4.4
Intermediate
15h
Updated 1 month ago
Machine Learning with NumPy, pandas, scikit-learn, and More
Learn practical machine learning with NumPy, pandas, scikit-learn, and more. Learn data analysis, feature engineering, and deep learning using industry-standard frameworks. Basic Python required.
If you're a software engineer looking to add machine learning to your skillset, this is the place to start.
This course will teach you to write useful code and create impactful machine learning applications immediately. From the start, you'll be given all the tools that you need to create industry-level machine learning projects. Rather than reading through dense theory, you’ll learn practical skills and gain actionable insights. Topics covered include data analysis/visualization, feature engineering, supervised learning, unsupervised learning, and deep learning. All of these topics are taught using industry-standard frameworks: NumPy, pandas, scikit-learn, XGBoost, TensorFlow, and Keras.
Basic knowledge of Python is a prerequisite to this course.
This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.
If you're a software engineer looking to add machine learning to your skillset, this is the place to start.
This course will t...Show More
Content
1.
What you'll learn from this course
1 Lessons
Get familiar with machine learning processes, types, and model building essentials.
2.
Data Manipulation with NumPy
11 Lessons
Grasp the fundamentals of data manipulation with NumPy arrays, arithmetic operations, and statistical analysis.
3.
Data Analysis with pandas
14 Lessons
Master the steps to utilize pandas for MLB data analysis, including processing, manipulation, and visualization.
4.
Data Preprocessing with scikit-learn
9 Lessons
Grasp the fundamentals of scikit-learn's data preprocessing techniques for scaling, normalizing, imputing, and dimensional reduction.
5.
Data Modeling with scikit-learn
13 Lessons
Dig into scikit-learn's data modeling techniques, including linear regression, classification, and hyperparameter tuning.
6.
Clustering with scikit-learn
10 Lessons
Follow the process of clustering algorithms, evaluating their performance, and feature clustering in scikit-learn.
7.
Gradient Boosting with XGBoost
10 Lessons
Build on XGBoost for efficient, high-performance gradient-boosted decision trees in data science.
8.
Deep Learning with TensorFlow
12 Lessons
Learn how to use TensorFlow for neural networks, from MLPs to multiclass classifications.
9.
Deep Learning with Keras
7 Lessons
Get started with creating, configuring, and evaluating neural network models using Keras.
Certificate of Completion
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