Collection of lectures and lab lectures on machine learning and deep learning. Lab practices in Python and TensorFlow.

🤖 HealthCare ChatBot Major -1 (4th year - 7th semester) Health Care Chat-Bot is a Healthcare Domain Chatbot to simulate the predictions of a General Physician. ChatBot can be described as software that can chat with people using artificial intelligence. These software are used to perform tasks such as quickly responding to users, informing them, helping to purchase products and providing better service to customers. We have made a healthcare based chatbot. The three main areas where chatbots can be used are diagnostics, patient engagement outside medical facilities, and mental health. In our major we are working on diagnostic. 📃 Brief A chatbot is an artificially intelligent creature which can converse with humans. This could be text-based, or a spoken conversation. In our project we will be using Python as it is currently the most popular language for creating an AI chatbot. In the middle of AI chatbot, architecture is the Natural Language Processing (NLP) layer. This project aims to build an user-friendly healthcare chatbot which facilitates the job of a healthcare provider and helps improve their performance by interacting with users in a human-like way. Through chatbots one can communicate with text or voice interface and get reply through artificial intelligence Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as E-commerce customer service, Call centres, Internet gaming,etc. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for. 📜 Problem Statement During the pandemic, it is more important than ever to get your regular check-ups and to continue to take prescription medications. The healthier you are, the more likely you are to recover quickly from an illness. In this time patients or health care workers within their practice, providers are deferring elective and preventive visits, such as annual physicals. For some, it is not possible to consult online. In this case, to avoid false information, our project can be of help. 📇 Features Register Screen. Sign-in Screen. Generates database for user login system. Offers you a GUI Based Chatbot for patients for diagnosing. [A pragmatic Approach for Diagnosis] Reccomends an appropriate doctor to you for the following symptom. 📜 Modules Used Our program uses a number of python modules to work properly: tkinter os webbrowser numpy pandas matplotlib 📃 Algorithm We have used Decision tree for our health care based chat bot. Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.It usually mimic human thinking ability while making a decision, so it is easy to understand. :suspect: Project Members Anushka Bansal - 500067844 - R164218014 Shreya Sharma - 500068573 - R164218070 Silvi - 500069092 - R164218072 Ishika Agrawal - 500071154 - R164218097

Welcome to my comprehensive course on python programming and ethical hacking. The course assumes you have NO prior knowledge in any of these topics, and by the end of it you'll be at a high intermediate level being able to combine both of these skills to write python programs to hack into computer systems exactly the same way that black hat hackers do. That's not all, you'll also be able to use the programming skills you learn to write any program even if it has nothing to do with hacking. This course is highly practical but it won't neglect the theory, we'll start with basics of ethical hacking and python programming and installing the needed software. Then we'll dive and start programming straight away. You'll learn everything by example, by writing useful hacking programs, no boring dry programming lectures. The course is divided into a number of sections, each aims to achieve a specific goal, the goal is usually to hack into a certain system! We'll start by learning how this system work and its weaknesses, then you'll lean how to write a python program to exploit these weaknesses and hack the system. As we write the program I will teach you python programming from scratch covering one topic at a time. By the end of the course you're going to have a number of ethical hacking programs written by yourself (see below) from backdoors, keyloggers, credential harvesters, network hacking tools, website hacking tools and the list goes on. You'll also have a deep understanding on how computer systems work, how to model problems, design an algorithm to solve problems and implement the solution using python. As mentioned in this course you will learn both ethical hacking and programming at the same time, here are some of the topics that will be covered in the course: Programming topics: Writing programs for python 2 and 3. Using modules and libraries. Variables, types ...etc. Handling user input. Reading and writing files. Functions. Loops. Data structures. Regex. Desiccation making. Recursion. Threading. Object oriented programming. Packet manipulation using scapy. Netfilterqueue. Socket programming. String manipulation. Exceptions. Serialisation. Compiling programs to binary executables. Sending & receiving HTTP requests. Parsing HTML. + more! Hacking topics: Basics of network hacking / penetration testing. Changing MAC address & bypassing filtering. Network mapping. ARP Spoofing - redirect the flow of packets in a network. DNS Spoofing - redirect requests from one website to another. Spying on any client connected to the network - see usernames, passwords, visited urls ....etc. Inject code in pages loaded by any computer connected to the same network. Replace files on the fly as they get downloaded by any computer on the same network. Detect ARP spoofing attacks. Bypass HTTPS. Create malware for Windows, OS X and Linux. Create trojans for Windows, OS X and Linux. Hack Windows, OS X and Linux using custom backdoor. Bypass Anti-Virus programs. Use fake login prompt to steal credentials. Display fake updates. Use own keylogger to spy on everything typed on a Windows & Linux. Learn the basics of website hacking / penetration testing. Discover subdomains. Discover hidden files and directories in a website. Run wordlist attacks to guess login information. Discover and exploit XSS vulnerabilities. Discover weaknesses in websites using own vulnerability scanner. Programs you'll build in this course: You'll learn all the above by implementing the following hacking programs mac_changer - changes MAC Address to anything we want. network_scanner - scans network and discovers the IP and MAC address of all connected clients. arp_spoofer - runs an arp spoofing attack to redirect the flow of packets in the network allowing us to intercept data. packet_sniffer - filters intercepted data and shows usernames, passwords, visited links ....etc dns_spoofer - redirects DNS requests, eg: redirects requests to from one domain to another. file_interceptor - replaces intercepted files with any file we want. code_injector - injects code in intercepted HTML pages. arpspoof_detector - detects ARP spoofing attacks. execute_command payload - executes a system command on the computer it gets executed on. execute_and_report payload - executes a system command and reports result via email. download_and_execute payload - downloads a file and executes it on target system. download_execute_and_report payload - downloads a file, executes it, and reports result by email. reverse_backdoor - gives remote control over the system it gets executed on, allows us to Access file system. Execute system commands. Download & upload files keylogger - records key-strikes and sends them to us by email. crawler - discovers hidden paths on a target website. discover_subdomains - discovers subdomains on target website. spider - maps the whole target website and discovers all files, directories and links. guess_login - runs a wordlist attack to guess login information. vulnerability_scanner - scans a target website for weaknesses and produces a report with all findings. As you build the above you'll learn: Setting up a penetration testing lab to practice hacking safely. Installing Kali Linux and Windows as virtual machines inside ANY operating system. Linux Basics. Linux terminal basics. How networks work. How clients communicate in a network. Address Resolution Protocol - ARP. Network layers. Domain Name System - DNS. Hypertext Transfer Protocol - HTTP. HTTPS. How anti-virus programs work. Sockets. Connecting devices over TCP. Transferring data over TCP. How website work. GET & POST requests. And more! By the end of the course you're going to have programming skills to write any program even if it has nothing to do with hacking, but you'll learn programming by programming hacking tools! With this course you'll get 24/7 support, so if you have any questions you can post them in the Q&A section and we'll respond to you within 15 hours. Notes: This course is created for educational purposes only and all the attacks are launched in my own lab or against devices that I have permission to test. This course is totally a product of Zaid Sabih & zSecurity, no other organisation is associated with it or a certification exam. Although, you will receive a Course Completion Certification from Udemy, apart from that NO OTHER ORGANISATION IS INVOLVED. What you’ll learn 170+ videos on Python programming & ethical hacking Install hacking lab & needed software (on Windows, OS X and Linux) Learn 2 topics at the same time - Python programming & Ethical Hacking Start from 0 up to a high-intermediate level Write over 20 ethical hacking and security programs Learn by example, by writing exciting programs Model problems, design solutions & implement them using Python Write programs in Python 2 and 3 Write cross platform programs that work on Windows, OS X & Linux Have a deep understanding on how computer systems work Have a strong base & use the skills learned to write any program even if its not related to hacking Understand what is Hacking, what is Programming, and why are they related Design a testing lab to practice hacking & programming safely Interact & use Linux terminal Understand what MAC address is & how to change it Write a python program to change MAC address Use Python modules and libraries Understand Object Oriented Programming Write object oriented programs Model & design extendable programs Write a program to discover devices connected to the same network Read, analyse & manipulate network packets Understand & interact with different network layers such as ARP, DNS, HTTP ....etc Write a program to redirect the flow of packets in a network (arp spoofer) Write a packet sniffer to filter interesting data such as usernames and passwords Write a program to redirect DNS requests (DNS Spoofer) Intercept and modify network packets on the fly Write a program to replace downloads requested by any computer on the network Analyse & modify HTTP requests and responses Inject code in HTML pages loaded by any computer on the same network Downgrade HTTPS to HTTP Write a program to detect ARP Spoofing attacks Write payloads to download a file, execute command, download & execute, download execute & report .....etc Use sockets to send data over TCP Send data reliably over TCP Write client-server programs Write a backdoor that works on Windows, OS X and Linux Implement cool features in the backdoor such as file system access, upload and download files and persistence Write a remote keylogger that can register all keystrikes and send them by Email Interact with files using python (read, write & modify) Convert python programs to binary executables that work on Windows, OS X and Linux Convert malware to torjans that work and function like other file types like an image or a PDF Bypass Anti-Virus Programs Understand how websites work, the technologies used and how to test them for weaknesses Send requests towebsites and analyse responses Write a program that can discover hidden paths in a website Write a program that can map a website and discover all links, subdomains, files and directories Extract and submit forms from python Run dictionary attacks and guess login information on login pages Analyse HTML using Python Interact with websites using Python Write a program that can discover vulnerabilities in websites Are there any course requirements or prerequisites? Basic IT knowledge No Linux, programming or hacking knowledge required. Computer with a minimum of 4GB ram/memory Operating System: Windows / OS X / Linux Who this course is for: Anybody interested in learning Python programming Anybody interested in learning ethical hacking / penetration testing Instructor User photo Zaid Sabih Ethical Hacker, Computer Scientist & CEO of zSecurity My name is Zaid Al-Quraishi, I am an ethical hacker, a computer scientist, and the founder and CEO of zSecurity. I just love hacking and breaking the rules, but don’t get me wrong as I said I am an ethical hacker. I have tremendous experience in ethical hacking, I started making video tutorials back in 2009 in an ethical hacking community (iSecuri1ty), I also worked as a pentester for the same company. In 2013 I started teaching my first course live and online, this course received amazing feedback which motivated me to publish it on Udemy. This course became the most popular and the top paid course in Udemy for almost a year, this motivated me to make more courses, now I have a number of ethical hacking courses, each focusing on a specific field, dominating the ethical hacking topic on Udemy. Now I have more than 350,000 students on Udemy and other teaching platforms such as StackSocial, StackSkills and zSecurity. Instructor User photo z Security Leading provider of ethical hacking and cyber security training, zSecurity is a leading provider of ethical hacking and cyber security training, we teach hacking and security to help people become ethical hackers so they can test and secure systems from black-hat hackers. Becoming an ethical hacker is simple but not easy, there are many resources online but lots of them are wrong and outdated, not only that but it is hard to stay up to date even if you already have a background in cyber security. Our goal is to educate people and increase awareness by exposing methods used by real black-hat hackers and show how to secure systems from these hackers. Video course

Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis.

(Phased out) A series of challenges for practicing your Machine Learning and Deep Learning skills

Image Classifier Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smartphone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice, you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories. When you've completed this project, you'll have an application that can be trained on any set of labelled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. This is the final Project of the Udacity AI with Python Nanodegree Prerequisites The Code is written in Python 3.6.5 . If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install pip run in the command Line python -m ensurepip -- default-pip to upgrade it python -m pip install -- upgrade pip setuptools wheel to upgrade Python pip install python -- upgrade Additional Packages that are required are: Numpy, Pandas, MatplotLib, Pytorch, PIL and json. You can donwload them using pip pip install numpy pandas matplotlib pil or conda conda install numpy pandas matplotlib pil In order to intall Pytorch head over to the Pytorch site select your specs and follow the instructions given. Viewing the Jyputer Notebook In order to better view and work on the jupyter Notebook I encourage you to use nbviewer . You can simply copy and paste the link to this website and you will be able to edit it without any problem. Alternatively you can clone the repository using git clone https://github.com/fotisk07/Image-Classifier/ then in the command Line type, after you have downloaded jupyter notebook type jupyter notebook locate the notebook and run it. Command Line Application Train a new network on a data set with train.py Basic Usage : python train.py data_directory Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains Options: Set direcotry to save checkpoints: python train.py data_dor --save_dir save_directory Choose arcitecture (alexnet, densenet121 or vgg16 available): pytnon train.py data_dir --arch "vgg16" Set hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_layer1 120 --epochs 20 Use GPU for training: python train.py data_dir --gpu gpu Predict flower name from an image with predict.py along with the probability of that name. That is you'll pass in a single image /path/to/image and return the flower name and class probability Basic usage: python predict.py /path/to/image checkpoint Options: Return top K most likely classes: python predict.py input checkpoint ---top_k 3 Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_To_name.json Use GPU for inference: python predict.py input checkpoint --gpu Json file In order for the network to print out the name of the flower a .json file is required. If you aren't familiar with json you can find information here. By using a .json file the data can be sorted into folders with numbers and those numbers will correspond to specific names specified in the .json file. Data and the json file The data used specifically for this assignemnt are a flower database are not provided in the repository as it's larger than what github allows. Nevertheless, feel free to create your own databases and train the model on them to use with your own projects. The structure of your data should be the following: The data need to comprised of 3 folders, test, train and validate. Generally the proportions should be 70% training 10% validate and 20% test. Inside the train, test and validate folders there should be folders bearing a specific number which corresponds to a specific category, clarified in the json file. For example if we have the image a.jpj and it is a rose it could be in a path like this /test/5/a.jpg and json file would be like this {...5:"rose",...}. Make sure to include a lot of photos of your catagories (more than 10) with different angles and different lighting conditions in order for the network to generalize better. GPU As the network makes use of a sophisticated deep convolutional neural network the training process is impossible to be done by a common laptop. In order to train your models to your local machine you have three options Cuda -- If you have an NVIDIA GPU then you can install CUDA from here. With Cuda you will be able to train your model however the process will still be time consuming Cloud Services -- There are many paid cloud services that let you train your models like AWS or Google Cloud Coogle Colab -- Google Colab gives you free access to a tesla K80 GPU for 12 hours at a time. Once 12 hours have ellapsed you can just reload and continue! The only limitation is that you have to upload the data to Google Drive and if the dataset is massive you may run out of space. However, once a model is trained then a normal CPU can be used for the predict.py file and you will have an answer within some seconds. Hyperparameters As you can see you have a wide selection of hyperparameters available and you can get even more by making small modifications to the code. Thus it may seem overly complicated to choose the right ones especially if the training needs at least 15 minutes to be completed. So here are some hints: By increasing the number of epochs the accuracy of the network on the training set gets better and better however be careful because if you pick a large number of epochs the network won't generalize well, that is to say it will have high accuracy on the training image and low accuracy on the test images. Eg: training for 12 epochs training accuracy: 85% Test accuracy: 82%. Training for 30 epochs training accuracy 95% test accuracy 50%. A big learning rate guarantees that the network will converge fast to a small error but it will constantly overshot A small learning rate guarantees that the network will reach greater accuracies but the learning process will take longer Densenet121 works best for images but the training process takes significantly longer than alexnet or vgg16 *My settings were lr=0.001, dropoup=0.5, epochs= 15 and my test accuracy was 86% with densenet121 as my feature extraction model. Pre-Trained Network The checkpoint.pth file contains the information of a network trained to recognise 102 different species of flowers. I has been trained with specific hyperparameters thus if you don't set them right the network will fail. In order to have a prediction for an image located in the path /path/to/image using my pretrained model you can simply type python predict.py /path/to/image checkpoint.pth Contributing Please read CONTRIBUTING.md for the process for submitting pull requests. Authors Shanmukha Mudigonda - Initial work Udacity - Final Project of the AI with Python Nanodegree

Delve into practical computer vision and image processing projects and get up to speed with advanced object detection techniques and machine learning algorithms Key Features Discover best practices for engineering and maintaining OpenCV projects Explore important deep learning tools for image classification Understand basic image matrix formats and filters Book Description OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI. Created by: Deeplearning.AI

Machine Learning Function Approximation: This code implements the fully-connected Deep Neural Network (DNN) architectures considered in the paper "The gap between theory and practice in function approximation with deep neural networks" available at https://arxiv.org/abs/2001.07523

Deep learning and machine learning example codes for practice

Examples of how machine learning and deep learning can be applied in practice

About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Machine learning and deep learning notes, practice, formula derivation.

52 Books in 52 weeks. Includes book summaries for Startups, Business Best Practices, Strategy, Artificial Intelligence, Machine and Deep Learning, Data Science, Game Theory, Global Politics and Economics.

This is the coding practice for AI on IoT security, or the state-of-the art machine learning and deep learning techniques to construct an intelligent security model for the Internet of Things.

I need to understand how to use the tools to build scalable AI-powered algorithms. All of the file I have practice during taking the course Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning.

Tool wear monitoring plays an important role in improving product quality and machining efficiency of high-speed milling. As a typical data-driven algorithm, deep learning has been widely studied in tool wear monitoring, but it is rarely applied in practice as an independent algorithm up to now. This is mainly because the interpretability of deep learning does not meet the requirements of the industrial field. In this study, a novel attention-based dual-scale hierarchical LSTM (ADHL) is proposed. The ADHL can not only accurately monitor tool wear, but also access the interpretability from aspect of structure design and feature extraction. Because the prior knowledge of periodicity is introduced into the structure design, the extracted multi-scale features can cover almost all the characteristic periods. In addition, the periodicity of the features of interest can be analyzed based on the attention distribution. The effectiveness and feasibility of this method are verified from many perspectives on the high-speed milling experiments. To our best knowledge, this is the first attempt on the interpretability of deep-learning based tool condition monitoring algorithms.

About this Specialization .. AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.

Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis.

Data Science Project Governance Framework is a framework that can be followed by any new Data Science business or team. It will help in formulating strategies around how to leverage Data Science as a business, how to architect Data Science based solutions and team formation strategy, ROI calculation approaches, typical Data Science project lifecycle components, commonly available Deep Learning toolsets and frameworks and best practices used by Data Scientists. A lot of research is happening all around the world in various domains to leverage Deep Learning, Machine Learning and Data Science based solutions to solve problems that would otherwise be impossible to solve using simple rule based systems. All the major players in the market and businesses are also getting started and setting up new Data Science teams to take advantages of modern State-of-the-Art ML/DL techniques. Even though most of the Data Scientists are great at knowledge of mathematical modeling techniques, they lack the business acumen and management knowledge to drive Data Science based solutions in a corporate/MNC setup. On the other hand, management executives in most of the corporates/MNCs do not have first hand knowledge of setting up new Data Science team and approach to solving business problems using Data Science. This framework intends to help bridge the above mentioned gap and provide Executives and Data Scientists with a common ground around which they can easily build any Data Science business/team from ground zero.

In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.

DSCode is a free, comprehensive learning platform for mastering Data Science, AI, and Machine Learning. With 445+ curated problems across 16 topics including Python, ML, Deep Learning, NLP, Computer Vision, Gen AI, RAG, and AI Agents, DSCode helps you build job-ready skills through hands-on practice.

Assignments from Berkeley's stat157 Deep Learning course. This class provides a practical introduction to deep learning, including theoretical motivations and how to implement it in practice. As part of the course we will cover multilayer perceptrons, backpropagation, automatic differentiation, and stochastic gradient descent. Moreover, we introduce convolutional networks for image processing, starting from the simple LeNet to more recent architectures such as ResNet for highly accurate models. Secondly, we discuss sequence models and recurrent networks, such as LSTMs, GRU, and the attention mechanism. Throughout the course we emphasize efficient implementation, optimization and scalability, e.g. to multiple GPUs and to multiple machines. The goal of the course is to provide both a good understanding and good ability to build modern nonparametric estimators.

Explore the boundaries of Python as you journey through 90 comprehensive sessions tailored for mastering machine learning and AI. From foundational Python skills to advanced deep learning techniques, this program offers a structured path to expertise, blending theory with hands-on practice.

Course description: "Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas."

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

This repository serves as a collection of various papers and practice codes related to statistics, machine learning, and deep learning.

# [Deep Learning Specialization on Coursera](https://www.coursera.org/specializations/deep-learning) **Master Deep Learning, and Break into AI** Instructor: [Andrew Ng](http://www.andrewng.org/) This repo contains all my work for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, [Deep Learning Specialization on Coursera](https://www.coursera.org/specializations/deep-learning). ## Goals - Learn the foundations of Deep Learning - Understand how to build neural networks - Learn how to lead successful machine learning projects - Learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. - Work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. - Practice all these ideas in Python and in TensorFlow.

my deep learning, data science and machine learning practices