Lvl.3 -> Machine Learning Expert
Pre-requisite: Lvl.2 Machine Learning Engineer
Approx. Time Required: 3 months
✅ NEW! Join our Mentorship Program for this Career Path here. 👈
Embark on an immersive journey into advanced data science and machine learning techniques. It begins with “Data Manipulation in Python: A Pandas Crash Course,” where you will master the art of data manipulation, analysis, and visualization using Pandas. From data loading and transformation to advanced techniques like MultiIndex and time series analysis, this course equips you with essential skills for handling and preparing complex datasets. The journey continues with “Machine Learning Level 3,” which dives into ensemble models and automated pipelines using cloud computing tools such as Amazon SageMaker and AWS Lambda, preparing you for working with production-ready machine learning systems. The career path then transitions to deep learning with “PyTorch: From Zero to Hero,” where you will gain foundational knowledge of PyTorch, including neural networks and advanced topics such as custom layers and optimization, empowering them to build and deploy complex models. “Deep Learning A-Z” expands on this by covering a wide range of deep learning models, from artificial neural networks to Boltzmann Machines, equipping you with the skills to build and implement deep learning systems for tasks like image recognition, time series analysis, and feature detection. Finally, the “MLOps: From Zero to Hero” course completes the path, guiding you through the end-to-end lifecycle of machine learning projects, from data preparation to scalable model deployment, ensuring efficient and automated workflows for real-world machine learning applications.
1. Data Manipulation in Python: A Pandas Crash Course
Embark on a comprehensive journey with Pandas to master data manipulation, analysis, and visualization in Python. This course covers everything from the basics of dataset handling to advanced data analysis techniques, designed for those aiming to deepen their data science skills.
- Navigate the essentials of Pandas for effective data analysis, including data loading, inspection, and serialization.
- Unlock the power of visual data exploration to understand dataset dynamics and unveil underlying patterns.
- Master the art of data manipulation, learning to slice, filter, replace, and modify datasets for analytical readiness.
- Delve into grouping and merging techniques for sophisticated data aggregation and integration strategies.
- Explore advanced data manipulation with MultiIndex, pivoting, and more, for complex data transformation tasks.
- Gain proficiency in handling time series data, leveraging Pandas for time-based analysis and forecasting.
This course equips you with the tools and techniques to perform comprehensive data analysis, from preliminary data preparation to advanced analytical processes. Upon completion, you’ll possess a robust skill set in Pandas, ready to tackle real-world data challenges and contribute valuable insights in a variety of data-driven contexts.
Dive into the world of Ensemble Models and Machine Learning Pipelines built with Cloud Computing through this hands-on and project-focused course. This course is your gateway to developing Ensemble Models and Machine Learning Pipelines by leveraging Cloud Computing with powerful tools like Amazon SageMaker and AWS Lambda for Regression and Classification problems.
- Learn the intuition behind Ensemble Models and Machine Learning Pipelines.
- Master Ensemble Models with SageMaker Canvas and SageMaker Studio Classic.
- Delve into Machine Learning Pipelines, discovering the power of AWS Lambda, designed to build automated machine learning pipelines.
Equipped with practical experience and an in-depth understanding of probably the most powerful machine learning frameworks, you’ll be ready to tackle complex machine learning challenges, optimize model performance, and significantly improve prediction accuracy in your projects. This course prepares you for advanced machine learning roles by providing you with the skills to make informed decisions on which ensemble technique to apply for optimal results.
This course provides a comprehensive introduction to deep learning using PyTorch. Starting from the basics, you’ll progressively build your skills, exploring neural network architectures, advanced PyTorch topics, and applying your knowledge to a final project. By the end of the course, you’ll be equipped to create complex machine learning models and implement real-world solutions.
- Learn the fundamentals of PyTorch, including tensor operations, automatic differentiation, and data handling.
- Explore how to build and train various neural network models using PyTorch’s powerful modules and layers.
- Dive into more complex topics such as custom layers, optimization techniques, and working with GPUs.
By the end of this course, you’ll have the skills to develop and deploy state-of-the-art models in PyTorch.
Dive into the transformative power of Deep Learning with this extensive course, designed to equip you with the knowledge and skills to build, analyze, and implement Artificial Neural Networks and various Deep Learning models. This course is perfect for those aiming to master Deep Learning from the ground up, covering essential topics from Artificial Neural Networks to advanced models like AutoEncoders and Boltzmann Machines.
- Understand the foundational concepts of Deep Learning and Artificial Neural Networks (ANNs), learning through hands-on coding and real-world problem solving.
- Build and train convolutional neural networks (CNNs) for image recognition, employing state-of-the-art techniques to achieve high accuracy.
- Explore recurrent neural networks (RNNs) for time series analysis, stock price prediction, and other sequential data challenges.
- Uncover the principles behind Self-Organizing Maps (SOMs) for feature detection and unsupervised learning, and apply them to detect fraud.
- Delve into the intriguing world of Boltzmann Machines (BMs) and AutoEncoders (AEs) for recommendation systems and feature learning.
By the end of this course, you’ll have a comprehensive understanding of deep learning’s core principles and applications. You’ll be proficient in building and deploying various deep learning models, ready to tackle complex challenges and contribute to the field of artificial intelligence.
This comprehensive course is designed to provide a deep dive into the world of MLOps (Machine Learning Operations), equipping you with the skills and knowledge to manage the end-to-end lifecycle of machine learning projects. Whether you’re a data scientist, engineer, or developer, this course will guide you through the key stages of MLOps, from data preparation to model deployment, with a focus on scalability and efficiency.
- Get introduced to the fundamentals of MLOps, exploring how it bridges the gap between machine learning development and production, ensuring smooth workflows, collaboration, and reliability.
- Learn the essential steps to building and automating data pipelines for machine learning. This module covers data cleaning, transformation, and optimization, ensuring your data is ready for model training.
- Gain practical experience with the entire model development lifecycle. You’ll train, evaluate, and fine-tune models, and learn how to deploy them into production environments with best practices for monitoring and iteration.
- A collection of industry-standard tools, techniques, and workflows to master MLOps. Learn about the latest advancements in automation, monitoring, and model maintenance, enabling you to scale machine learning applications efficiently.
This course is ideal for anyone looking to streamline and automate machine learning workflows, ensuring smooth transitions from model development to production.
Plus, check out this Live Lab Recording:
Live Lab Recording #14: Building a RAG system using Hugging Face and open-source LLMs
👉 Remember to join the Mentorship Program [here] to grow even faster!