Complete A.I. & Machine Learning, Data Science Bootcamp

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

4.63 (26724 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Become a Data Scientist and get hired

Master Machine Learning and use it on the job

Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0

Use modern tools that big tech companies like Google, Apple, Amazon and Meta use

Present Data Science projects to management and stakeholders

Learn which Machine Learning model to choose for each type of problem

Real life case studies and projects to understand how things are done in the real world

Learn best practices when it comes to Data Science Workflow

Implement Machine Learning algorithms

Learn how to program in Python using the latest Python 3

How to improve your Machine Learning Models

Learn to pre process data, clean data, and analyze large data.

Build a portfolio of work to have on your resume

Developer Environment setup for Data Science and Machine Learning

Supervised and Unsupervised Learning

Machine Learning on Time Series data

Explore large datasets using data visualization tools like Matplotlib and Seaborn

Explore large datasets and wrangle data using Pandas

Learn NumPy and how it is used in Machine Learning

A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided

Learn to use the popular library Scikit-learn in your projects

Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry

Learn to perform Classification and Regression modelling

Learn how to apply Transfer Learning

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Our Verdict

This Data Science Bootcamp is a solid and engaging introduction to foundational AI & Machine Learning techniques, best suited for beginners looking for an approachable yet content-dense course. Students should expect some trial-and-error due to occasional gaps in provided explanations or outdated course materials, but will come away with an understanding of real-world application and useful industry tools - making it a worthwhile investment for aspiring Data Scientists seeking hands-on experience in Python and Tensorflow.

What We Liked

Comprehensive coverage of data science and machine learning concepts
Real-life case studies and projects provide practical context
Instructors convey complex information in a friendly, easy-to-understand style
Active and engaged student community for additional support

Potential Drawbacks

Limited variety in the types of machine learning models explored in depth
Some outdated code examples that require independent research to correct
Occasional slow pace due to unnecessary talkiness or lack of adequate explanation
Minimal focus on mathematical principles underlying foundational concepts