Nick Kolakowski’s Post

🚀 Your Ultimate Machine Learning Engineer Roadmap! 🚀 Are you ready to dive into the exciting world of Machine Learning? 🌟 Whether you're just starting out or looking to advance your skills, this comprehensive roadmap will guide you every step of the way. 🔍 Key Areas Covered: Foundations: Brush up on essential mathematics and statistics. Programming Skills: Master Python and key libraries like NumPy and pandas. Data Handling: Learn data preprocessing, cleaning, and visualization techniques. Core ML Concepts: Understand algorithms, model training, and evaluation. Advanced Topics: Explore deep learning, natural language processing, and reinforcement learning. Tools & Frameworks: Get hands-on with TensorFlow, Keras, and PyTorch. Real-World Projects: Build and deploy models to solve actual problems. 💼 Why Pursue a Career in ML? High demand and lucrative opportunities. Work on innovative and impactful projects. Continuously learn and grow in a dynamic field. Embark on your journey to becoming a Machine Learning Engineer today! 🌐 #MachineLearning #AI #DataScience #CareerPath #TechRoadmap #MLEngineer #LearningJourney . . https://lnkd.in/dpv9UUnF

A Roadmap to Becoming a Machine Learning Engineer 🚀 ====================================> Are you eager to embark on the exciting journey of becoming a Machine Learning Engineer but unsure where to start? Fear not! This beginner's guide is here to pave the way for your success. Let's dive into the foundational knowledge and essential skills you need to master to kickstart your career in machine learning. 📚 **Foundational Knowledge:** To lay a solid groundwork, familiarize yourself with key mathematical concepts like Linear Algebra, Probability & Statistics, and Calculus. Understanding fundamental algorithms and data structures is crucial for solving complex problems efficiently. 🔢 **Data Preprocessing:** Before modeling, master data preprocessing. This includes tasks such as cleaning messy data, transforming variables, performing feature engineering for insights, and conducting exploratory data analysis (EDA) for better dataset comprehension. 🛠️ **Programming Languages:** Python is the go-to language for machine learning due to its simplicity and rich libraries. Learn Python, along with Pandas, NumPy, and Matplotlib for data tasks. SQL knowledge is beneficial for database querying. 🐍 **Machine Learning Basics:** Get familiar with supervised, unsupervised, and reinforcement learning. Understand their workings and when to apply them. 🤖 **Core ML Algorithms:** Study popular algorithms like Linear Regression, Logistic Regression, Decision Trees, and SVMs. Learn their mechanics, strengths, and real-world applications. 📊 **Tools & Libraries:** Explore ML frameworks like TensorFlow, PyTorch, and Scikit-Learn. These offer powerful features for building, training, and evaluating models. 🛠️ **Deep Learning:** Delve into deep learning, training neural networks on large data volumes. Study CNNs, RNNs, and other architectures for tasks like image recognition and NLP. 🧠 **Model Evaluation:** Learn techniques like cross-validation and hyperparameter tuning to assess model performance. Understand metrics like accuracy, precision, recall, and F1-score. 📈 **Advanced Topics:** Explore transfer learning, reinforcement learning, time series analysis, and computer vision. These expand your skillset for complex problems. 🌟 **Deployment:** Understand containerization with Docker, orchestration with Kubernetes, and model serving for seamless deployment. 🚢 **Ethics:** Grasp ethical considerations like bias, fairness, explainability, privacy, and security in ML. Build models responsibly. 🤝 **Continuous Learning:** Stay updated with research papers, Kaggle competitions, open-source projects, and workshops to sharpen your skills. 📖 Embark on your journey to becoming a Machine Learning Engineer with confidence armed with this comprehensive guide. Remember, patience, persistence, and a passion for learning are the keys to success in this exciting field! 🚀

Shared this post By Previn J. Want a 30% salary increase? According to a survey for Amazon Web Services: 1,93% of employers plan Al use in 5 years, BUT 1.75% struggle to find Al-skilled candidates. 1. Al skills mean 30% higher salaries in many roles. 9 Al courses to accelerate your career, (All FREE with Certificates): #artificialintelligence #ai #stabledifusion #Growt #opportunities #concept #gpt #aiadvancements #aichallenges #aiadoption #aiandbusiness #chatgpt #chatgpttips #chatgptprompts #job #jobhelp #jobopportunity #jobopportunities #resume #resumetips #resumes #resumeservices #guide #connect #connectingpeople #connections #careersucess #certificates #increaseproductivity #increase #increasedproductivity

Machine learning (ML) is rapidly transforming the world around us. It's a powerful technology that allows computers to learn and improve without explicit programming. This writeup explores the fascinating world of machine learning, delving into its core concepts, applications, and exciting future prospects. What is machine learning? Machine Learning as defined from the Oxford dictionary is the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data. What is machine Learning engineer? A machine learning engineer (ML engineer) is a professional who designs and builds software that can automate artificial intelligence and machine learning (AI/ML) models. Skills of a machine Learning engineer 📌Ability to work in a team Excellent communication skills 📌Understanding of data structures, data modeling and software architecture 📌Deep knowledge of math, probability, statistics and algorithms 📌Outstanding analytical and problem-solving skills 📌Ability to write robust code in Python, Java and R 📌Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn) Career paths in machine learning 📌Machine Learning Engineer 📌 Data Scientist 📌Human-Centered Machine Learning Designer Relationship between machine Learning and artificial intelligence Machine learning is an application of AI. It's the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own, based on experience. Future of machine learning 📌All-purpose models: There will be models that can perform many different tasks without needing to be specifically programmed for each one. 📌Quantum computing:This could significantly speed up machine learning by allowing for complex calculations to be done much faster. 📌Easier to use tools: Machine learning will become more accessible to people who are not computer scientists, with tools that don't require a lot of coding knowledge. 📌Distributed ML Portability:This will allow data scientists to use their models on different platforms without having to start from scratch. Machine learning holds immense potential to revolutionize various industries. As the field continues to evolve, with advancements in all-purpose models, quantum computing, and user-friendly tools, machine learning is poised to become an even more pervasive force in our lives. This document has equipped you with a foundational understanding of machine learning, its key players, and its promising future. Embrace the power of machine learning and explore its potential to make a positive impact on the world.

Want a 30% salary increase? According to a survey for Amazon Web Services: ↳ 93% of employers plan AI use in 5 years, BUT ↳ 75% struggle to find AI-skilled candidates. ↳ AI skills mean 30% higher salaries in many roles. 9 AI courses to accelerate your career, (with Certificates): ✳️ General Introduction to AI 1. IBM's "AI Foundations for Everyone"    ↳ Basic AI concepts and responsible AI usage.    ↳ Level: Beginner    ↳ Duration: 4 weeks        Enroll for free: https://lnkd.in/g8PJ98tj         2. University of Pennsylvania's "AI For Business"    ↳ Applying AI in business contexts.    ↳ Level: Beginner    ↳ Duration: 4 weeks        Enroll for free: https://lnkd.in/gfHQbc8C         3. Intel's Introduction to AI    ↳ End-to-end AI course on ML, DL, NLP, and more.    ↳ Level: Intermediate    ↳ Duration: 8 weeks        Enroll for free: https://lnkd.in/gAEVZHA7 ✳️ Specialised AI Applications 1. IBM Applied AI Professional Certificate    ↳ Learn Python, ML, and AI with chatbot projects.    ↳ Level: Beginner    ↳ Duration: 3 months        Enroll for free: https://lnkd.in/gj-6XsP6         2. IBM's Machine Learning    ↳ Prepare for a career in machine learning    ↳ Level: Intermediate    ↳ Duration: 3 months        Enroll for free: https://lnkd.in/gahrieJe         3. Vanderbilt University’s "Prompt Engineering"    ↳ Master prompt engineering patterns & techniques    ↳ Level: Beginner    ↳ Duration: 4 weeks        Enroll for free: https://lnkd.in/gnhNGNSk ✳️ Careers in AI 1. IBM Data Science Professional Certificate    ↳ Learn Python, SQL, ML, analysis, visualization.    ↳ Level: Beginner    ↳ Duration: 5 months        Enroll for free: https://lnkd.in/gCi2sv2Q         2. Georgia Tech’s "Machine Learning" Course    ↳ Machine learning principles and applications.    ↳ Level: Intermediate    ↳ Duration: 4 months        Enroll for free: https://lnkd.in/g3PB8jtD         3. Google' Advanced Data Analytics    ↳ Machine learning principles and applications.    ↳ Level: Advanced    ↳ Duration: 6 months        Enroll for free: https://lnkd.in/gWriWuHA Start learning AI today and shape your future! [Save for later] 🔁 Repost to help others learn about AI 👉 Follow Previn J. for more. P.S. How often are you using AI in your work? #ai #learnai #career #careergrowth

"The essence of learning "Coding" is not to entirely write them. You will need to read/study more codes than you write on your career journey" I attended a Google Generative AI event some weeks ago, where I was inspired by the speaker's words, a Machine Learning Engineer; my forewords. Journey with me… Last week, I completed an Electricity Forecasting Hybrid Model that uses a combination of Linear Regressor, ARIMA and Machine Learning techniques. The Linear Regressor and Arima served as Classical approaches in the development process. Acknowledgement: This is a study by https://lnkd.in/d2DdQA-x from Ukraine. It was a comprehensive masterpiece that I, personally, believes it's going to enhance the effectiveness and efficiency of the electricity sectors if adopted and incorporated. Initially, I intended to use datasets from Nigeria for the study while I maintain the aim of the study, which was to employ Gated Recurrent Unit (GRU) algorithm as a replacement for the Long Short-Term Memory (LSTM) algorithm used in the initial study. However, due to difficulties accessing Nigerian electricity datasets from open-source databases coupled with the deadline of the project, I couldn't consider getting such data from the Ministry (we all know how difficult such would be in Nigeria.) My colleague and I spent over two weeks studying and understanding the codes, as both R-programming and Python were used for the hybrid model development (you should understand the reason for my preamble by now. Smiles). Despite initial frustrations gotten from the complexity of the codes and the unspecified information within the dataset and code labels, I made significant progress through meticulous study. I experimented with the GRU algorithm, with all of the parameters used to train the LSTM model; initially observing lower hybrid model forecasting accuracy compared to the one built with LSTM model. However, after some adjustments of the parameters and optimization techniques, I got the exact hybrid model accuracy as that of the LSTM model. While I attempted to further play around the parameters to get a higher accuracy, I encountered challenges due to limited data points. Nevertheless, I observed a decrease in computational time, thanks to Google Collaboratory's GPU, which enabled faster model training. I was able to train the two models (using GRU) in 11 minutes and 7 minutes respectively using Collab's GPU. Without the GPU, each model trains with a minimum of 4 hours. Follow this link https://lnkd.in/dcEFCqiD to my GitHub page to check out the codes for personal studies. Despite the challenges faced, the learning experience was great. I thank my colleague, Barakat Ogunjoun, my Data Scientist, for her support throughout the course of the project. She did an excellent job writing the report of the work. Thank you Let's do more Data Ninjas♥️🔥 #ML #ELECTRICITY #FORECAST #LSTM #GRU #ARIMA #LINEAR_REGRESSOR #Google_Colab #Python #R-

What skills are needed for machine learning jobs? Machine learning jobs require a combination of technical, analytical, and problem-solving skills. Here's a breakdown: Strong foundation in mathematics and statistics: Understanding linear algebra, calculus, probability, and statistics is crucial for comprehending machine learning algorithms and models. Programming proficiency: Proficiency in programming languages commonly used in machine learning such as Python, R, or Julia is essential. Additionally, familiarity with libraries like TensorFlow, PyTorch, scikit-learn, and Keras is valuable. Understanding of machine learning algorithms and techniques: Knowledge of various machine learning algorithms (supervised learning, unsupervised learning, reinforcement learning) and techniques (classification, regression, clustering, dimensionality reduction) is fundamental. Data manipulation and preprocessing: Ability to manipulate and preprocess data using tools like pandas, NumPy, or SQL is crucial. Understanding data cleaning, feature scaling, and feature engineering is essential for model performance. Experience with machine learning frameworks and tools: Experience with frameworks like TensorFlow, PyTorch, or scikit-learn, and familiarity with cloud platforms like AWS, Azure, or Google Cloud Platform are highly valuable. Understanding of deep learning: Knowledge of deep learning concepts, architectures (CNNs, RNNs, GANs), and frameworks (TensorFlow, PyTorch) is important, especially for roles involving computer vision, natural language processing, or speech recognition. Problem-solving skills: Ability to formulate real-world problems into machine learning tasks, identify appropriate algorithms, and iteratively improve model performance. Critical thinking and analytical skills: Ability to analyze model results, interpret findings, and make data-driven decisions. Communication skills: Effective communication is crucial for explaining complex concepts to non-technical stakeholders, collaborating with multidisciplinary teams, and presenting findings. Continuous learning and adaptability: Machine learning is a rapidly evolving field, so a willingness to learn new techniques, stay updated with the latest research, and adapt to new tools and technologies is essential for success in this field.

Very smart to leverage SHAP summary plot to compare explainability of various ML models...