Machine Learning Engineering

Course Description

Dive into the essentials of machine learning with our focused course, starting with data manipulation using NumPy and progressing to sophisticated data analysis with pandas. Learn the nuances of data preprocessing and advanced modeling techniques using scikit-learn, then explore the dynamics of clustering and master gradient boosting with XGBoost.

Key Areas Covered:

Data Manipulation: Master array-based operations and data structures with NumPy.
Data Analysis: Uncover patterns and insights using pandas for comprehensive data analysis.
Data Preprocessing: Enhance model accuracy with preprocessing techniques in scikit-learn.
Data Modeling: Build predictive models using various algorithms within scikit-learn.
Clustering: Segment data intuitively to discover inherent groupings and patterns.
Gradient Boosting: Leverage XGBoost for powerful boosting techniques that improve model performance.
Deep Learning: Develop deep neural networks with TensorFlow and Keras for advanced learning tasks.

The journey continues with an in-depth look at neural network implementations through TensorFlow and Keras, culminating in the exploration of Large Language Models (LLMs). Each section is designed to build practical skills and theoretical knowledge, preparing you to tackle real-world data science challenges efficiently. This curriculum not only broadens your understanding but also sharpens your ability to apply these technologies effectively in varied scenarios.

‍

Results after course completion

Each module in this course is designed to build both practical skills and theoretical knowledge through engaging lessons and hands-on activities. Here's what you can expect:

Module 1: Data Manipulation with NumPy

Learn to handle and manipulate numerical data efficiently using NumPy's powerful array operations.

Understand the array object and matrix operations.
Explore slicing, indexing, and iterating through arrays.
Learn about broadcasting rules and functions for array manipulation.

Module 2: Data Analysis with pandas

Dive into data analysis techniques using pandas to handle, process, and analyze data with ease.

Master data structures like DataFrames and Series.
Perform data merging, reshaping, and pivoting.
Explore data summarization and aggregation techniques.

Module 3: Data Preprocessing with scikit-learn

Prepare your data for optimal modeling results using scikit-learn’s preprocessing tools.

Implement scaling, normalization, and data encoding.
Handle missing data to improve dataset integrity.
Learn to split data into training and test sets effectively.

Module 4: Data Modeling with scikit-learn

Build and refine machine learning models using scikit-learn’s robust toolkit.

Explore regression, classification, and clustering algorithms.
Evaluate model performance with cross-validation.
Optimize models using hyperparameter tuning.

Module 5: Clustering with scikit-learn

Uncover hidden patterns in data with clustering techniques, an essential skill in unsupervised learning.

Learn to implement K-means and hierarchical clustering.
Understand the concepts of cluster evaluation.
Explore advanced clustering techniques like DBSCAN.

Module 6: Gradient Boosting with XGBoost

Boost your model's performance with XGBoost, a leading machine learning library for regression, classification, and ranking algorithms.

Understand the fundamentals of boosting and its advantages.
Learn to configure and optimize XGBoost models.
Apply XGBoost models to real-world data challenges.

Module 7: Deep Learning with TensorFlow

Harness the power of TensorFlow to build, train, and deploy deep learning models that can scale to massive datasets.

Build foundational knowledge of neural networks.
Explore convolutional and recurrent neural networks.
Implement models for image and sequence data processing.

Module 8: Deep Learning with Keras

Simplify deep learning model implementations with Keras, a high-level neural networks API running on top of TensorFlow.

Design and train deep learning models using a simple, flexible interface.
Use built-in layers and callbacks for complex architectures.
Learn model fine-tuning and transfer learning techniques.

Module 9: Large Language Models (LLMs)

Dive into the world of Large Language Models and their applications, from natural language processing to generating human-like text.

Explore the architecture and training processes of LLMs.
Understand the applications and implications of LLMs in various industries.
Implement practical projects using pre-trained models like GPT and BERT.

‍

Summary Notes and Cheat Sheets

This course is designed not only to teach you the fundamental and advanced concepts of machine learning and data science but also to prepare you for challenging interviews in the field. To enhance your readiness, we include five strategically placed cheat sheets that summarize critical topics, providing quick references and reinforcing learning outcomes. These cheat sheets serve as valuable resources during interview preparations by condensing complex topics into digestible, crucial points.

Cheat Sheet 1: Python for Data Science

Python Basics: Quick reminders on syntax, data types, functions, and control flow.
NumPy Essentials: Key functions and operations for handling arrays and matrix calculations.
pandas Operations: Summary of data manipulation techniques including sorting, filtering, and querying.

Cheat Sheet 2: Data Preprocessing and Modeling

Preprocessing Techniques: Overview of scaling, encoding, and handling missing data.
Model Building: Steps for creating regression and classification models using scikit-learn.
Model Evaluation: Metrics and methods to assess model performance.

Cheat Sheet 3: Advanced Machine Learning Techniques

Clustering Methods: Summaries of K-means, hierarchical, and DBSCAN clustering.
Ensemble Methods: Key concepts of boosting, bagging, and stacking.
XGBoost Parameters: Important parameters and tuning tips for optimizing XGBoost.

Cheat Sheet 4: Deep Learning Fundamentals

Neural Network Basics: Core concepts including layers, activation functions, and backpropagation.
TensorFlow and Keras: Summary of APIs for model building, training, and evaluation.
Common Architectures: Overview of CNNs and RNNs, and their applications.

Cheat Sheet 5: Large Language Models and Real-World Applications

LLM Overview: Key characteristics and types of large language models.
Practical Uses: Applications of LLMs in business, healthcare, and more.
Interview Questions: Sample questions and answers that can be expected concerning LLMs.

‍

These cheat sheets are carefully crafted to ensure they cover the essential knowledge required to handle technical questions effectively. They also provide practical tips and techniques to apply during your interviews, making them an indispensable part of your interview preparation toolkit. This structured approach ensures you are not only knowledgeable about machine learning technologies but also well-prepared to discuss and demonstrate your understanding in a professional setting.

For those interested in additional resources, each cheat sheet is available for purchase separately. Please find more details and purchase options below.

‍

What prerequisites do I need for this course?

This course is designed for learners with a basic understanding of Python programming. Familiarity with basic statistical concepts and linear algebra will be helpful but not strictly necessary, as the course includes introductory modules to cover essential groundwork.

How long will it take to complete the course?

The duration to complete the course can vary based on your prior knowledge and commitment. Typically, learners take about 8-10 weeks when dedicating 5-7 hours per week to complete all modules, engage with the practical assignments, and review the cheat sheets.

Can I access the course materials offline?

Yes, enrolled students will have access to downloadable content including PDFs of the cheat sheets, video lectures, and other resources, which makes it convenient to study even when you're offline.

Are there any additional costs for the cheat sheets if I'm already enrolled in the course?

All enrolled students receive the cheat sheets as part of the course material at no additional cost. However, if you wish to purchase additional copies or acquire them separately without enrolling in the full course, you can do so. Details and pricing for individual cheat sheet purchases are available below.

‍