Complete Data Science BootCamp
Learn about Data Science, Machine Learning and Deep Learning and build 5 different projects.
Bullstrack
Complete Data Science BootCamp udemy course
Learn about Data Science, Machine Learning and Deep Learning and build 5 different projects.
What you'll 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 Facebook 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
Requirements:
- No prior experience is needed (not even Math and Statistics). We start from the very basics.
- A computer (Linux/Windows/Mac) with internet connection.
- Two paths for those that know programming and those that don’t.
- All tools used in this course are free for you to use.
Description:
Data science is the field that encompasses the various techniques and methods used to extract insights and knowledge from data. Machine learning (ML) and deep learning (DL) are both subsets of data science, and they are often used together to analyze and understand data.
In data science, ML algorithms are often used to build predictive models that can make predictions based on historical data. These models can be used for tasks such as classification, regression, and clustering. ML algorithms include linear regression, decision trees, and k-means.
DL, on the other hand, is a subset of ML that is based on artificial neural networks with multiple layers, which allows the system to learn and improve through experience. DL is particularly well-suited for tasks such as image recognition, speech recognition, and natural language processing. DL algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
In a data science project, DL models are often used in combination with other techniques such as feature engineering, data cleaning, and visualization, to extract insights and knowledge from data. For instance, DL models can be used to automatically extract features from images, and then these features can be used in a traditional ML model.
In summary, Data science is the field that encompasses various techniques and methods to extract insights and knowledge from data, ML and DL are subsets of data science that are used to analyze and understand data, ML is used to build predictive models and DL is used to model complex patterns and relationships in data. Both ML and DL are often used together in data science projects to extract insights and knowledge from data.
IN THIS COURSE YOU WILL LEARN ABOUT :
Life Cycle of a Data Science Project.
Python libraries like Pandas and Numpy used extensively in Data Science.
Matplotlib and Seaborn for Data Visualization.
Data Preprocessing steps like Feature Encoding, Feature Scaling etc...
Machine Learning Fundamentals and different algorithms
Cloud Computing for Machine Learning
Deep Learning
5 projects like Diabetes Prediction, Stock Price Prediction etc...
ALL THE BEST !!!
Who this course is for:
- People who want to start their Data Science Journey in Python.
- Someone who is looking for a complete course that covers all important topics of Data Science, Machine Learning and Deep Learning.
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Course Details:
- 7.5 hours on-demand video
- 61 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
Complete Data Science BootCamp udemy free download
Learn about Data Science, Machine Learning and Deep Learning and build 5 different projects.
Demo Link: https://www.udemy.com/course/complete-data-science-bootcamp/
