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Python: Machine Learning, Deep Learning, Pandas, Matplotlib

Python, Machine Learning, Deep Learning, Pandas, Seaborn, Matplotlib, Geoplotlib, NumPy, Data Analysis, Tensorflow

Oak Academy

Summary

Price
£39 inc VAT
Study method
Online, On Demand What's this?
Duration
22.5 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed Courses Certificate of Completion - Free
1 student purchased this course

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Overview

Hello there,
Machine learning python, python, machine learning, Django, ethical hacking, python bootcamp, data analysis, machine learning python, python for beginners, data science, machine learning, django:

Welcome to the “Python: Machine Learning, Deep Learning, Pandas, Matplotlib” course.

Python, Machine Learning, Deep Learning, Pandas, Seaborn, Matplotlib, Geoplotlib, NumPy, Data Analysis, Tensorflow


Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work.

In this course, we will learn what is Deep Learning and how does it work.

This course has suitable for everybody who is interested in Machine Learning and Deep Learning concepts in Data Science.

First of all, in this course, we will learn some fundamental stuff of Python and the Numpy library. These are our first steps in our Deep Learning journey. After then we take a little trip to Machine Learning Python history. Then we will arrive at our next stop. Machine Learning in Python Programming. Here we learn the machine learning concepts, machine learning a-z workflow, models and algorithms, and what is neural network concept. After then we arrive at our next stop. Artificial Neural network. And now our journey becomes an adventure. In this adventure we'll enter the Keras world then we exit the Tensorflow world. Then we'll try to understand the Convolutional Neural Network concept. But our journey won't be over. Then we will arrive at Recurrent Neural Network and LTSM. We'll take a look at them. After a while, we'll trip to the Transfer Learning concept. And then we arrive at our final destination. Projects in Python Bootcamp. Our play garden. Here we'll make some interesting machine learning models with the information we've learned along our journey.

In this course, we will start from the very beginning and go all the way to the end of "Deep Learning" with examples.

The Logic of Machine Learning such as Machine Learning models and algorithms, Gathering data, Data pre-processing, Training and testing the model etc.

Before we start this course, we will learn which environments we can be used for developing deep learning projects.

During the course you will learn:

  1. Fundamental stuff of Python and its library Numpy

  2. What is the Artificial Intelligence (Ai), Machine Learning, and Deep Learning

  3. History of Machine Learning

  4. Turing Machine and Turing Test

  5. The Logic of Machine Learning such as

    • Understanding the machine learning models

    • Machine Learning models and algorithms

    • Gathering data

    • Data pre-processing

    • Choosing the right algorithm and model

    • Training and testing the model

    • Evaluation

  6. Artificial Neural Network with these topics

    • What is ANN

    • Anatomy of NN

    • Tensor Operations

    • The Engine of NN

    • Keras

    • Tensorflow

  7. Convolutional Neural Network

  8. Recurrent Neural Network and LTSM

  9. Transfer Learning

  10. Reinforcement Learning

Finally, we will make four different projects to reinforce what we have learned.

Object-oriented programming (OOP) is a computer programming paradigm where a software application is developed by modeling real world objects into software modules called classes. Consider a simple point of sale system that keeps record of products purchased from whole-sale dealers and the products sold to the customer. An object-oriented language would implement these requirements by creating a Product class, a Customer class, a Dealer class and an Order class. All of these classes would interact together to deliver the required functionality where each class would be concerned with storing its own data and performing its own functions. This is the basic idea of object-oriented programming or also called OOP.

When you enroll, you will feel the OAK Academy`s seasoned developers' expertise. Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest.

Video and Audio Production Quality

All our videos are created/produced as high-quality video and audio to provide you the best learning experience.

You will be,

  • Seeing clearly

  • Hearing clearly

  • Moving through the course without distractions

You'll also get:

  • Lifetime Access to The Course

  • Fast & Friendly Support in the Q&A section

We offer full support, answering any questions.

If you are ready to learn “Python: Machine Learning, Deep Learning, Pandas, Matplotlib”

Dive in now! See you in the course!

Curriculum

12
sections
153
lectures
22h 28m
total

    • 1: Section 8 Data Visualisation - Matplotlib Files 01:00

    • 2: Introduction 04:44

    • 3: Installing Anaconda Distribution and Python 04:58
    • 4: Overview of Jupyter Notebook and Google Colab 05:32

    • 5: Data Types in Python 12:42
    • 6: Operators in Python 10:31
    • 7: Conditionals 09:49
    • 8: Loops 13:07
    • 9: Lists, Tuples, Dictionaries and Sets 17:54
    • 10: Data Type Operators and Methods 11:21
    • 11: Modules in Python 05:15
    • 12: Functions in Python 08:05
    • 13: Exercise Analyse 01:46
    • 14: Exercise Solution 10:46
    • 15: quiz 01:00

    • 16: Logic of OOP 04:58
    • 17: Constructor 06:52
    • 18: Methods 04:41
    • 19: Inheritance 06:42
    • 20: Overriding and Overloading 10:33
    • 21: quiz 01:00

    • 22: Introduction to NumPy Library 06:24
    • 23: The Power of NumPy 16:04
    • 24: Creating NumPy Array with The Array() Function 08:16
    • 25: Creating NumPy Array with Zeros() Function 05:05
    • 26: Creating NumPy Array with Ones() Function 03:06
    • 27: Creating NumPy Array with Full() Function 02:49
    • 28: Creating NumPy Array with Arange() Function 02:55
    • 29: Creating NumPy Array with Eye() Function 03:08
    • 30: Creating NumPy Array with Linspace() Function 01:31
    • 31: Creating NumPy Array with Random() Function 08:29
    • 32: Properties of NumPy Array 05:24
    • 33: Reshaping a NumPy Array: Reshape() Function 05:57
    • 34: Identifying the Largest Element of a Numpy Array: 03:45
    • 35: Detecting Least Element of Numpy Array: Min(), Ar 02:35
    • 36: Concatenating Numpy Arrays: Concatenate() Functio 09:40
    • 37: Splitting One-Dimensional Numpy Arrays: The Split 05:46
    • 38: Splitting Two-Dimensional Numpy Arrays: Split(), 09:33
    • 39: Sorting Numpy Arrays: Sort() Function 04:16
    • 40: Indexing Numpy Arrays 07:39
    • 41: Slicing One-Dimensional Numpy Arrays 06:08
    • 42: Slicing Two-Dimensional Numpy Arrays 09:30
    • 43: 22 Assigning Value to One-Dimensional Arrays 05:02
    • 44: Assigning Value to two-Dimensional Arrays 09:57
    • 45: Fancy Indexing of One-Dimensional Arrrays 06:09
    • 46: Fancy Indexing of Two-Dimensional Arrrays 12:32
    • 47: Combining Fancy Index with Normal Indexing 03:25
    • 48: Combining Fancy Index with Normal Slicing 04:36
    • 49: Operations with Comparison Operators 06:09
    • 50: Arithmetic Operations in Numpy 15:10
    • 51: Statistical Operations in Numpy 06:35
    • 52: Solving second-degree equations with NumPy 07:00
    • 53: quiz 02:00

    • 54: What is Numpy? 06:49
    • 55: Why Numpy? 04:23
    • 56: Array and Features 12:08
    • 57: Array Operators 04:53
    • 58: Numpy Functions 18:25
    • 59: Indexing and Slicing 10:15
    • 60: Numpy Exercises 16:03
    • 61: Using Numpy in Linear Algebra 30:14
    • 62: NumExpr Guide 09:15
    • 63: AI, Machine Learning and Deep Learning 04:54
    • 64: History of Machine Learning 06:52
    • 65: Turing Machine and Turing Test 12:10
    • 66: quiz 01:00

    • 67: Introduction to Pandas Library 06:38
    • 68: Creating a Pandas Series with a List 10:21
    • 69: Creating a Pandas Series with a Dictionary 04:53
    • 70: Creating Pandas Series with NumPy Array 03:10
    • 71: Object Types in Series 05:14
    • 72: Examining the Primary Features of the Pandas Series 04:55
    • 73: Most Applied Methods on Pandas Series 12:53
    • 74: Indexing and Slicing Pandas Series 07:13
    • 75: Creating Pandas DataFrame with List 05:33
    • 76: Creating Pandas DataFrame with NumPy Array 03:03
    • 77: Creating Pandas DataFrame with Dictionary 04:01
    • 78: Examining the Properties of Pandas DataFrames 06:32
    • 79: Element Selection Operations in Pandas DataFrames Lesson 1 07:41
    • 80: Element Selection Operations in Pandas Data Frames Lesson 2 06:04
    • 81: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 08:42
    • 82: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 07:33
    • 83: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 05:35
    • 84: Element Selection with Conditional Operations in Pandas Data Frames 11:23
    • 85: Adding Columns to Pandas Data Frames 08:16
    • 86: Removing Rows and Columns from Pandas Data frames 04:00
    • 87: Null Values in Pandas Dataframes 14:42
    • 88: Dropping Null Values Dropna() Function 07:14
    • 89: Filling Null Values0 Fillna() Function 11:36
    • 90: Setting Index in Pandas DataFrames 07:03
    • 91: Multi-Index and Index Hierarchy in Pandas DataFrames 09:17
    • 92: Element Selection in Multi-Indexed DataFrames 05:12
    • 93: Selecting Elements Using the xs() Function in Multi-Indexed DataFrames 07:03
    • 94: Concatenating Pandas Dataframes Concat Function 12:40
    • 95: Merge Pandas Dataframes Merge() Function Lesson 1 10:45
    • 96: Merge Pandas Dataframes Merge() Function Lesson 2 05:37
    • 97: Merge Pandas Dataframes Merge() Function Lesson 3 09:44
    • 98: Merge Pandas Dataframes Merge() Function Lesson 4 07:34
    • 99: Joining Pandas Dataframes Join() Function 11:41
    • 100: Loading a Dataset from the Seaborn Library 06:41
    • 101: Examining the Data Set 07:29
    • 102: Aggregation Functions in Pandas DataFrames 21:45
    • 103: Examining the Dataset 10:38
    • 104: Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes 18:14
    • 105: Advanced Aggregation Functions Aggregate() Function 07:40
    • 106: Advanced Aggregation Functions Filter() Function 06:30
    • 107: Advanced Aggregation Functions The Transform() Function 11:38
    • 108: Advanced Aggregation Functions The Apply() Function 10:06
    • 109: Examining the Dataset 08:14
    • 110: Pivot Tables in Pandas Library 10:35
    • 111: Accessing and Making Files Available 05:11
    • 112: Data Entry with Csv and Txt Files 13:35
    • 113: Data Entry with Excel Files 04:25
    • 114: Output of File with CSV Extension 07:09
    • 115: Outputting as an Excel File 03:43
    • 116: quiz 09:00

    • 117: What is Pandas 05:48
    • 118: Series and Features 20:06
    • 119: Data Frame Attributes and Methods 18:14
    • 120: Data Frame Attributes and Methods Part – II 13:04
    • 121: Data Frame Attributes and Methods Part – III 11:38
    • 122: Multi Index 11:59
    • 123: Groupby Operations 13:30
    • 124: Missing Data and Data Munging 21:08
    • 125: Missing Data and Data Munging Part II 10:37
    • 126: How We Deal with Missing Data? 17:19
    • 127: Combining Data Frames 20:25
    • 128: Combining Data Frames Part – II 19:28
    • 129: Work with Dataset Files 11:29

    • 130: Basic Plots in Matplotlib I 26:47
    • 131: Basic Plots in Matplotlib II 13:28
    • 132: Figure Customization 14:47
    • 133: Figure Subplot Multiplot Axes 17:28
    • 134: Grid, Spines, Ticks 07:05
    • 135: Plot Customization 06:44
    • 136: Using Pyplot - Pylab - Matplotlib 07:19
    • 137: Using Pyplot 07:29
    • 138: What is Data Visualization 07:53
    • 139: What is Matplotlib 03:02
    • 140: quiz 01:00

    • 141: Basic Plots in Seabornlib 19:57
    • 142: Color Palette 13:00
    • 143: Controlling Figure Aesthetics 10:21
    • 144: Example 09:07
    • 145: Multi-Plots in Seaborn 09:19
    • 146: Regression Plots and Squarify 14:22
    • 147: What is Seaborn 04:09
    • 148: quiz 02:00

    • 149: Example - II 16:08
    • 150: Example - III 09:39
    • 151: Example 08:16
    • 152: What is Geoplotlib 08:43
    • 153: quiz 01:00

Course media

Description

Hello there,
Machine learning python, python, machine learning, Django, ethical hacking, python bootcamp, data analysis, machine learning python, python for beginners, data science, machine learning, django:

Welcome to the “Python: Machine Learning, Deep Learning, Pandas, Matplotlib” course.

Python, Machine Learning, Deep Learning, Pandas, Seaborn, Matplotlib, Geoplotlib, NumPy, Data Analysis, Tensorflow


Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work.

In this course, we will learn what is Deep Learning and how does it work.

This course has suitable for everybody who is interested in Machine Learning and Deep Learning concepts in Data Science.

First of all, in this course, we will learn some fundamental stuff of Python and the Numpy library. These are our first steps in our Deep Learning journey. After then we take a little trip to Machine Learning Python history. Then we will arrive at our next stop. Machine Learning in Python Programming. Here we learn the machine learning concepts, machine learning a-z workflow, models and algorithms, and what is neural network concept. After then we arrive at our next stop. Artificial Neural network. And now our journey becomes an adventure. In this adventure we'll enter the Keras world then we exit the Tensorflow world. Then we'll try to understand the Convolutional Neural Network concept. But our journey won't be over. Then we will arrive at Recurrent Neural Network and LTSM. We'll take a look at them. After a while, we'll trip to the Transfer Learning concept. And then we arrive at our final destination. Projects in Python Bootcamp. Our play garden. Here we'll make some interesting machine learning models with the information we've learned along our journey.

In this course, we will start from the very beginning and go all the way to the end of "Deep Learning" with examples.

The Logic of Machine Learning such as Machine Learning models and algorithms, Gathering data, Data pre-processing, Training and testing the model etc.

Before we start this course, we will learn which environments we can be used for developing deep learning projects.

During the course you will learn:

  1. Fundamental stuff of Python and its library Numpy

  2. What is the Artificial Intelligence (Ai), Machine Learning, and Deep Learning

  3. History of Machine Learning

  4. Turing Machine and Turing Test

  5. The Logic of Machine Learning such as

    • Understanding the machine learning models

    • Machine Learning models and algorithms

    • Gathering data

    • Data pre-processing

    • Choosing the right algorithm and model

    • Training and testing the model

    • Evaluation

  6. Artificial Neural Network with these topics

    • What is ANN

    • Anatomy of NN

    • Tensor Operations

    • The Engine of NN

    • Keras

    • Tensorflow

  7. Convolutional Neural Network

  8. Recurrent Neural Network and LTSM

  9. Transfer Learning

  10. Reinforcement Learning

Finally, we will make four different projects to reinforce what we have learned.

Object-oriented programming (OOP) is a computer programming paradigm where a software application is developed by modeling real world objects into software modules called classes. Consider a simple point of sale system that keeps record of products purchased from whole-sale dealers and the products sold to the customer. An object-oriented language would implement these requirements by creating a Product class, a Customer class, a Dealer class and an Order class. All of these classes would interact together to deliver the required functionality where each class would be concerned with storing its own data and performing its own functions. This is the basic idea of object-oriented programming or also called OOP.

When you enroll, you will feel the OAK Academy`s seasoned developers' expertise. Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest.

Video and Audio Production Quality

All our videos are created/produced as high-quality video and audio to provide you the best learning experience.

You will be,

  • Seeing clearly

  • Hearing clearly

  • Moving through the course without distractions

You'll also get:

  • Lifetime Access to The Course

  • Fast & Friendly Support in the Q&A section

We offer full support, answering any questions.

If you are ready to learn “Python: Machine Learning, Deep Learning, Pandas, Matplotlib”

Dive in now! See you in the course!

Who is this course for?

  • Anyone who has programming experience and wants to learn machine learning and deep learning.
  • Statisticians and mathematicians who want to learn machine learning and deep learning.
  • Tech geeks who curious with Machine Learning and Deep Learning concept.
  • Data analysts who want to learn machine learning and deep learning.
  • If you are one of these, you are in the right place. But please don't forget. You must know a little bit of coding and scripting.
  • Anyone who need a job transition
  • People who want to data analysis, pandas
  • People who want to learn artificial intellience, ai, reinforcement learning
  • People who want to learn machine learning, deep learning, python pandas numpy, pandas numpy
  • People who want to learn data science python, matplotlib, numpy

Requirements

  • Coding skills are a plus

  • Math skills will boost your understanding

  • Be able to download and install all the free software and tools needed to practice

  • A strong work ethic, willingness to learn and plenty of excitement about the back door of the digital world

  • Just you, your computer and your ambition to get started now!

  • Desire to learn machine learning python

  • Desire to learn python learning python

  • Desire to learn data science with python

  • Desire to learn data analysis

  • Desire to learn pandas for data anlaysis

  • Desire to learn matplotlib

Questions and answers

Currently there are no Q&As for this course. Be the first to ask a question.

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Certificates

Reed Courses Certificate of Completion

Digital certificate - Included

Will be downloadable when all lectures have been completed.

Reviews

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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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