Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!
What you will learn from this Course:
- Master critical data science skills.
- Understand Machine Learning from top to bottom.
- Replicate real-world situations and data reports.
- Learn NumPy for numerical processing with Python.
- Conduct feature engineering on real world case studies.
- Learn Pandas for data manipulation with Python.
- Create supervised machine learning algorithms to predict classes.
- Learn Matplotlib to create fully customized data visualizations with Python.
- Create regression machine learning algorithms for predicting continuous values.
- Learn Seaborn to create beautiful statistical plots with Python.
- Construct a modern portfolio of data science and machine learning resume projects.
- Learn how to use Scikit-learn to apply powerful machine learning algorithms.
- Get set-up quickly with the Anaconda data science stack environment.
- Learn best practices for real-world data sets.
- Understand the full product workflow for the machine learning lifecycle.
- Explore how to deploy your machine learning models as interactive APIs.
Requirements for this Course:
- Basic Python Knowledge (capable of functions)
What is in the course?
Welcome to the most over the top total seminar on learning Data Science and Machine Learning on the web! In the wake of instructing more than 2 million understudies, I’ve worked for longer than a year to assemble what I accept to be the most ideal approach from zero to legend for information science and AI in Python!
This course is intended for the understudy who definitely knows some Python and is prepared to plunge further into utilizing those Python abilities for Data Science and Machine Learning. The run-of-the-mill-beginning compensation for information researchers can be more than $150,000 dollars, and we’ve made this course to help guide understudies to acquiring a bunch of abilities to make them incredibly hirable in the present working environment climate.
We’ll cover all you require to know for the full information science and AI tech stack needed at the world’s top organizations. Our understudies have landed positions at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech organizations! We’ve organized the course utilizing our experience training both on the web and face to face to convey a reasonable and organized methodology that will direct you through understanding not exactly how to utilize information science and AI libraries, yet why we use them. This course is adjusted between viable true contextual investigations and the numerical hypothesis behind the AI calculations.
We cover progressed AI calculations that most different courses don’t! Counting progressed regularization techniques and cutting edge solo learning strategies, like DBSCAN.
This exhaustive course is intended to be comparable to Bootcamps that normally cost a huge number of dollars and incorporates the accompanying subjects:
- Programming with Python
- NumPy with Python
- Profound jump into Pandas for Data Analysis
- Full comprehension of Matplotlib Programming Library
- Profound plunge into seaborn for information perceptions
- AI with SciKit Learn, including:
- Straight Regression
- Tether Regression
- Edge Regression
- Flexible Net
- K Nearest Neighbors
- K Means Clustering
- Choice Trees
- Irregular Forests
- Normal Language Processing
- Backing Vector Machines
- Hierarchal Clustering
- Model Deployment
- what’s more, a whole lot more!
As usual, we’re appreciative for the opportunity to show you information science, AI, and python and expectation you will go along with us inside the course to help your range of abilities!
Jose and Pierian Data Inc. Group
Who this course is for:
- Beginner Python developers curious about Machine Learning and Data Science with Python
- Introduction to Course
- Optional: Python Crash Course
- Machine Learning Pathway Overview
- Seaborn Data Visualizations
- Data Analysis and Visualization Capstone Project Exercise
- Machine Learning Concepts Overview
- Linear Regressions
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