The Complete Python And Tensorflow Data Science Course

The complete guide to TensorFlow, data science, data analysis, image recognition and everything else you NEED to know

What you'll learn
Code in Python from scratch
Machine learning theory applied in practical examples
Pandas data manipulation and analysis projects
PyPlot, a MATLAB-like plotting framework
Build machine learning models in TensorFlow
Build projects with NumPy for Python
Build a convolutional neural network
Use Keras with machine learning models
Requirements
No experience necessary
Description
Learn everything you need to become a data scientist. 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 Science with NumPyBuild projects with NumPy, the #1 Python library for data science providing arrays and matrices.2. Data Analysis with PandasBuild projects with pandas, a software library written for the Python programming language for data manipulation and analysis.2. Data Visualization with PyPlotBuild projects with 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 TheoryMachine 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 TensorFlowBuild projects with 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. Build a Convolutional Neural NetworkBuild 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.

Overview

Section 1: Python Language Basics

Lecture 1 Python Language Basics Introduction

Lecture 2 Intro to Python

Lecture 3 Variables

Lecture 4 Type Conversion Examples

Lecture 5 Operators

Lecture 6 Operators Examples

Lecture 7 Collections

Lecture 8 Lists

Lecture 9 Multidimensional List Examples

Lecture 10 Tuples Examples

Lecture 11 Dictionaries Examples

Lecture 12 Ranges Examples

Lecture 13 Conditionials

Lecture 14 If Statements Examples

Lecture 15 If Statements Variants Examples

Lecture 16 Loops

Lecture 17 While Loops Examples

Lecture 18 For Loops Examples

Lecture 19 Functions

Lecture 20 Functions Examples

Lecture 21 Parameters and Return Values Examples

Lecture 22 Classes and Objects

Lecture 23 Classes Examples

Lecture 24 Objects Examples

Lecture 25 Inheritance Examples

Lecture 26 Static Members Examples

Lecture 27 Summary and Outro

Lecture 28 Section Slides

Lecture 29 Section Code

Section 2: Build NumPy Projects

Lecture 30 Build NumPy Projects Introduction

Lecture 31 Intro to NumPy

Lecture 32 Installing NumPy

Lecture 33 Creating NumPy Arrays

Lecture 34 Creating NumPy Matrices

Lecture 35 Getting and Setting NumPy Elements

Lecture 36 Arithmetic Operations on NumPy Arrays

Lecture 37 NumPy Functions Part 1

Lecture 38 NumPy Functions Part 2

Lecture 39 Summary and Outro

Lecture 40 Section Slides

Lecture 41 Section Code

Section 3: Build Pandas Projects

Lecture 42 Build Pandas Projects Introduction

Lecture 43 Intro to Pandas

Lecture 44 Installing Pandas

Lecture 45 Creating Pandas Series

Lecture 46 Date Ranges

Lecture 47 Getting Elements from Series

Lecture 48 Getting Properties of Series

Lecture 49 Modifying Series

Lecture 50 Operations on Series

Lecture 51 Creating Pandas DataFrames

Lecture 52 Getting Elements from DataFrames

Lecture 53 Getting Properties from DataFrames

Lecture 54 DataFrame Modification

Lecture 55 DataFrame Operations

Lecture 56 DataFrame Comparisons and Iteration

Lecture 57 Reading CSV

Lecture 58 Summary and Outro

Lecture 59 Section Slides

Lecture 60 Section Code

Section 4: Build PyPlot Projects

Lecture 61 Build PyPlot Projects Introduction

Lecture 62 Intro to PyPlot

Lecture 63 Installing Matplotlib

Lecture 64 Basic Line Plot

Lecture 65 Customizing Graphs

Lecture 66 Plotting Multiple Datasets

Lecture 67 Bar Chart

Lecture 68 Pie Chart

Lecture 69 Histogram

Lecture 70 3D Plotting

Lecture 71 Course Outro

Lecture 72 Section Code

Section 5: Machine Learning Introduction

Lecture 73 Machine Learning Introduction

Lecture 74 Quick Intro to Machine Learning

Lecture 75 Deep Dive into Machine Learning

Lecture 76 Problems Solved with Machine Learning Part 1

Lecture 77 Problems Solved with Machine Learning Part 2

Lecture 78 Types of Machine Learning

Lecture 79 How Machine Learning Works

Lecture 80 Common Machine Learning Structures

Lecture 81 Steps to Build a Machine Learning Program

Lecture 82 Summary and Outro

Lecture 83 Section Slides

Section 6: Build a Model with TensorFlow

Lecture 84 Build a Model with TensorFlow Introduction

Lecture 85 Intro to Tensorflow

Lecture 86 Installing Tensorflow

Lecture 87 Intro to Linear Regression

Lecture 88 Linear Regression Model - Creating Dataset

Lecture 89 Linear Regression Model - Building the Model

Lecture 90 Linear Regression Model - Creating a Loss Function

Lecture 91 Linear Regression Model - Training the Model

Lecture 92 Linear Regression Model - Testing the Model

Lecture 93 Summary and Outro

Lecture 94 Section Slides

Lecture 95 Section Code

Section 7: Build a Convolutional Neural Network

Lecture 96 Build a Convolutional Neural Network Introduction

Lecture 97 Intro to Image Recognition

Lecture 98 Intro to MNIST

Lecture 99 Building a CNN Part 1 - Obtaining Data

Lecture 100 Building a CNN Part 2 - Building the Model

Lecture 101 Building a CNN Part 3 - Adding Loss and Optimizer Functions

Lecture 102 Building a CNN Part 4 - Train and Test Functions

Lecture 103 Building a CNN Part 5 - Train and Test the Model

Lecture 104 MNIST Image Recognition with Keras Sequential Model

Lecture 105 Summary and Outro

Lecture 106 Section Slides

Lecture 107 Section Code

Absolute beginners to programming,Developers transferring from other languages,Developers who need to learn machine learning