Data Science And Machine Learning Developer Certification (2025)
Published 6/2025
Created by Starweaver Instructor Team
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 72 Lectures ( 9h 51m ) | Size: 3.79 GB
Data Science | Machine Learning | Deep Learning | Keras | TensorFlow | Scikit
What you'll learn
Evaluate deep learning models using TensorFlow and Keras across various applications.
Identify and prepare raw data for analysis, modeling, and deployment in scalable ML workflows.
Apply supervised and unsupervised learning techniques to solve real-world prediction and classification tasks.
Develop end-to-end machine learning models using Python and open-source ML libraries.
Requirements
Exposure to coding (Python is helpful but not an absolute must).
Exposure to basic math (linear algebra is a plus but not required).
Description
Course Description:Are You Ready to Build Machine Learning Models That Work in the Real World?You've probably heard of machine learning, seen flashy headlines about AI beating humans at games or diagnosing diseases, and maybe even tried a few Python tutorials. But when it comes to actually building and deploying ML models that solve real business problems - it's easy to get stuck. That's where this course comes in.This is more than a course. It's a complete, hands-on journey through the machine learning lifecycle - built for developers, analysts, and professionals who want to move from understanding theory to applying it with confidence.Whether you're looking to transition into a machine learning role, collaborate more effectively with data scientists, or lead a data-driven team, this course equips you with the tools, intuition, and experience to make an impact.Course OverviewThis course takes a practical approach to learning machine learning and deep learning. Rather than diving straight into math-heavy formulas or overly simplified toy problems, we focus on what you actually need to know to build intelligent systems - and how to do it using modern, open-source tools.Starting with the fundamentals of machine learning, you'll explore how models learn, what makes them perform well (or poorly), and how to train and evaluate them using real-world data. You'll work with classification algorithms like support vector machines and naive Bayes, and explore practical use cases such as admissions, forecasting, and outlier detection.As you advance, you'll build deep learning models using TensorFlow and Keras - experimenting with architectures, layers, activation functions, and learning rates. You'll get hands-on experience with convolutional operations for image recognition, as well as transfer learning using pretrained models to boost performance on smaller datasets.You'll also explore how to scale your models using pipelines and distributed systems, preparing you for real-world deployment challenges.What You Will LearnBy the end of this course, you will be able to
evelop end-to-end machine learning models using Python and open-source libraries like Scikit-learn, TensorFlow, and Keras.Apply supervised and unsupervised learning techniques to real-world datasets.Evaluate and fine-tune deep learning architectures including CNNs and pretrained models.Identify and prepare raw data for modeling, from feature engineering to training workflows.Scale your machine learning pipelines using cloud-based and distributed systems.What Makes This Course DifferentUnlike many theoretical or overly simplified machine learning courses, this one is designed around real-world use cases, industry-standard tools, and a strong emphasis on intuitive understanding. Every topic is built to be immediately applicable-not just academic. By the end of this course, you'll have written working code, developed practical workflows, and built a toolkit of reusable techniques.Here's how this course stands apart:Hands-on from the start: You won't just watch lectures-you actively write code, run experiments, and troubleshoot models.Focused on practical fundamentals: You learn topics like support vector machines, neural networks, and transfer learning through real-world examples, not abstract formulas.Built for modern ML roles: Whether you're aiming to become a machine learning engineer, data scientist, or technical lead, this course prepares you with job-relevant skills.Uses professional-grade tools: You'll work with TensorFlow, Keras, Scikit-learn, and other frameworks widely used in the machine learning industry.Ready to Get Started?If you're looking for a course that goes beyond theory, teaches you how machine learning really works, and gets you building useful models right away - this is the course for you.Join now and start your journey toward becoming a skilled, job-ready machine learning practitioner. The future of AI isn't just for PhDs - it's for builders. Let's get started.Who this course is for
Aspiring Data Scientists & ML Engineers: Developers and recent graduates looking to transition into machine learning roles or launch a career in data science.
Technical Professionals Enhancing ML Knowledge: Software engineers, information architects, and developers who want to deepen their understanding of ML/DL to better collaborate with data teams.
Analytics & BI Professionals: Business analysts and analytics managers seeking to apply data science techniques and lead ML-driven projects more effectively.
AI & ML Practitioners Upskilling: Working professionals in AI/ML aiming to formalize their skills, build scalable models, and stay current with industry tools.
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