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
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