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Data Science and Machine Learning with Python
Navid Shirzadi

Master NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Scipy

Summary

Price
£399 inc VAT
Or £66.50/mo. for 6 months...
Study method
Online, On Demand
Duration
14.7 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed courses certificate of completion - Free
Additional info
  • Tutor is available to students

Overview

Are you interested in data science and machine learning, but you don't have any background, and you find the concepts confusing?

Are you interested in programming in Python, but you always afraid of coding?

I think this course is for you!

Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.

Certificates

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

Curriculum

8
sections
81
lectures
14h 41m
total

    • 10: NumPy 1 Preview 07:25
    • 11: NumPy 2 09:11
    • 12: NumPy 3 12:22
    • 13: NumPy 4 06:32
    • 14: NumPy 5 18:07
    • 15: NumPy 6 16:44
    • 16: Pandas1 15:40
    • 17: Pandas2 15:11
    • 18: Pandas3 14:44
    • 19: Pandas4 24:19
    • 20: Visualization with Matplotlib1 14:57
    • 21: Visualization with Matplotlib2 22:13
    • 22: Visualization with Matplotlib3 18:53
    • 23: Visualization with Matplotlib4 16:03
    • 24: Visualization with Matplotlib5 12:56
    • 25: Chapter 2 Test 01:00

    • 26: Reading and Modifying a Dataset 20:02
    • 27: Statistics 1 09:02
    • 28: Statistics 2 20:06
    • 29: Statistics 3 - Covariance 13:38
    • 30: Missing Values 1 13:20
    • 31: Missing Values 2 20:52
    • 32: Outlier Detection 1 11:16
    • 33: Outlier Detection 2 15:06
    • 34: Outlier Detection 3 02:59
    • 35: Concatenation 07:17
    • 36: Dummy Variables 06:55
    • 37: Normalization 20:15
    • 38: Preprocessing Quiz! 02:00

    • 39: Types of Learnings 07:20
    • 40: Quiz Time! 01:00

    • 41: Supervised Learning Models - Introduction and Understanding the Data 29:51
    • 42: k-NN Concepts 10:07
    • 43: k-NN Model Development 14:56
    • 44: k-NN Training-Set and Test-Set Creation 24:08
    • 45: Decision Tree Concepts 06:12
    • 46: Decision Tree Model Development 06:56
    • 47: Decision Tree - Cross Validation 08:34
    • 48: Naive Bayes Concepts 14:01
    • 49: Naive Bayes Model Development 06:23
    • 50: Logistic Regression Concepts 03:05
    • 51: Logistic Regression Model Development 11:12
    • 52: Model Evaluation Concepts 17:27
    • 53: Model Evaluation - Calculating with Python 18:44
    • 54: Quiz Time! 03:00

    • 55: Simple and Multiple Linear Regression Concepts 28:47
    • 56: Multiple Linear Regression - Model Development 08:01
    • 57: Evaluation Metrics - Concepts 11:26
    • 58: Evaluation Metrics - Implementation 16:52
    • 59: Polynomial Linear Regression Concepts 05:57
    • 60: Polynomial Linear Regression Model Development 21:57
    • 61: Random Forest Concepts 06:35
    • 62: Random Forest Model Development 22:33
    • 63: Support Vector Regression Concepts 07:07
    • 64: Support Vector Regression Model Development 10:36
    • 65: Quiz Time! 02:00

    • 66: Introduction to Unsupervised Learning 07:22
    • 67: K-Means Concepts 1 09:37
    • 68: K-Means Concepts 2 05:55
    • 69: K-means Model Development 1 04:37
    • 70: K-means Model Development 2 12:30
    • 71: K-means - Model Evaluation 10:59
    • 72: DBSCAN Concepts 05:31
    • 73: DBSCAN Model Development 09:48
    • 74: Hierarchical Clustering Concepts 05:27
    • 75: Hierarchical Clustering Model Development 15:59
    • 76: Quiz Time! 01:00

    • 77: Introduction 04:21
    • 78: Support Vector Regression - Model Tuning 12:56
    • 79: K-Means - Model Tuning 02:16
    • 80: k-NN - Model Tuning 13:22
    • 81: Overfitting and Underfitting 09:58

Course media

Description

This course is completely categorized, and we don't start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:

Chapter1: Introduction and all required installations

Chapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)

Chapter3: Preprocessing

Chapter4: Machine Learning Types

Chapter5: Supervised Learning: Classification

Chapter6: Supervised Learning: Regression

Chapter7: Unsupervised Learning: Clustering

Chapter8: Model Tuning

Furthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.

Remember! That this course is created for you with any background as all the concepts will be explained from the basics! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python.

Who is this course for?

  • Data Science Enthusiast
  • Beginner Programmers
  • Python Developers
  • Researchers who like to forecast into future
  • Data Analysts
  • Anyone who is interested in Time Series and Future Forecasting

Requirements

Python Basic Syntax

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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