#building-machine-learning-projects-with-tensorflow

[aideasacademy]

The Ultimate AI/ML Roadmap: A Step-by-Step Guide to Mastering Artificial Intelligence

Introduction

Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries, from healthcare to finance. However, for beginners, the sheer volume of libraries, algorithms, and mathematical concepts can be overwhelming. How do you go from zero knowledge to building complex neural networks?

This AI/ML roadmap provides a structured path to help you navigate this exciting field, ensuring you build a strong foundation before tackling advanced topics.

Phase 1: The Foundations

Before diving into algorithms, you must master the tools of the trade. Skipping this step is the most common mistake aspiring data scientists make.

Mathematics for ML

You don't need a PhD in math, but you do need a solid grasp of core concepts:

Linear Algebra: Understanding vectors and matrices is crucial for data manipulation.

Calculus: Derivatives and gradients are the backbone of optimization algorithms.

Statistics & Probability: Essential for understanding data distributions and model evaluation.

Programming Skills

Python is the undisputed king of AI programming. Focus on:

Basic syntax and data structures (lists, dictionaries).

Object-Oriented Programming (OOP).

Environment management (Anaconda, Virtualenv, Jupyter Notebooks).

Phase 2: Data Analysis and Manipulation

Data is the fuel for machine learning. You must learn how to clean, visualize, and manipulate data effectively.

NumPy: For high-performance numerical computing.

Pandas: For data manipulation and analysis (DataFrames).

Matplotlib & Seaborn: For data visualization to uncover patterns and insights.

Phase 3: Core Machine Learning

Once you can handle data, it is time to learn the classic algorithms. This involves using Scikit-Learn, the industry-standard library for traditional ML.

Supervised Learning

Learning with labeled data:

Linear & Logistic Regression.

Decision Trees and Random Forests.

Support Vector Machines (SVM).

Unsupervised Learning

Finding hidden patterns in unlabeled data:

K-Means Clustering.

Principal Component Analysis (PCA) for dimensionality reduction.

Phase 4: Deep Learning and Neural Networks

This is where "AI" truly shines. Deep Learning mimics the human brain using neural networks. To master this, you will need to learn frameworks like TensorFlow or PyTorch.

ANN (Artificial Neural Networks): The basis of deep learning.

CNN (Convolutional Neural Networks): Used for image recognition and computer vision.

RNN (Recurrent Neural Networks) & Transformers: The standard for Natural Language Processing (NLP) and Large Language Models (LLMs).

Structuring Your Learning Path

Self-study is possible, but the landscape changes rapidly. Many learners find that a structured curriculum accelerates their progress significantly by filtering out noise and focusing on industry-relevant skills.

If you are looking for a comprehensive, mentor-led approach to cover everything mentioned in this roadmap, consider enrolling in a professional program. The AI & ML Training Certification offers a rigorous curriculum designed to take you from fundamentals to advanced deployment, ensuring you are job-ready.

Phase 5: Deployment and MLOps

Building a model is only half the battle; deploying it is the other. To become a full-stack ML engineer, you need to understand:

Model Deployment: Using Flask, FastAPI, or Streamlit.

Cloud Platforms: AWS, Google Cloud, or Azure AI services.

MLOps: Managing the lifecycle of ML models (versioning, monitoring).

Conclusion

The journey to mastering AI and ML is a marathon, not a sprint. By following this AI/ML roadmap, sticking to the fundamentals, and consistently practicing with real-world projects, you will position yourself for a successful career in the technology sector.

[educationmore]

Python for Beginners: Launch Your Tech Career with Coding Skills

Are you ready to launch your tech career but don’t know where to start? Learning Python is one of the best ways to break into the world of technology—even if you have zero coding experience.

In this guide, we’ll explore how Python for beginners can be your gateway to a rewarding career in software development, data science, automation, and more.

Why Python Is the Perfect Language for Beginners

Python has become the go-to programming language for beginners and professionals alike—and for good reason:

Simple syntax: Python reads like plain English, making it easy to learn.

High demand: Industries spanning the spectrum are actively seeking Python developers to fuel their technological advancements.

Versatile applications: Python's versatility shines as it powers everything from crafting websites to driving artificial intelligence and dissecting data.

Whether you want to become a software developer, data analyst, or AI engineer, Python lays the foundation.

What Can You Do With Python?

Python is not just a beginner language—it’s a career-building tool. Here are just a few career paths where Python is essential:

Web Development: Frameworks like Django and Flask make it easy to build powerful web applications. You can even enroll in a Python Course in Kochi to gain hands-on experience with real-world web projects.

Data Science & Analytics: For professionals tackling data analysis and visualization, the Python ecosystem, featuring powerhouses like Pandas, NumPy, and Matplotlib, sets the benchmark.

Machine Learning & AI: Spearheading advancements in artificial intelligence development, Python boasts powerful tools such as TensorFlow and scikit-learn.

Automation & Scripting: Simple yet effective Python scripts offer a pathway to amplified efficiency by automating routine workflows.

Cybersecurity & Networking: The application of Python is expanding into crucial domains such as ethical hacking, penetration testing, and the automation of network processes.

How to Get Started with Python

Starting your Python journey doesn't require a computer science degree. Success hinges on a focused commitment combined with a thoughtfully structured educational approach.

Step 1: Install Python

Download and install Python from python.org. It's free and available for all platforms.

Step 2: Choose an IDE

Use beginner-friendly tools like Thonny, PyCharm, or VS Code to write your code.

Step 3: Learn the Basics

Focus on:

Variables and data types

Conditional statements

Loops

Functions

Lists and dictionaries

If you prefer guided learning, a reputable Python Institute in Kochi can offer structured programs and mentorship to help you grasp core concepts efficiently.

Step 4: Build Projects

Learning by doing is key. Start small:

Build a calculator

Automate file organization

Create a to-do list app

As your skills grow, you can tackle more complex projects like data dashboards or web apps.

How Python Skills Can Boost Your Career

Adding Python to your resume instantly opens up new opportunities. Here's how it helps:

Higher employability: Python is one of the top 3 most in-demand programming languages.

Better salaries: Python developers earn competitive salaries across the globe.

Remote job opportunities: Many Python-related jobs are available remotely, offering flexibility.

Even if you're not aiming to be a full-time developer, Python skills can enhance careers in marketing, finance, research, and product management.

If you're serious about starting a career in tech, learning Python is the smartest first step you can take. It’s beginner-friendly, powerful, and widely used across industries.

Whether you're a student, job switcher, or just curious about programming, Python for beginners can unlock countless career opportunities. Invest time in learning today—and start building the future you want in tech.

Globally recognized as a premier educational hub, DataMites Institute delivers in-depth training programs across the pivotal fields of data science, artificial intelligence, and machine learning. They provide expert-led courses designed for both beginners and professionals aiming to boost their careers.

Python Modules Explained - Different Types and Functions - Python Tutorial

[tech-insides]

What are the skills needed for a data scientist job?

It’s one of those careers that’s been getting a lot of buzz lately, and for good reason. But what exactly do you need to become a data scientist? Let’s break it down.

Technical Skills

First off, let's talk about the technical skills. These are the nuts and bolts of what you'll be doing every day.

Programming Skills: At the top of the list is programming. You’ll need to be proficient in languages like Python and R. These are the go-to tools for data manipulation, analysis, and visualization. If you’re comfortable writing scripts and solving problems with code, you’re on the right track.

Statistical Knowledge: Next up, you’ve got to have a solid grasp of statistics. This isn’t just about knowing the theory; it’s about applying statistical techniques to real-world data. You’ll need to understand concepts like regression, hypothesis testing, and probability.

Machine Learning: Machine learning is another biggie. You should know how to build and deploy machine learning models. This includes everything from simple linear regressions to complex neural networks. Familiarity with libraries like scikit-learn, TensorFlow, and PyTorch will be a huge plus.

Data Wrangling: Data isn’t always clean and tidy when you get it. Often, it’s messy and requires a lot of preprocessing. Skills in data wrangling, which means cleaning and organizing data, are essential. Tools like Pandas in Python can help a lot here.

Data Visualization: Being able to visualize data is key. It’s not enough to just analyze data; you need to present it in a way that makes sense to others. Tools like Matplotlib, Seaborn, and Tableau can help you create clear and compelling visuals.

Analytical Skills

Now, let’s talk about the analytical skills. These are just as important as the technical skills, if not more so.

Problem-Solving: At its core, data science is about solving problems. You need to be curious and have a knack for figuring out why something isn’t working and how to fix it. This means thinking critically and logically.

Domain Knowledge: Understanding the industry you’re working in is crucial. Whether it’s healthcare, finance, marketing, or any other field, knowing the specifics of the industry will help you make better decisions and provide more valuable insights.

Communication Skills: You might be working with complex data, but if you can’t explain your findings to others, it’s all for nothing. Being able to communicate clearly and effectively with both technical and non-technical stakeholders is a must.

Soft Skills

Don’t underestimate the importance of soft skills. These might not be as obvious, but they’re just as critical.

Collaboration: Data scientists often work in teams, so being able to collaborate with others is essential. This means being open to feedback, sharing your ideas, and working well with colleagues from different backgrounds.

Time Management: You’ll likely be juggling multiple projects at once, so good time management skills are crucial. Knowing how to prioritize tasks and manage your time effectively can make a big difference.

Adaptability: The field of data science is always evolving. New tools, techniques, and technologies are constantly emerging. Being adaptable and willing to learn new things is key to staying current and relevant in the field.

Conclusion

So, there you have it. Becoming a data scientist requires a mix of technical prowess, analytical thinking, and soft skills. It’s a challenging but incredibly rewarding career path. If you’re passionate about data and love solving problems, it might just be the perfect fit for you.

Good luck to all of you aspiring data scientists out there!

[dishachrista]

Exploring Game-Changing Applications: Your Easy Steps to Learn Machine Learning:

Machine learning technology has truly transformed multiple industries and continues to hold enormous potential for future development. If you're considering incorporating machine learning into your business or are simply eager to learn more about this transformative field, seeking advice from experts or enrolling in specialized courses is a wise step. For instance, the ACTE Institute offers comprehensive machine learning training programs that equip you with the knowledge and skills necessary for success in this rapidly evolving industry. Recognizing the potential of machine learning can unlock numerous avenues for data analysis, automation, and informed decision-making.

Now, let me share my successful journey in machine learning, which I believe can benefit everyone. These 10 steps have proven to be incredibly effective in helping me become a proficient machine learning practitioner:

Step 1: Understand the Basics

Develop a strong grasp of fundamental mathematics, particularly linear algebra, calculus, and statistics.

Learn a programming language like Python, which is widely used in machine learning and provides a variety of useful libraries.

Step 2: Learn Machine Learning Concepts

Enroll in online courses from reputable platforms like Coursera, edX, and Udemy. Notably, the ACTE machine learning course is a stellar choice, offering comprehensive education, job placement, and certification.

Supplement your learning with authoritative books such as "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron and "Pattern Recognition and Machine Learning" by Christopher Bishop.

Step 3: Hands-On Practice:

Dive into real-world projects using both simple and complex datasets. Practical experience is invaluable for gaining proficiency.

Participate in machine learning competitions on platforms like Kaggle to challenge yourself and learn from peers.

Step 4: Explore Advanced Topics

Delve into deep learning, a critical subset of machine learning that focuses on neural networks. Online resources like the Deep Learning Specialisation on Coursera are incredibly informative.

For those intrigued by language-related applications, explore Natural Language Processing (NLP) using resources like the "Natural Language Processing with Python" book by Steven Bird and Ewan Klein.

Step 5: Learn from the Community

Engage with online communities such as Reddit's r/Machine Learning and Stack Overflow. Participate in discussions, seek answers to queries, and absorb insights from others' experiences.

Follow machine learning blogs and podcasts to stay updated on the latest advancements, case studies, and best practices.

Step 6: Implement Advanced Projects

Challenge yourself with intricate projects that stretch your skills. This might involve tasks like image recognition, building recommendation systems, or even crafting your own AI-powered application.

Step 7: Stay updated

Stay current by reading research papers from renowned conferences like NeurIPS, ICML, and CVPR to stay on top of cutting-edge techniques.

Consider advanced online courses that delve into specialized topics such as reinforcement learning and generative adversarial networks (GANs).

Step 8: Build a Portfolio

Showcase your completed projects on GitHub to demonstrate your expertise to potential employers or collaborators.

Step 9: Network and Explore Career Opportunities

Attend conferences, workshops, and meetups to network with industry professionals and stay connected with the latest trends.

Explore job opportunities in data science and machine learning, leveraging your portfolio and projects to stand out during interviews.

In essence, mastering machine learning involves a step-by-step process encompassing learning core concepts, engaging in hands-on practice, and actively participating in the vibrant machine learning community. Starting from foundational mathematics and programming, progressing through online courses and projects, and eventually venturing into advanced topics like deep learning, this journey equips you with essential skills. Embracing the machine learning community and building a robust portfolio opens doors to promising opportunities in this dynamic and impactful field.

[bookmyshiksha]

Three Kinds of Python Practice Projects

A reader inquired: "Any Python practice projects we can work on for learning you can suggest?"

You bet.

1) A Django Webapp

This is particularly for people who aren't experienced in web development.

(Data scientists You're looking at me.)

Making an application for the web is a crucial ability for any programmer. It lets you apply any other programming that you have and package it into a format that's easily accessible to all.

If you've never attempted web development prior to now, this should be the top priority in comparison to other items that are on your list. (If you've completed web development then skip the next step... take yourself out of your comfortable zone.)

Which framework would you employ? Google will provide many great alternatives for you to choose from. It's not a big deal what you choose to use. You can choose the one you enjoy.

If you're looking for some recommendations, I'll give you a recommendation:

Use Django.

It's a fantastic full-stack framework and is well-documented.. If you're finding yourself having to spend more than a couple of minutes selecting a framework choose Django and start writing.

This is one possible project idea. Next step:

2) A Command Line Tool

If you're not yet able how to build command-line applications... You're not getting the full benefit.

When you have your program and package it into the form of a command-line interface that is scriptable...

The configuration can be controlled by the options available and by options and flags...

As well as outputs and inputs to the program that are that are controlled by command-line args...

This is always a way to increase the worth the program. Always. All the time.

If you've never been there prior to now... you'll need to master it.

In essence, it's that you learn how to use the "argparse" module. It's part of Python's library standard.

Some other libraries to create command line interfaces that aren't in the Python standard library. They have their own fanatical fans who have already sent me angered emails at me stuffed with misspelled words, because they had the courage to recommend Arguparse in lieu of their favourite library, libwhateverz.

Don't bother with them. Argparse is full-featured, and is difficult to improve. It also comes with a battery in Python.

The next time you write your Python program, expand it. Utilize raggares for making it automated and flexible, as well as scriptable and generally better.

Is Python More Popular Than Ruby?

This is the second project suggestion. And lastly:

3) Machine Learning

If you haven't yet ridden the hype train before then you should consider taking at least a brief day excursion.

Yes all the talk about artificial machine learning intelligence is exaggerated. But. It is a real thing as well. You will gain from knowing it.

There are two choices for how to proceed. I would suggest that you master the scikit-learn library. It has tools for unsupervised and supervised learning and also for creating pipelines.

This is one option, and one that I suggest you begin with. A different option would be to master Tensorflow. I believe you'll do better by going to Tensorflow after you've had some experience using scikit-learn. If you decide to skip ahead, you should ensure that you understand the math behind working in "compute graphs" first.

How can you make use of the new library of ML? It's ideal to apply it to the problems that you're faced with in your work. It's a challenge when you're still learning.

Also, there's a practice ground: Kaggle.

Simply search on "Kaggle Competitions", and search to"Kaggle Competitions" and then look for the "Getting Started" category. They will make it simple for you to get started.

This Powerful Python Newsletter is just for you. As the reader Charles Hayden puts it:

"I have seen a lot of books, articles, and newsletters over the years and yours is one of the best. Not just what you say about Python, but how to go about learning." If you want to learning Django so Click Python Institute in Delhi.

[thejoanglebook]

Tiny ML Air Writing Recognition with Nicla Sense ME Experimenting with air-writing recognition on Arduino’s smallest board

Software: Neuton TinyML ; Hardware: Arduino Nicla Sense ME Less than half a year ago, a new Arduino’s board, Nicla Sense ME (Motion & Environment), hit the market. And, needless to say, I couldn't wait for a chance to try this brand new device and explore its capabilities.

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https://preview.redd.it/qbfrigj9q9i81.png?width=4032&format=png&auto=webp&s=18e0ef235d128f0569c26ea83c1965b2ac66a43f

Nicla Sense ME is currently the smallest board across the entire Arduino PRO family of boards for industrial applications. It comes with a range of Bosch Sensortec smart motion and environmental sensors, and I’m quite sure that this device will be widely applicable in industrial IoT, as it’s great for various research projects and commercial use.

Just have a look at its unique characteristics:

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https://preview.redd.it/g4ba8yjaq9i81.png?width=2087&format=png&auto=webp&s=62816f2d984eb85f136d1631c9240872fe59f50e

I’m really excited about the pace of development of the TinyML field because such small devices coupled with tiny machine learning capabilities indeed demonstrate enormous potential for many industries and domains.

Now, let me share my findings after an experiment with Nicla Sense ME.

Procedure:

In my experiment, I decided to play with in-air handwriting using an accelerometer-based pen device for handwriting recognition applications in order to test the ability of Nicla Sense ME to identify and process data about handwritten numbers on the edge. Compared to handwriting based on touch devices, this task is much more complex and challenging.

The device on which the experiment was carried out consisted of a triaxial accelerometer and Nicla Sense ME. The accelerations generated by hand motions were processed on MCU for further inference using the Neuton TinyML model.

Step 1: Model Training

For model training, I created my own dataset by capturing data containing 200 samples for each handwritten digit (from 0 to 9). Each sample was recorded for 2 seconds with a frequency of 100 Hz. It's easy to calculate how many times I had to move my hands in the air to collect this data, and it's even easier to imagine what my family members were thinking about me :)

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https://reddit.com/link/sukr01/video/e6zrf6oq2di81/player

Training Dataset

I merge captured samples to a single CSV file and add a target variable using Python script. The resultant dataset consisted of three features (accelerometer axes) and 400000 rows (200 samples by 200 rows for each digit).

I uploaded the dataset in a CSV format to the Neuton TinyML platform, selected the target variable (Digit), target metric (Accuracy). And then I enabled the TinyML option, selected 8-bit calculations without float support. For this case, I activated the Digital Signal Processing option for automatic data preprocessing and feature extraction. The model was trained automatically, and nothing needed to be compressed.

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https://preview.redd.it/ap0ccqocq9i81.png?width=3360&format=png&auto=webp&s=18c452423644325431b2b42b06fb05e4b5e3b943

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https://preview.redd.it/lhmjiakdq9i81.png?width=3360&format=png&auto=webp&s=edb88e88d969da60ccf7bb006a941ae089f89305

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https://preview.redd.it/vcfnbfieq9i81.png?width=3360&format=png&auto=webp&s=fdec1f13fb9bc8476d079358a0c20d95c5aadd15

The platform allows us to see a preprocessed dataset. It merged 200 samples in one row and added some statistical features (min, max, mean, etc.)

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https://preview.redd.it/8r6q5edfq9i81.png?width=3344&format=png&auto=webp&s=34bab5db79c6607033e0b60d0afebc9a3147970e

I chose Neuton for this experiment since the platform automatically builds models that are optimal in size and accuracy. I also tried to train the same model with TensorFlow. Сheck out the comparison of the resultant metrics:

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https://preview.redd.it/hxxs461gq9i81.png?width=1686&format=png&auto=webp&s=604a2a5e40c12bd93a3883a02385c1659dcef317

Just have a look at the dramatic difference in the number of coefficients, Neuton's model has 42 times fewer coefficients than a TensorFlow one!

Step 2: Embedding into a Microcontroller

After the training was completed, I downloaded the archive containing all the necessary files for the embedding to the microcontrollers firmware project.

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https://preview.redd.it/kso9m11hq9i81.png?width=3358&format=png&auto=webp&s=34e54fb7fcc2a0450485c583b0df841590077ec7

I uploaded all the components to GitHub so that you can easily replicate my experiment for yourself: https://github.com/Neuton-tinyML/nicla_digits

Step 3: Running On-device Inferences

I connected my device to a PC via USB to display inference results. Check out the outcomes of my experiment on the video below:

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https://reddit.com/link/sukr01/video/bqyhvv9f1di81/player

As you can see, the sensor accuracy is really high so it managed to identify all the numbers with great probability. The model operates right on the device and weighs about 1 KB. That’s what I call real TinyML!

Conclusion:

I believe that the future belongs to such incredibly tiny machine learning solutions. The ability to sense and accurately process various types of data in real-time, at low energy consumption but with high computing power opens up new opportunities in many fields. The implementation of such solutions can, for instance, streamline robotic science, ease the detection of seismic activities and dangerous gases in the air, advance IoT devices that we use on a daily basis, and a lot more — the scenarios are infinite! Isn’t that inspiring?

Please write in the comments below what other experiments you want me to do with Nicla Sense ME!

submitted by /u/literallair [link] [comments] from Ai https://ift.tt/E93tcHj

[parrothacks]

Welcome to my Cyber Playground

A little about me: I am a grey-hat hacker, I mainly do bug bounties, but sometimes do a bit of...questionable work albeit never explicitly malicious.

I am a programmer and my primary programming languages are C, C++, and Python 3, Python being my favorite and the language I am most fluent in. 

I build malware and hacking tools, sometimes for fun, sometimes for curiousity, sometimes to sell for... “educational purposes” albeit I hold NO responsibility for what people do with my software.

I am 28 years old and part of a grey-hat hacking group who also has a few black-hats thrown in the mix for good measure. I am not bias against what other people do, but I have a firm belief in not harming anyone unless they harm you first.

Furthermore, most of the stuff I will be posting on here is just programming stuf, maybe some malware sourcecode, and possibly some penetration testing tools that I have developed or am in the process of developing.

My current projects are:

Building a website for my fiance with Flask, HTML5, Boostrap, and Mongo. Building a new ransomware.

Getting more familiar with different types of encryption Learning Tensorflow, Tkinter, PyQt6, and Tensorflow for machine learning. Currently I am enrolled in: Intro to Statistics IBM AI Engineering Certification Classes IBM Deep Learning IBM Cyber Security Analyst Certification Machine Learning with Python FreeCodeCamp’s Machine Learning with Python

Stuff I’ve recently finished or am interested in collaborating on: A phone number grabber that geolocates the origin of the number using Google’s phonenumber python package & geolocate package for python. (Looking to collaborate on this to make it into much more than just a geolocator and more similar to a doxer) A RAT called DEATHSDOOR which is available on my github for study. A keylogger that sends logs to email. A ransomware that needs debugging and finishing called Slenderware (please feel free to inquire about collaboration on this project) An email bomber called Slenderbomber which needs significant improvements to avoid spam ban.

An email client that I finished with my friend Fazal from Instagram, we just created the GUI for it using Tkinter and it is fully functional! Will upload videos later of the basic program pre-GUI. A Discord spammer called Yoshibot that is finished but broken due to ASCII errors and all that fun stuff.

A Telegram RAT that uses Telegram as a listener/command prompt (works splendidly!) Where I am going from here I am part of the BugCrowd platform and heavily interested in bug bounties.  I practice on HackerOne’s CTF and Over the Wire. Currently about to complete Bandit! I want to finish my certifications. I am getting married to my long-time girlfriend in December or March! I want to learn Javascript, Java, and Batch, along with finishing learning Ruby and Ruby on Rails. I also want to learn JQuery as well. Thank you for following my Tumblr if you so choose, I will try to put out content regularly along with updates from my Github!

[adalidda]

Illustration Photo: Qiskit Camp Africa 2019. Qiskit Camps are an immersive experience that consists of training sessions, deep technical talks, and a hackathon alongside the Qiskit core development team. (credits: IBM Research / Flickr Creative Commons Attribution-NoDerivs 2.0 Generic (CC BY-ND 2.0))

DSI Skills Training in Data Science, AI, Machine Learning, Big Data Computing and Business Intelligence

Big Data, data science, machine learning and artificial intelligence are driving a revolution around the world as companies rush to take advantage of their data and turn it into a competitive advantage. However there is a shortage of skilled data scientists and managers worldwide and this shortage is much worse in Africa. In this program, participants will master the data science process, from statistics and data wrangling, to advanced topics like machine learning and data storytelling, by working on real projects.

The goals of the program are to deliver:

Hands-on experience and deep understanding of key technical skills in data science including machine learning, big data computing and business intelligence. A dynamic network of colleagues who will be future leaders and data scientists. A portfolio of real-world success stories in data science, backed up by quantitative performance metrics. The ability to rapidly learn new data science skills to allow them to remain on the cutting edge despite the rapidly evolution in the field. An understanding of which areas of data science they wish to specialise in.

Among many others, the program will cover:

Advanced python skills Managing the data science project life cycle Problem solving and model selection: starting to build a solution Machine learning algorithms: which one is right? Deep learning and neural networks for AI based on Google’s Tensorflow Big data computing including Spark and related database technologies Natural language processing and machine learning for text Testing and quality assurance for data science projects Scrum as a methology for agile project management Productionalising data science projects with cloud services such as AWS Creating stunning visualisations and dashboards and compelling story-telling.

Applications

In order to apply, you will need to:

Complete the registration form by clicking the link below. As part of the registration form you will also need to upload a short CV (2 pages max.) The first round of selections will be required to complete an online test, the purpose of this is to ensure that candidates have sufficient practical coding, logic and basic statistics skills – as well as flexibility and motivation – to be successful in the program. Due to the high demand for places additional online assessments may be required before candidates are short listed. Short listed candidates will be then be invited for an online interview. We will begin offering places from December 2021. Selection will continue until all places are filled.

Scholarships

Full awards covering tuition for the complete course and travel and living expenses to attend the 2-week workshop are available for qualifying African students.

Applications for scholarships will only be accepted from students in:

South Africa, Botswana, Ghana, Kenya, Madagascar, Mauritius, Mozambique, Namibia, and Zambia

Industry participants

Some spaces may be allocated to  depending on demand. If you as an individual or a company would like to send staff on this workshop please send an email to [email protected] for further information.

Application Deadline: 30 September 2021

Check more https://adalidda.com/posts/Mp6kEJEmGx7hkRbuw/dsi-skills-training-in-data-science-ai-machine-learning-big