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Machine Learning with NumPy, pandas, scikit-learn, and More

Name: Machine Learning with NumPy, pandas, scikit-learn, and More
Price: 59 USD
Rating: 4.71 (126 reviews)
Author: Adaptilab

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.

4.7

88 lessons

1 AI mock interview

15h

Created by Adaptilab

Machine Learning with NumPy, pandas, scikit-learn, and More

What's included

88 lessons across 9 modules
13 quizzes with instant feedback
1 AI mock interview with scoring rubrics
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

Apply the seven-step workflow to process data, select models, and validate ML results
Manipulate multidimensional data with NumPy using indexing, filtering, math, and aggregation
Load, clean, combine, and summarize datasets in pandas using groupby, sorting, and file I/O
Convert DataFrames to quantitative NumPy matrices using missing-value handling and get_dummies
Preprocess features in scikit-learn using scaling, normalization, imputation, and PCA
Train and evaluate supervised models using scikit-learn with CV, metrics, and GridSearchCV
Build and train MLP classifiers in TensorFlow and Keras using softmax, loss, and fit/evaluate

Learning Roadmap

88 Lessons13 Quizzes115 Challenges

What you'll learn from this course

Get familiar with machine learning processes, types, and model building essentials.

Overview

Data Manipulation with NumPy

Grasp the fundamentals of data manipulation with NumPy arrays, arithmetic operations, and statistical analysis.

Data Analysis with pandas

15 Lessons

Master the steps to utilize pandas for MLB data analysis, including processing, manipulation, and visualization.

Data Preprocessing with scikit-learn

9 Lessons

Grasp the fundamentals of scikit-learn's data preprocessing techniques for scaling, normalizing, imputing, and dimensional reduction.

Data Modeling with scikit-learn

13 Lessons

Dig into scikit-learn's data modeling techniques, including linear regression, classification, and hyperparameter tuning.

Clustering with scikit-learn

10 Lessons

Follow the process of clustering algorithms, evaluating their performance, and feature clustering in scikit-learn.

Gradient Boosting with XGBoost

10 Lessons

Build on XGBoost for efficient, high-performance gradient-boosted decision trees in data science.

Deep Learning with TensorFlow

12 Lessons

Learn how to use TensorFlow for neural networks, from MLPs to multiclass classifications.

Deep Learning with Keras

7 Lessons

Get started with creating, configuring, and evaluating neural network models using Keras.

Machine Learning Fundamentals

Mock Interview

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

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About this course

WHY I WROTE THIS COURSE

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.

ABOUT THE AUTHOR

Adaptilab

AdaptiLab is a Seattle-based SaaS startup helping companies grow their machine learning teams from hiring to productivity.

Learn more about Adaptilab

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