Machine Learning and Deep Learning A-Z: Hands-On Python

Hello there,

Welcome to the “Machine Learning and Deep Learning A-Z: Hands-On Python” course.

Do you know data science needs will create 11.5 million job openings by 2026?

Do you know the average salary is $100.000 for data science careers!
Data Science Careers Are Shaping The Future

• If you want to learn one of the employer’s most request skills?

• If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?

• If you are an experienced developer and looking for a landing in Data Science!

In all cases, you are at the right place!

We've designed for you “Machine Learning and Deep Learning A-Z: Hands-On Python ” a straightforward course for Python Programming Language and Machine Learning.

In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples.

We will open the door of the Data Science and Machine Learning a-z world and will move deeper. You will learn the fundamentals of Machine Learning A-Z and its beautiful libraries such as Scikit Learn.

Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms.

In this course, we will learn what is data visualization and how does it work with python.

This course has suitable for everybody who is interested data vizualisation concept.

Learn python and how to use it to python data analysis and visualization, present data. Includes tons of code data vizualisation.

In this course, you will learn data analysis and visualization in detail.

Also during the course, you will learn:

The Logic of Matplotlib

• What is Matplotlib

• Using Matplotlib

• Pyplot – Pylab - Matplotlib - Excel

• Figure, Subplot, Multiplot, Axes,

• Figure Customization

• Plot Customization

• Grid, Spines, Ticks

• Basic Plots in Matplotlib

• Overview of Jupyter Notebook and Google Colab

1. Seaborn library with these topics

   • What is Seaborn

   • Controlling Figure Aesthetics

   • Color Palettes

   • Basic Plots in Seaborn

   • Multi-Plots in Seaborn

   • Regression Plots and Squarify

2. Geoplotlib with these topics

   • What is Geoplotlib

   • Tile Providers and Custom Layers

This Machine Learning course is for everyone!

My "Machine Learning with Hands-On Examples in Data Science" is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher).

What you will learn?

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

Before each lesson, there will be a theory part. After learning the theory parts, we will reinforce the subject with practical examples.

With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.

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

During the course you will learn:

What is the AI, Machine Learning, and Deep Learning

1. History of Machine Learning

2. Turing Machine and Turing Test

3. 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

4. Artificial Neural Network with these topics

   • What is ANN

   • Anatomy of NN

   • Tensor Operations

   • The Engine of NN

   • Keras

   • Tensorflow

5. Convolutional Neural Network

6. Recurrent Neural Network and LTSM

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

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

Why would you want to take this course?

Our answer is simple: The quality of teaching.

Reed courses certificate of completion

Digital certificate - Included

• Python, Machine Learning and Deep Learning Code Files And Resources 05:00

  • 1: Section 1 Data Visualisation - Matplotlib Files in Python 01:00
  • 2: Section 2 Data Visualization - Seaborn 01:00
  • 3: Section 3 Data Visualisation - Geoplotlib 01:00
  • 4: Section 4 to 17 Machine Learning Part 01:00
  • 5: Section 18 To 23 Deep Learning Part 01:00

• Data Visualisation - Matplotlib 1:45:10

  • 6: 04_01 What is Matplotlib 03:02
  • 7: 04_02 Using Pyplot 07:30
  • 8: 04_03 Pyplot - Pylab - Matplotlib 07:19
  • 9: 04_04 Figure, Subplot, Multiplot, Axes 17:28
  • 10: 04_05 Figure Customization 14:47
  • 11: Basic Plots in Matplotlib I 26:47
  • 12: Basic Plots in Matplotlib II 13:28
  • 13: Grid, Spines, Ticks 07:05
  • 14: Plot Customization 06:44
  • 15: quiz 01:00

• Data Visualization - Seaborn 1:21:15

  • 16: What is Seaborn 04:09
  • 17: Controlling Figure Aesthetics 10:21
  • 18: Example 09:07
  • 19: Color Palette 13:00
  • 20: Basic Plots in Seabornlib 19:57
  • 21: Multi-Plots in Seaborn 09:19
  • 22: Regression Plots and Squarify 14:22
  • 23: quiz 01:00

• Data Visualisation - Geoplotlib 43:46

  • 24: What is Geoplotlib 08:43
  • 25: Example 08:16
  • 26: Example - II 16:08
  • 27: Example - III 09:39
  • 28: quiz 01:00

• First Contact with Machine Learning 08:44

  • 29: 1 - What is Machine Learning 04:05
  • 30: 2 - Machine Learning Terminology 02:39
  • 31: Machine Learning Project Files 01:00
  • 32: quiz 01:00

• Evalution Metrics in Machine Learning 45:00

  • 33: 4 - Classification vs Regression 03:38
  • 34: 5 - Classification Error Metrics 20:15
  • 35: 6 - Regression Error Metrics 07:54
  • 36: 7 - Machine Learning with Python 13:13

• Supervised Learning with Machine Learning 11:27

  • 37: 8 - Supervised Learning Overview 11:27

• Linear Regression Algorithm 1:08:42

  • 38: 9 - Linear Regression Theory 06:09
  • 39: 10 - Linear Regression with Python Part 1 23:22
  • 40: 11 - Linear Regression with Python Part 2 09:12
  • 41: 12 - Linear Regression Project Overview 03:19
  • 42: 13 - Linear Regression Project Solution 25:40
  • 43: quiz 01:00

• Bias Variance Trade-Off in Machine Learning 09:30

  • 44: 14 - BIAS Variance Trade-Off 08:30
  • 45: quiz 01:00

• Logistic Regression Algorithm in Machine Learning A-Z 1:28:02

  • 46: 15 - Logistic Regression Theory 14:51
  • 47: 16 - Logistic Regression with Python Part 1 21:00
  • 48: 17 - Logistic Regression with Python Part 2 23:03
  • 49: 18 - Logistic Regression with Python Part 3 10:39
  • 50: 19 - Logistic Regression Project Overview 02:32
  • 51: 20 - Logistic Regression Project Solutions 14:57
  • 52: quiz 01:00

• K Nearest Neighbors Algorithm 56:18

  • 53: 21 - K Nearest Neighbors Algorithm Theory 06:56
  • 54: 22 - K Nearest Neighbors Algorithm With Python 26:38
  • 55: 23 - K Nearest Neighbors Algorithm Project Overview 01:51
  • 56: 24 - K Nearest Neighbors Algorithm Project Solutions 19:53
  • 57: quiz 01:00

• Decision Trees And Random Forest Algorithm 57:03

  • 58: 25 - Decision Trees And Random Forest Algorithm Theory 08:51
  • 59: 26 - Decision Trees And Random Forest Algorithm With Python 15:03
  • 60: 27 - Decision Trees And Random Forest Algorithm Project Overview 04:44
  • 61: 28 - Decision Trees And Random Forest Algorithm Project Solutions - Part 1 16:13
  • 62: 29 - Decision Trees And Random Forest Algorithm Project Solutions - Part 2 12:12

• Support Vector Machine Algorithm 47:13

  • 63: 30 - Support Vecto¨r Machines Algorithm Theory 06:06
  • 64: 31 - Support Vecto¨r Machines Algorithm With Python 24:08
  • 65: 32 - Support Vecto¨r Machines Algorithm Project Overview 02:53
  • 66: 33 - Support Vecto¨r Machines Algorithm Project Solutions 14:06

• Unsupervised Learning 03:26

  • 67: 34 - Unsupervised Learning Overview 03:26

• K Means Clustering Algorithm 47:09

  • 68: 35 - K Means Clustering Algorithm Theory 06:17
  • 69: 36 - K Means Clustering Algorithm With Python 16:48
  • 70: 37 - K Means Clustering Algorithm Project Overview 04:22
  • 71: 38 - K Means Clustering Algorithm Project Solutions 18:42
  • 72: quiz 01:00

• Hierarchical Clustering Algorithm 15:45

  • 73: 39 - Hierarchical Clustering Algorithm Theory 04:41
  • 74: 40 - Hierarchical Clustering Algorithm With Python 11:04

• Principal Component Analysis (PCA) 25:47

  • 75: 41 - Principal Component Analysis (PCA) Theory 04:13
  • 76: 42 - Principal Component Analysis (PCA) With Python 21:34

• Recommender System Algorithm 41:09

  • 77: 43 - Recommender System Algorithm Theory 05:44
  • 78: 44 - Recommender System Algorithm With Python - Part 1 17:54
  • 79: 45 - Recommender System Algorithm With Python - Part 2 17:31

• Fundamentals of Machine Learning 2:58:31

  • 80: 29 - AI, Machine Learning and Deep Learning 04:54
  • 81: 30 - History of Machine Learning 06:52
  • 82: 31 - Turing Machine and Turing Test 12:10
  • 83: 32 - What is Deep Learning 05:53
  • 84: 33 - Learning Representetion from Data 11:15
  • 85: 34 - Workflow of Machine Learning 09:45
  • 86: 35 - Machine Learning Methods 13:34
  • 87: 36 - Supervised Machine Learning Methods - 1 08:47
  • 88: 37 - Supervised Machine Learning Methods - 2 13:26
  • 89: 38 - Supervised Machine Learning Methods - 3 13:53
  • 90: 39 - Supervised Machine Learning Methods - 4 17:04
  • 91: 40 - Unsupervised Machine Learning Methods 23:58
  • 92: 41 - Gathering Data 04:54
  • 93: 42 - Data Pre-Processing 05:33
  • 94: 42 - Data Pre-Processing 05:33
  • 95: 43 - Choosing The Right Algorithm and Model 07:49
  • 96: 44 - Training and testing the model 05:19
  • 97: 45 - Evaluation 06:52
  • 98: quiz 01:00

• Artificial Neural Network 1:32:44

  • 99: 46 - What is ANN 07:19
  • 100: 47 - Anatomy of a neural network 09:22
  • 101: 48 - Creating a Simple ANN 17:33
  • 102: 49 - Tensor Operations - 1 14:04
  • 103: 50 - Tensor Operations - 2 08:20
  • 104: 51 - Keras API 06:46
  • 105: 52 - Optimizers 10:40
  • 106: 53 - What is Tensorflow 17:40
  • 107: quiz 01:00

• Convolutional Neural Network 15:16

  • 108: 54 - What is CNN 15:16

• Recurrent Neural Network and LTSM 13:14

  • 109: 55 - Understanding RNN and LSTM Networks 13:14

• Transfer Learning 16:08

  • 110: 56 - What is Transfer Learning 16:08

• Projects: Python, Machine Learning, Deep Learning 1:22:57

  • 111: 57 - Project - 1 22:32
  • 112: 58 - Project - 2 28:40
  • 113: 59 - Project - 3 15:33
  • 114: 60 - Project - 4 16:12

Hello there,

Welcome to the “Machine Learning and Deep Learning A-Z: Hands-On Python” course.

Do you know data science needs will create 11.5 million job openings by 2026?

Do you know the average salary is $100.000 for data science careers!
Data Science Careers Are Shaping The Future

• If you want to learn one of the employer’s most request skills?

• If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?

• If you are an experienced developer and looking for a landing in Data Science!

In all cases, you are at the right place!

We've designed for you “Machine Learning and Deep Learning A-Z: Hands-On Python ” a straightforward course for Python Programming Language and Machine Learning.

In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples.

We will open the door of the Data Science and Machine Learning a-z world and will move deeper. You will learn the fundamentals of Machine Learning A-Z and its beautiful libraries such as Scikit Learn.

Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms.

Because data can mean an endless number of things, it’s important to choose the right visualization tools for the job. Whether you’re interested in learning Tableau, D3.js, After Effects, or Python, has a course for you.

In this course, we will learn what is data visualization and how does it work with python.

This course has suitable for everybody who is interested data vizualisation concept.

Learn python and how to use it to python data analysis and visualization, present data. Includes tons of code data vizualisation.

In this course, you will learn data analysis and visualization in detail.

Also during the course, you will learn:

The Logic of Matplotlib

• What is Matplotlib

• Using Matplotlib

• Pyplot – Pylab - Matplotlib - Excel

• Figure, Subplot, Multiplot, Axes,

• Figure Customization

• Plot Customization

• Grid, Spines, Ticks

• Basic Plots in Matplotlib

• Overview of Jupyter Notebook and Google Colab

1. Seaborn library with these topics

   • What is Seaborn

   • Controlling Figure Aesthetics

   • Color Palettes

   • Basic Plots in Seaborn

   • Multi-Plots in Seaborn

   • Regression Plots and Squarify

2. Geoplotlib with these topics

   • What is Geoplotlib

   • Tile Providers and Custom Layers

This Machine Learning course is for everyone!

My "Machine Learning with Hands-On Examples in Data Science" is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher).

Why we use a Python programming language in Machine learning?

Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today’s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development.

What you will learn?

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

Before each lesson, there will be a theory part. After learning the theory parts, we will reinforce the subject with practical examples.

During the course you will learn the following topics:

• What is Machine Learning?

• More About Machine Learning

• Machine Learning Terminology

• Evaluation Metrics

• What is Classification vs Regression?

• Evaluating Performance-Classification Error Metrics

• Evaluating Performance-Regression Error Metrics

• Machine Learning with Python

• Supervised Learning

• Cross-Validation and Bias Variance Trade-Off

• Use Matplotlib and seaborn for data visualizations

• Machine Learning with SciKit Learn

• Linear Regression Theory

• Logistic Regression Theory

• Logistic Regression with Python

• K Nearest Neighbors Algorithm Theory

• K Nearest Neighbors Algorithm With Python

• K Nearest Neighbors Algorithm Project Overview

• K Nearest Neighbors Algorithm Project Solutions

• Decision Trees And Random Forest Algorithm Theory

• Decision Trees And Random Forest Algorithm With Python

• Decision Trees And Random Forest Algorithm Project Overview

• Decision Trees And Random Forest Algorithm Project Solutions

• Support Vector Machines Algorithm Theory

• Support Vector Machines Algorithm With Python

• Support Vector Machines Algorithm Project Overview

• Support Vector Machines Algorithm Project Solutions

• Unsupervised Learning Overview

• K Means Clustering Algorithm Theory

• K Means Clustering Algorithm With Python

• K Means Clustering Algorithm Project Overview

• K Means Clustering Algorithm Project Solutions

• Hierarchical Clustering Algorithm Theory

• Hierarchical Clustering Algorithm With Python

• Principal Component Analysis (PCA) Theory

• Principal Component Analysis (PCA) With Python

• Recommender System Algorithm Theory

• Recommender System Algorithm With Python

With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.

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

During the course you will learn:

What is the AI, Machine Learning, and Deep Learning

1. History of Machine Learning

2. Turing Machine and Turing Test

3. 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

4. Artificial Neural Network with these topics

   • What is ANN

   • Anatomy of NN

   • Tensor Operations

   • The Engine of NN

   • Keras

   • Tensorflow

5. Convolutional Neural Network

6. Recurrent Neural Network and LTSM

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

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

Why would you want to take this course?

Our answer is simple: The quality of teaching.

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.

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 the “Machine Learning and Deep Learning A-Z: Hands-On Python” course.

Dive in now! See you in the course!

• 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. It is for everyone
• Anyone who wants to start learning "Machine Learning"
• Anyone who needs a complete guide on how to start and continue their career with machine learning
• Software developer who wants to learn "Machine Learning"
• Students Interested in Beginning Data Science Applications in Python Environment
• People Wanting to Specialize in Anaconda Python Environment for Data Science and Scientific Computing
• Students Wanting to Learn the Application of Supervised Learning (Classification) on Real Data Using Python
• People who want to learn machine learning, deep learning, python

• Basic knowledge of Python Programming Language

• Be Able To Operate & Install Software On A Computer

• Free software and tools used during the course

• Determination to learn and patience.

• Desire to master on python, machine learning a-z, deep learning a-z

• Motivation to learn the the second largest number of job postings relative program language among all others

• Data visualization libraries in python such as seaborn, matplotlib

• Learn to create Machine Learning and Deep Algorithms in Python Code templates included.

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