Become a Machine Learning Engineer

SKILL PATH

Become a Machine Learning Engineer

Start your journey to becoming a machine learning engineer by mastering the fundamentals of coding with Python. Learn machine learning techniques, data manipulation, and visualization. As you progress, you'll explore object-oriented programming and the machine learning process, gaining hands-on experience with machine learning algorithms and tools like scikit-learn. Tackle practical projects, including predicting auto insurance payments and customer segmentation using K-means clustering. Finally, explore the deep learning models with convolutional neural networks and apply your skills to an AI-powered image colorization project.

105 hours

133 Lessons

Learning Objectives

A firm grasp of basic coding concepts and computational problem-solving in Python, setting a solid foundation for machine learning for beginners.

An understanding of the fundamentals of machine learning and building linear regression and classification models using various machine learning methods and algorithms.

The ability to use data preprocessing and modeling techniques with machine learning tools like scikit-learn, focusing on feature extraction, scaling, and decision trees.

Exploration of building and fine-tuning neural networks, and applying deep learning models to classify nonlinear data using support vector machines through hands-on exercises.

A comprehensive understanding of deep learning with convolutional neural networks (CNNs) and applying these skills to AI-powered projects, such as image colorization.

Path Content

Module 1

Learn Python

This module demystifies the foundations of Python, providing a comprehensive Python tutorial for beginners and guiding absolute novices to learn Python programming from scratch.

Module 2

Python Libraries

Learn to effectively process and visualize data using Python data structures, loops, and libraries to master data science techniques in Python.

Module 3

Rock Paper Scissors Game

Create a Rock Paper Scissors game in Python to enhance your programming skills and showcase a practical Python project in your portfolio.

Module 4

Object Oriented Programming in Python - I

Explore OOP in Python, including concepts such as Python classes and objects, and the use of encapsulation.

Module 5

Machine Learning for Beginners

Learn machine Learning for beginners with key topics like the machine learning process, scikit-learn, artificial neural networks, and gradient descent to become a skilled machine learning engineer.

Module 6

Preprocessing Data

Master data preprocessing and modeling techniques with scikit-learn. Build machine learning models like linear regression and decision trees, focusing on data training, testing, and evaluation.

Module 7

Machine Learning Project Using scikit-learn

Learn to build and optimize neural networks and support vector machines (SVMs) with scikit-learn. Tackle machine learning challenges through practical exercises and solution reviews.

Module 8

Machine Learning Challenges

Fine-tune neural networks and classify non-linear data using support vector machines (SVMs) with scikit-learn. Tackle machine learning challenges by optimizing various machine learning models.

Module 9

Unsupervised Learning

Explore clustering with scikit-learn, focusing on unsupervised learning techniques like K-Means, DBSCAN, cosine similarity, nearest neighbors, and cluster evaluation.

Module 10

Customer Segmentation with K-Means Clustering

Implement customer segmentation with K-Means clustering to refine marketing efforts and enhance experiences through targeted strategies using the K-Means algorithm in unsupervised learning.

Module 11

Deep Learning Models

Explore deep learning models with convolutional neural networks (CNNs) and apply your skills in an AI-powered image colorization project, featuring face extraction and colorization exercises.

Module 12

Capstone Project: Build a Road Sign Recognition System with CNN

Develop a deep learning model using a convolutional neural network (CNN) to recognize road signs, enhancing safety in autonomous vehicles and improving traffic management systems.

What Our Learners Say

Your method is simple, straight to the point and I can practice with it everywhere, even from my phone, that's something I have never had in other learning platforms.

Felipe Matheus

I highly recommend Educative. The courses are well organized and easy to understand.

Adina Ong

I prefer Educative courses because they have a nice mix of text & images. I find that with full video courses, it can often be too easy to go into passive learning mode.

Clifford Fajardo

I prefer Educative courses because they have a nice mix of text & images. I find that with full video courses, it can often be too easy to go into passive learning mode.

Clifford Fajardo

What Our Learners Say

Your method is simple, straight to the point and I can practice with it everywhere, even from my phone, that's something I have never had in other learning platforms.

Felipe Matheus

I highly recommend Educative. The courses are well organized and easy to understand.

Adina Ong

I prefer Educative courses because they have a nice mix of text & images. I find that with full video courses, it can often be too easy to go into passive learning mode.

Clifford Fajardo

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