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.
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
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.When does the course start and finish? The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish. How long do I have access to the course? How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own. What if I am unhappy with the course? We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvard's edX, Business Insider and more. Over 11 years, Mammoth Interactive has built a global student community with 1.1 million courses sold. Mammoth Interactive has released over 250 courses and 2,500 hours of video content. Founder and CEO John Bura has been programming since 1997 and teaching since 2002. John has created top-selling applications for iOS, Xbox and more. John also runs SaaS company Devonian Apps, building efficiency-minded software for technology workers like you.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 PandasLearn pandas, a software library written for the Python programming language for data manipulation and analysis.2. Data Visualization with PyPlotLearn 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 TensorFlowLearn 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 MNISTBuild 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: Introduction - Learning the Python Basic
Lecture 1 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 List
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 Intro to Python Slides
Lecture 29 Python_Language_Basics Code
Section 2: NumPy 2020
Lecture 30 Section Intro
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 NumPy Slides
Lecture 41 NumPy code
Section 3: Pandas
Lecture 42 Section Intro
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 Code
Lecture 60 Pandas Practice CSV
Lecture 61 Section Slides
Section 4: PyPlot
Lecture 62 Section Intro
Lecture 63 Intro to PyPlot
Lecture 64 Installing Matplotlib
Lecture 65 Basic Line Plot
Lecture 66 Customizing Graphs
Lecture 67 Plotting Multiple Datasets
Lecture 68 Bar Chart
Lecture 69 Pie Chart
Lecture 70 Histogram
Lecture 71 3D Plotting
Lecture 72 Course Outro
Lecture 73 Section Code
Section 5: Machine Learning Theory
Lecture 74 Section Intro
Lecture 75 Quick Intro to Machine Learning
Lecture 76 Deep Dive into Machine Learning
Lecture 77 Problems Solved with Machine Learning Part 1
Lecture 78 Problems Solved with Machine Learning Part 2
Lecture 79 Types of Machine Learning
Lecture 80 How Machine Learning Works
Lecture 81 Common Machine Learning Structures
Lecture 82 Steps to Build a Machine Learning Program
Lecture 83 Summary and Outro
Lecture 84 Intro to Machine Learning Slides
Section 6: Introduction to Tensorflow
Lecture 85 Section intro
Lecture 86 Intro to Tensorflow
Lecture 87 Installing Tenforflow
Lecture 88 Intro to Linear Regression
Lecture 89 Linear Regression Model - Creating Dataset
Lecture 90 Linear Regression Model - Building the Model
Lecture 91 Linear Regression Model - Creating a Loss Function
Lecture 92 Linear Regression Model - Training the Model
Lecture 93 Linear Regression Model - Testing the Model
Lecture 94 Summary and Outro
Lecture 95 Course Slides
Lecture 96 Course Code
Absolute beginners to programming,Developers transferring from other languages,Developers who need to learn machine learning
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