Data Science & Machine Learning with Python

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Course Summary

Learn the foundations of data science and machine learning using Python. Learn how to think like a data scientist, and dive into ever-more advanced analysis and predictive modeling using data and code. Understand what it means to learn from data using ML tools and algorithms. Use real tools and build your experience with Jupyter Lab, Numpy, Matplotlib, Seaborn, Pandas, Scikit Learn, and more.

21 Sections · 204 Pages · By Gilad Gressel

Level

Intermediate-to-Advanced: Completion of Python 301 or equivalent knowledge required.
Duration

225 Hours. Or, ~3 months of dedicated learning @ 15-20 hours per week
What's included

79 lessons. 77 videos. 15 Jupyter Notebooks. 19 Practical Labs. 25 quizzes. 1 Capstone Project. And much more!
Certificate

225-Hour Data Science & Machine Learning Certificate

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This course is part of the following career track:

Data Science + Machine Learning + AI Career Track

Join the AI revolution by becoming a machine learning and data science engineer. Learn to build end-to-end machine learning models that solve decision making problems in areas of your interest. This career track starts with Python, and takes you through building a portfolio of projects using in-demand data science, machine learning and deep learning techniques.

1) Introduction
1. Video: Considering a Career in Data Science and Machine Learning?
  
  5 min
2. Video: About Data Science & Machine Learning Course
  
  10 min
2) Let's Get Started
3) The Essence of Machine Learning
4) Machine Setup
5) Learning Numpy, Pandas, and Matplotlib
6) Math Review
6.5) Exploratory Data Analysis
7) Linear Regression I
1. Video: What is Linear Regression
  
  17 min
2. Quiz: Linear Things
3. Assignment
4. Lab: What is Linear Regression
  
  3 min
5. Solutions and More
6. Video Lab Solution: What is Linear Regression
  
  13 min
7. The Bias Variance Tradeoff
  
  4 min
8. Feedback
9. Feedback: Linear Regression I
8) Linear Regression II
9) Model Evaluation I
10) Model Evaluation II
11) Classification with Decision Trees
12) Model Validation
13) Ensemble Methods
14) Dealing with Data
15) Unsupervised Learning Intro
16) Clustering
17) Dimensionality Reduction
18) Putting It All Together - Capstone Project
19) Next Steps
20) Data Science Certificate Request
1. Request Completion Certificate

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Data Science & Machine Learning with Python

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Course Summary

1) Introduction

2) Let's Get Started

3) The Essence of Machine Learning

4) Machine Setup

5) Learning Numpy, Pandas, and Matplotlib

6) Math Review

6.5) Exploratory Data Analysis

7) Linear Regression I

8) Linear Regression II

9) Model Evaluation I

10) Model Evaluation II

11) Classification with Decision Trees

12) Model Validation

13) Ensemble Methods

14) Dealing with Data

15) Unsupervised Learning Intro

16) Clustering

17) Dimensionality Reduction

18) Putting It All Together - Capstone Project

19) Next Steps

20) Data Science Certificate Request

Data Science & Machine Learning with Python Copy Link LinkedIn Message Facebook Email X

Course Summary

1) Introduction

2) Let's Get Started

3) The Essence of Machine Learning

4) Machine Setup

5) Learning Numpy, Pandas, and Matplotlib

6) Math Review

6.5) Exploratory Data Analysis

7) Linear Regression I

8) Linear Regression II

9) Model Evaluation I

10) Model Evaluation II

11) Classification with Decision Trees

12) Model Validation

13) Ensemble Methods

14) Dealing with Data

15) Unsupervised Learning Intro

16) Clustering

17) Dimensionality Reduction

18) Putting It All Together - Capstone Project

19) Next Steps

20) Data Science Certificate Request

Data Science & Machine Learning with Python

Copy Link LinkedIn Message Facebook Email X