apply advanced techniques like TextRank for summarization
Understand and implement topic modeling with LDA and NMF
Utilize Latent Semantic Analysis in Python projects

Natural Language Processing – Deep Learning Models in Python
MP4 | Video: AVC 1280×720 | Audio: AAC 44KHz 2ch | Duration: 6.5H | 989 MB
Genre: eLearning | Language: English​

Embark on a journey into Natural Language Processing (NLP) with a focus on deep learning models using Python. The course starts with an introduction to neurons, explaining how they form the basic building blocks of neural networks. You will learn to fit lines and prepare classification codes, culminating in practical text classification tasks using TensorFlow.

Progressing to Feedforward Artificial Neural Networks (ANNs), you will delve into forward propagation, activation functions, and multiclass classification. The course includes extensive code preparation for text classification in TensorFlow, covering text preprocessing, embeddings, and advanced techniques like Continuous Bag of Words (CBOW). This section ensures you understand the geometrical aspects and hyperparameter tuning.

The course then explores Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), crucial for advanced NLP tasks. You will learn the intricacies of convolutions, CNN architecture, and their application to text. The RNN section covers simple RNNs, GRUs, and LSTMs, with hands-on exercises in text classification, parts-of-speech tagging, and named entity recognition in TensorFlow. Each section is designed to build your skills progressively, ensuring a deep understanding of both theoretical concepts and practical applications.
What you will learn

Develop a solid understanding of neural networks and their applications in NLP.
Implement text classification models using TensorFlow.
Master advanced NLP techniques like embeddings and named entity recognition.
Apply convolutional and recurrent neural networks to real-world NLP tasks.
Optimize model performance through effective hyperparameter tuning.
Advanced techniques like CBOW and hyperparameter tuning.

Natural Language Processing & Deep Learning: Zero to Hero
Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | VTT | Size: 8.43 GB | Duration: 15h 29m​

Linguistics & Machine Learning: Grammar Syntax, Sentiment, ScrapeTweets, RNN/LSTM,Chatbot, SQuAD, Summary, Audio To Text

What you’ll learn
Libraries: Tensorflow, Pytorch, NLTK, SpaCy, Sci-kit Learn, Twint
Linguistics Foundation To Help Learn NLP Concepts
Deep Learning: Neural Networks, RNN, LSTM Theory & Practical Projects
Machine Reading Comprehension: Create A Question Answering System with SQuAD
No Tedious Anaconda or Jupyter Installs: Use Modern Google Colab Cloud-Based Notebooks for using Python
How To Build Generative AI Chatbots
Create A Netflix Recommendation System With Word2Vec
Perform Sentiment Analysis on Steam Game Reviews
Convert Speech To Text
Machine Learning Modelling Techniques
Markov Property – Theory & Practical
Optional Python For Beginners Section
Cosine-Similarity & Vectors
Word Embeddings: My Favourite Topic Taught In Depth
Scrape Unlimited Tweets Using An Open Source Intelligence Tool
Speech Recognition
LSTM Fake News Detector
Context-Free Grammar Syntax
Scrape Wikipedia & Create An Article Summarizer

Description
This course takes you from a beginner level to being able to understand NLP concepts, linguistic theory, and then practice these basic theories using Python – with very simple examples as you code along with me.

Get experience doing a full real-world workflow from Collecting your own Data to NLP Sentiment Analysis using Big Datasets of over 50,000 Tweets.

Data collection: Scrape Twitter using: OSINT – Open Source Intelligence Tools: Gather text data using real-world techniques. In the real world, in many instances you would have to create your own data set; i.e source your data instead of downloading a clean, ready-made file online

Use Python to search relevant tweets for your study and NLP to analyze sentiment.

Language Syntax: Most NLP courses ignore the core domain of Linguistics. This course explains the fundamentals of Language Syntax & Parse trees – the foundation of how a machine can interpret the structure of s sentence.

New to Python: If you are new to Python or any computer programming, the course instructions make it easy for you to code together with me. I explain code line by line.

No Installs, we go straight to coding – Code using Google Colab – to be up-to-date with what’s being used in the Data Science world 2021!

The gentle pace takes you gradually from these basics of NLP foundation to being able to understand Mathematical & Linguistic (English-Language-based, Non-Mathematical) theories of Deep Learning.

Natural Language Processing Foundation

Linguistics & Semantics – study the background theory on natural language to better understand the Computer Science applications

Pre-processing Data (cleaning)

Regex, Tokenization, Stemming, Lemmatization

Name Entity Recognition (NER)

Part-of-Speech Tagging

Libraries:

NLTK

Sci-kit Learn

Tensorflow

Pytorch

SpaCy

DeepPavlov

Twint

The topics outlined below are taught using practical Python projects!

Parse Tree

Markov Chain

Text Classification & Sentiment Analysis

Company Name Generator

Unsupervised Sentiment Analysis

Topic Modelling

Word Embedding with Deep Learning Models

Open Domain Question Answering (like asking Google)

Closed Domain Question Answering (Like asking a Restaurant-Finder bot)

LSTM using TensorFlow, Keras Sequence Model

Speech Recognition

Convert Speech to Text

Neural Networks

This is taught from first principles – comparing Biological Neurons in the Human Brain to Artificial Neurons.

Practical project: Sentiment Analysis of Steam Reviews

Word Embedding: This topic is covered in detail, similar to an undergraduate course structure that includes the theory & practical examples of:

TF-IDF

Word2Vec

One Hot Encoding

gloVe

Deep Learning

Recurrent Neural Networks

LSTMs

Get introduced to Long short-term memory and the recurrent neural network architecture used in the field of deep learning.

Build models using LSTMs

Who this course is for:
Anyone who is curious about data science & NLP
Those who are in the Business & Marketing world – learn use NLP to gain insight into customers & products. Can help at interviews & job promotions.
If you intend to enrol in an NLP/Data Science course but are a total newbie, complete this course before to avoid being lost in class since it can seem overwhelming if classmates already have a foundation in Python or Datascience.