Machine Learning with Python – ML Programmer to ML Architect E-Learning

Algemene omschrijving

The Machine Learning with Python – ML Programmer to ML Architect E-Learning is entirely in English. As a Dutch IT training provider, we offer the information on this page in Dutch. At the bottom of the page, you will find a brief summary in English. The topics within the Machine Learning with Python – ML Programmer to ML Architect E-Learning package itself are described in English.

Machine Learning Architects spelen een sleutelrol in het analyseren van data in ware tijd en het automatiseren van processen binnen verschillende bedrijfsdomeinen. Met de Machine Learning with Python – ML Programmer to ML Architect E-Learning zul je de benodigde kennis en vaardigheden verkrijgen om AI-oplossingen te ontwikkelen die niet alleen reageren, maar ook voorspellen. Je ontdekt hoe jij data kunt vertalen naar waardevolle inzichten en innovatieve toepassingen.

Door de nadruk op computationele theorie te leggen, helpt de Machine Learning with Python – ML Programmer to ML Architect E-Learning jou om de overstap te maken van programmeur naar expert in Machine Learning (ML) en Deep Learning (DL). Je zult geavanceerde technieken leren om schaalbare en efficiënte ML-architecturen te ontwerpen en te implementeren, waarbij je AI integreert binnen complexe bedrijfsprocessen.

Of je nu een carrière in data science (datawetenschap) wilt beginnen of jouw huidige expertise in Machine Learning (ML) uit wilt breiden, de Machine Learning with Python – ML Programmer to ML Architect E-Learning biedt jou uitgebreid onderwijs op het gebied van Machine Learning (ML). Word een expert in Machine Learning (ML) en positioneer jezelf als een onmisbare professional in de wereld van kunstmatige intelligentie (AI) en automatisering.

De Machine Learning with Python – ML Programmer to ML Architect E-Learning, met meer dan 85 uur aan online cursusmateriaal, is onderverdeeld in de volgende onderdelen:

• ML Track 1: Machine Learning Programmer.
• ML Track 2: Deep Learning Programmer.
• ML Track 3: Machine Learning Engineer.
• ML Track 4: Machine Learning Architect.
• MLOps (Machine Learning Operations).

Doelgroep

De Machine Learning with Python – ML Programmer to ML Architect E-Learning is ontworpen voor zowel beginnende als ervaren professionals die uit willen blinken in Machine Learning (ML) en Deep Learning (DL).

Dit betreft met name de volgende mensen:

• ML-programmeurs.
• Softwareontwikkelaars.
• Datawetenschappers.
• AI Engineers.
• Data-analisten.
• ICT-managers.
• Productontwikkelaars.
• Bedrijfsanalisten.

Daarnaast is de Machine Learning with Python – ML Programmer to ML Architect E-Learning relevant voor professionals binnen diverse sectoren, zoals:

• Gezondheidszorg.
• Financiële dienstverlening.
• E-commerce en detailhandel.
• Productie en logistiek.

Wat is inbegrepen

De Machine Learning with Python – ML Programmer to ML Architect E-Learning is een uitgebreid zelfstudiepakket dat uit vier onderdelen bestaat en een specialisatie in MLOps (Machine Learning Operations).

Hier is een overzicht van de inhoud van de Machine Learning with Python – ML Programmer to ML Architect E-Learning:

• Machine Learning Programmer
  • Focus: Lineaire regressie, computationele theorie en trainingssets.
  • Labopdrachten: Machine Learning Programming with Python – Uitvoeren van ML-taken zoals dataverwerking, classificatie en het trainen van AI-modellen.
  • Opdracht: Final Exam: ML Programmer.
• Deep Learning Programmer
  • Focus: Neurale netwerken, CNNs, RNNs en ML-algoritmen.
  • Opdracht: Final Exam: DL Programmer.
• Machine Learning Engineer
  • Focus: Predictieve modellering, ML-modellering en architectuur.
  • Opdracht: Final Exam: ML Engineer.
• Machine Learning Architect
  • Focus: Toegepaste predictieve modellering, CNNs, RNNs en geavanceerde ML-algoritmen.
  • Labopdrachten: Architecting Advanced ML/DL Apps with Python – Werken met MLP, CNN en LSTM-modellen voor beeld- en tekstclassificatie.
  • Opdracht: Final Exam: ML Architect.
• MLOps (Machine Learning Operations)
  • Focus: Overgang van ML-experimenten naar real-world implementatie met efficiënte, schaalbare ML-pijplijnen.
  • Intro to MLOps: Basisprincipes, werklasten en beste werkwijzen in MLOps.
  • MLFlow: Tracking, beheren en implementeren van ML-modellen.
  • Data Version Control (DVC): Versiebeheer en automatisering in ML-workflows.
  • Opdracht: Final Exam: MLOps.

Wat krijg je nog meer?

• Online begeleiding: Neem contact op met jouw mentor via chat en e-mail.
• Praktische vaardigheden: Leer gebruik te maken van software als Jupyter Notebook, Anaconda, Spyder IDE, en meer.

Let op: Er is geen examenvoucher inbegrepen bij de Machine Learning with Python – ML Programmer to ML Architect E-Learning.

Voorkennis

Voordat je begint met de Machine Learning with Python – ML Programmer to ML Architect E-Learning, raden wij aan dat jij beschikt over de volgende kennis en vaardigheden, echter is dit niet verplicht:

• Basiskennis van Python en de principes van programmeren, zoals loops, functies en datastructuren.
• Een wiskundige achtergrond met begrip van lineaire algebra, statistiek en calculus.
• Ervaring of bekendheid met data-analysesoftware, zoals Pandas en NumPy.
• Inzicht in eenvoudige Machine Learning-principes, zoals classificatie, clustering of regressie.
• Bekendheid met hulpmiddelen voor het programmeren, zoals Jupyter Notebook, Anaconda of Visual Studio Code.

De Machine Learning with Python – ML Programmer to ML Architect E-Learning biedt ondersteuning voor beginners die willen leren, maar een basisniveau van technische kennis zal het leerproces versnellen. Hiermee ben je goed voorbereid om jouw kennis en vaardigheden op het gebied van Machine Learning, Deep Learning en MLOps op te doen.

Onderwerpen

Track 1: Machine Learning Programmer

In this track of the machine learning journey, the focus is linear regression, computational theory, and training sets.

Content:

E-learning courses

• NLP for ML with Python.
• Linear Algebra and Probability.
• Linear Regression Models.
• Computational Theory.
• Model Management.
• Bayesian Methods.
• Reinforcement Learning.
• Math for Data Science & Machine Learning.
• Building ML Training Sets.
• Linear Models & Gradient Descent.

Online Mentor

• You can reach your Mentor by entering chats or submitting an email.

Assessment

• Final Exam: ML Programmer.

Practice Labs: Machine Learning Programming with Python

Perform ML programming tasks with Python, such as splitting data and standardizing data, and classification using nearest neighbors and ridge regression. Then, test your skills by answering assessment questions after performing principal component analysis, visualizing correlations, training a naive Bayes model and a support vector machine model. This lab provides access to several tools commonly used in ML, including: Microsoft Excel, Visual Studio Code, Anaconda, Jupyter Notebook + JupyterHub, Pandas, NumPy, SiPy, Seaborn Library, Spyder IDE.

Track 2: Deep Learning Programmer

In this track of the machine learning journey, the focus is neural networks, CNNs, RNNs, and ML algorithms.

Content:

E-learning courses

• Getting Started with Neural Networks.
• Building Neural Networks.
• Training Neural Networks.
• Improving Neural Networks.
• ConvNets.
• Convolutional Neural Networks.
• Convo Nets for Visual Recognition.
• Fundamentals of Sequence Model.
• Build & Train RNNs.
• ML Algorithms.

Online Mentor

Assessment

• Final Exam: DL Programmer.

Track 3: Machine Learning Engineer

In this track of the machine learning journey, the focus is predictive modeling and analytics, ml modeling, and ml architecting.

Content:

E-learning collections

• Predictive Modeling.
• Planning AI Implementation.
• ML/DL in the Enterprise.
• Enterprise Services.
• Architecting Balance.
• Enterprise Architecture.
• Refactoring ML/DL Algorithms.

Online Mentor

Assessment

• Final Exam: ML Engineer.

Track 4: Machine Learning Architect

In this track of the machine learning journey, the focus is applied predictive modeling, CNNs and RNNs, and ML algorithms.

Content:

E-learning collections

• Applied Predictive Modeling.
• Implementing Deep Learning.
• Applied Deep Learning.
• Advanced Reinforcement Learning.
• ML/DL Best Practices.
• Research Topics in ML and DL.
• Deep Learning with Keras.

Online Mentor

Assessment

• Final Exam: ML Architect.

Practice Labs: Architecting Advanced ML/DL Apps with Python

Perform advanced ML/DL app architecture tasks using Python, such as loading a data set to train a simple multilayer perceptron (MLP), a Convolutional Neural Network (CNN) and an LSTM model. Then, test your skills by answering assessment questions after performing image and text classification using CNN.

MLOps (Machine Learning Operations)

This is a transformative journey through the world of MLOps (Machine Learning Operations), where data science meets engineering excellence. The MLOps journey is designed to equip you with the skills and knowledge to seamlessly transition from machine learning experimentation to real-world deployment.

Explore the principles, tools, and best practices that bridge the gap between data science and operational success. Whether you're new to MLOps or seeking to enhance your expertise, this journey will empower you to create efficient, reproducible, and scalable machine learning pipelines while overcoming the unique challenges of managing machine learning models and data at scale.

Part 1: Intro to MLOps

In this part of the MLOps Aspire Journey, the focus will be on understanding the fundamental concepts and principles that underpin this transformative field. Explore the evolution of MLOps, dissect the MLOps workflow, and delve into the challenges and best practices that await you on this exciting journey.

Course:

• Getting Started with MLOps.

Part 2: MLFlow

In this part of the MLOps Aspire Journey, the focus will be on how to track, manage, and deploy your machine learning models efficiently. From MLFlow tracking and models to model deployment and CI/CD integration, this track empowers you with essential MLOps skills.

Courses:

• MLOps with MLflow: Getting Started.
• MLOps with MLflow: Creating & Tracking ML Models.
• MLOps with MLflow: Registering & Deploying ML Models.
• MLOps with MLflow: Hyperparameter Tuning ML Models.
• MLOps with MLflow: Creating Time-series Models & Evaluating Models.
• MLOps with MLflow: Tracking Deep Learning Models.
• MLOps with MLflow: Using MLflow Projects & Recipes.

Part 3: Data Version Control

In this track of the MLOps Aspire Journey, you will discover the power of Data Version Control (DVC) and its role in simplifying experiment tracking, model management, and automation in MLOps. Explore DVC's VS Code extension, command-line tools, and open-source version control system. Learn to streamline your machine learning workflows and enable continuous machine learning with DVC.

Courses:

• MLOps with Data Version Control: Getting Started.
• MLOps with Data Version Control: Working with Pipelines & DVCLive.
• MLOps with Data Version Control: Tracking & Serving Models with DVC & MLEM.
• MLOps with Data Version Control: Tracking & Logging Deep Learning Models.
• MLOps with Data Version Control: Creating & Using DVC Pipelines.
• MLOps with Data Version Control: CI/CD Using Continuous Machine Learning.

Assessment

• Final Exam: MLOps.

English Summary

The Machine Learning with Python – ML Programmer to ML Architect E-Learning offers over 85 hours of comprehensive training to help you become an expert in Machine Learning (ML) and Deep Learning (DL). Learn to design scalable AI solutions that automate processes, generate insights, and make accurate predictions. With tracks such as ML Programmer, ML Architect, and MLOps, the Machine Learning with Python – ML Programmer to ML Architect E-Learning equips you with the skills to excel in data science and artificial intelligence (AI).