The Ultimate Python Machine Learning with TensorFlow Course
3.8 (3 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
28 students enrolled

The Ultimate Python Machine Learning with TensorFlow Course

Learn everything you need to become a data scientist. Jump into Pandas, PyPlot, MNIST, Keras and more popular libraries.
3.8 (3 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
28 students enrolled
Last updated 4/2020
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 15.5 hours on-demand video
  • 11 articles
  • 11 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Code in Python from scratch
  • Machine learning theory applied in practical examples
  • Pandas data manipulation and analysis
  • PyPlot, a MATLAB-like plotting framework
  • Build machine learning models in TensorFlow
  • Build a convolutional neural network
  • Use Keras with machine learning models
Course content
Expand all 96 lectures 15:19:17
+ Introduction - Learning the Python Basic
29 lectures 04:51:18
Variables
19:34
Type Conversion Examples
10:21
Operators
07:21
Operators Examples
22:09
Collections
08:39
List
11:55
Multidimensional List Examples
08:22
Tuples Examples
08:51
Dictionaries Examples
14:41
Ranges Examples
08:47
Conditionials
06:58
If Statements Examples
10:32
If Statements Variants Examples
11:35
Loops
07:17
While Loops Examples
11:47
For Loops Examples
11:35
Functions
08:04
Functions Examples
09:33
Parameters and Return Values Examples
14:08
Classes and Objects
11:30
Classes Examples
13:28
Objects Examples
10:11
Inheritance Examples
17:43
Static Members Examples
11:20
Summary and Outro
04:23
Intro to Python Slides
00:00
Python_Language_Basics Code
00:00
+ NumPy 2020
12 lectures 01:48:09
Section Intro
05:11
Intro to NumPy
06:20
Installing NumPy
03:59
Creating NumPy Arrays
16:55
Creating NumPy Matrices
11:57
Getting and Setting NumPy Elements
16:59
Arithmetic Operations on NumPy Arrays
11:56
NumPy Functions Part 1
19:13
NumPy Functions Part 2
12:36
Summary and Outro
03:01
NumPy Slides
00:01
NumPy code
00:00
+ Pandas
20 lectures 04:28:40
Section Intro
05:43
Intro to Pandas
07:55
Installing Pandas
05:28
Creating Pandas Series
20:34
Date Ranges
11:29
Getting Elements from Series
19:21
Getting Properties of Series
13:04
Modifying Series
19:01
Operations on Series
11:48
Creating Pandas DataFrames
22:57
Getting Elements from DataFrames
25:12
Getting Properties from DataFrames
17:44
DataFrame Modification
36:24
DataFrame Operations
20:09
DataFrame Comparisons and Iteration
15:35
Reading CSV
12:00
Summary and Outro
04:14
Section Code
00:00
Pandas Practice CSV
00:01
Section Slides
00:00
+ PyPlot
12 lectures 01:19:51
Section Intro
05:30
Intro to PyPlot
05:10
Installing Matplotlib
05:51
Basic Line Plot
07:53
Customizing Graphs
10:47
Plotting Multiple Datasets
08:10
Bar Chart
06:26
Pie Chart
09:13
Histogram
10:14
3D Plotting
06:28
Course Outro
04:09
Section Code
00:00
+ Machine Learning Theory
11 lectures 01:46:07
Section Intro
06:05
Quick Intro to Machine Learning
09:01
Deep Dive into Machine Learning
06:01
Problems Solved with Machine Learning Part 1
13:26
Problems Solved with Machine Learning Part 2
16:25
Types of Machine Learning
10:15
How Machine Learning Works
11:40
Common Machine Learning Structures
13:51
Steps to Build a Machine Learning Program
16:34
Summary and Outro
02:49
Intro to Machine Learning Slides
00:00
+ Introduction to Tensorflow
12 lectures 01:05:09
Section intro
06:10
Intro to Tensorflow
05:33
Installing Tenforflow
03:52
Intro to Linear Regression
09:26
Linear Regression Model - Creating Dataset
05:49
Linear Regression Model - Building the Model
07:22
Linear Regression Model - Creating a Loss Function
05:57
Linear Regression Model - Training the Model
12:42
Linear Regression Model - Testing the Model
05:22
Summary and Outro
02:55
Course Slides
00:00
Course Code
00:00
Requirements
  • No experience necessary
Description

Machine learning is quickly becoming a required skill for every software developer.

Enroll now to learn everything you need to know to get up to speed, whether you're a developer or aspiring data scientist. This is the course for you.

Your complete Python course for image recognition, data analysis, data visualization and more.

Reviews On Our Python Courses:

  • "I know enough Python to be dangerous. Most of the ML classes are so abstract and theoretical that no learning happens. This is the first class where we use concrete examples that I can relate to and allow me to learn. Absolutely love this course!" - Mary T.


  • "Yes, this is an amazing start. For someone new in python this is a very simple boot course. I am able to relate to my earlier programming experience with ease!" - Gajendran C.


  • "Clear and concise information" - Paul B.


  • "Easy to understand and very clear explanations. So far so good!!!" - Alejandro M.

Don't miss the biggest Python course of the year. This is a once in a lifetime chance to enroll in a massive course.

Absolutely no experience necessary. Start with a complete introduction to Python that is perfect for absolute beginners and can also be used a review.

Jump into using the most popular libraries and frameworks for working with Python. You'll learn everything you need to become a data scientist. This includes:

1. Data Analysis with Pandas

Learn pandas, a software library written for the Python programming language for data manipulation and analysis.

2. Data Visualization with PyPlot

Learn pyplot, a MATLAB-like plotting framework enabling you to create a figure, create a plotting area in a figure, plot lines in a plotting area, decorate the plot with labels and much more. Learn it all in this massive course.

3. Machine Learning Theory

Machine learning is in high demand and is quickly becoming a requirement on every software engineer's resume. Learn how to solve problems with machine learning before diving into practical examples.

4. Introduction to TensorFlow

Learn TensorFlow, the most popular plaform enabling ML developers to build and deploy machine learning applications such as neural networks. Build your first linear regression model with TensorFlow. Learn how to build a dataset, model, train and test!

5. Image Recognition with MNIST

Build a convolutional neural network (CNN.) Learn how to use Keras with machine learning models.

Keras is a neural-network library written in Python capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, and PlaidML. You'll be able to enable fast experimentation with deep neural networks with Keras.

All source code is included for each project.

If you buy one course this year, this is it. Sign up while spots are open.

Who this course is for:
  • Absolute beginners to programming
  • Developers transferring from other languages
  • Developers who need to learn machine learning