Machine Learning Zero To Hero - Hands-On With Tensorflow

Get to grips with TensorFlow. Become an AI, Machine Learning, and Deep Learning expert.

What you'll learn

Practical implementation with comprehensive examples of canonical machine learning, and supervised and unsupervised machine learning

Deep learning and image-classification examples, and time series predictive model examples

Effectively use TensorFlow in your production system, including framing a task in each task example

Fundamentals of machine learning

Requirements

Mac / Windows / Linux - all operating systems work with this course!

No previous TensorFlow knowledge required. Basic understanding of Machine Learning is helpful

Description

Have you been looking for a course that teaches you effective machine learning in TensorFlow? Or have you always wanted an efficient and skilled working knowledge of how to solve problems that can't be explicitly programmed through the latest machine learning techniques? If you're familiar with pandas and NumPy, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data. The theory will be underpinned with plenty of practical examples, and code example walk-throughs. The course aims to make you highly efficient at constructing algorithms and models that perform with the highest possible accuracy based on the success output or hypothesis you've defined for a given task.TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD according to 2023 statistics. By passing this certificate, which is officially recognized by Google, you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow developer! If you pass the exam, you will also be part of Google's TensorFlow Developer Network where recruiters are able to find you. The goal of this course is to teach you all the skills necessary for you to go and pass the exam and get your TensorFlow Certification from Google so you can display it on your resume, LinkedIn, Github and other social media platforms to truly make you stand out. By the end of this course, you will be able to comfortably solve an array of industry-based machine learning problems by training, optimizing, and deploying models into production. Being able to do this effectively will allow you to create successful prediction and decisions for the task in hand.

Overview

Section 1: Machine Learning ZERO to HERO - Hands-on with Tensorflow

Lecture 1 Introduction to Machine Learning with Tensorflow

Lecture 2 Understanding Machine Learning

Lecture 3 How do Machines Learns

Lecture 4 Uses of Machine Learning

Lecture 5 Examples with tensorflow by Google

Lecture 6 Setting up the Workstation

Lecture 7 Understanding program languages

Lecture 8 Understanding and Functions of Jupyter

Lecture 9 Learning of Jupyter installation

Lecture 10 Understanding what Anaconda cloud is

Lecture 11 Installation of Anaconda for Windows

Lecture 12 Installation of Anaconda in Linux

Lecture 13 Using the Jupyter notebook

Lecture 14 Getting started with Anaconda

Lecture 15 Determining options for Cloudberry

Lecture 16 Introduction to Third Party Libraries

Lecture 17 Numpy-Array

Lecture 18 Numpy-Array Continue

Lecture 19 Arrays

Lecture 20 Arrays Continue

Lecture 21 Indexing

Lecture 22 Indexing Continue

Lecture 23 Universal Functions

Lecture 24 Introoduction to Pandas

Lecture 25 Pandas Series

Lecture 26 Pandas Series Continue

Lecture 27 Import Randin

Lecture 28 Import Randin Continue

Lecture 29 Paratmeters

Lecture 30 Indexing and Database

Lecture 31 Missing Data

Lecture 32 Missing Data-Groupby

Lecture 33 Missing Data-Groupby Continue

Lecture 34 Concat-Merge-Join

Lecture 35 Operations

Lecture 36 Import-Export

Lecture 37 Python Visualisation

Lecture 38 Mat Plotting

Lecture 39 Multiple Plot Subsections

Lecture 40 API Functionality

Lecture 41 Title of the Plot

Lecture 42 Change Size of Articles

Lecture 43 Two Different Crops

Lecture 44 Mat Plotting Label

Lecture 45 Marker Color

Lecture 46 Create a New Dataframe

Lecture 47 Change the Style

Lecture 48 Index and Value

Lecture 49 Seaborn-Statistical Data Visualization

Lecture 50 Seaborn library

Lecture 51 Jointplot

Lecture 52 Pairplot

Lecture 53 Barplot

Lecture 54 Boxplot

Lecture 55 Stripplot

Lecture 56 Matrix

Lecture 57 Matrix Continue

Lecture 58 Grid

Lecture 59 Grid Continue

Lecture 60 Style

Lecture 61 Python Libraries Conclusion

Lecture 62 Introduction To Conda Envirement

Lecture 63 Scikit Learn

Lecture 64 Scikit Learn Continue

Lecture 65 Datasets

Lecture 66 California Dataset

Lecture 67 Data Visualization

Lecture 68 Datavisualization Continue

Lecture 69 Downloading a Test Data

Lecture 70 Population Parameter

Lecture 71 Processing

Lecture 72 Null Values with Median Value

Lecture 73 Replace Missing Values

Lecture 74 Label Enconder

Lecture 75 Import Labelencoder

Lecture 76 Custom Transformation

Lecture 77 Transformer Custom Transformer

Lecture 78 Housing with Custom Colums

Lecture 79 Numeric Hosing Data

Lecture 80 Liner Regression

Lecture 81 Fine Tuning Model

Lecture 82 Fine Tuning Model Continue

Lecture 83 Quick-Recap

Lecture 84 Tensorflow

Lecture 85 Tensorflow-Hello-World

Lecture 86 Basic Ops

Lecture 87 Basic Ops Continue

Lecture 88 More on Basic Ops

Lecture 89 Eager-Mode

Lecture 90 Concept

Lecture 91 Linear-Regression

Lecture 92 Linear-Model

Lecture 93 Matrix Multiplication Function

Lecture 94 Practice for a Simple Linear Model

Lecture 95 Cost Function

Lecture 96 Creative Optimizer

Lecture 97 RR Input and Output Value

Lecture 98 Logistic-Regression

Lecture 99 Global Variabales Initializer

Lecture 100 Run Optimizer

Lecture 101 Create a Range

Lecture 102 Introduction to Neural Networks

Lecture 103 Basic-Concepts

Lecture 104 Activative Functions

Lecture 105 Activative Functions Input to Output

Lecture 106 Classification Functions

Lecture 107 Tensorflow-Playground

Lecture 108 Mnist-Dataset

Lecture 109 Mnist-Dataset Continue

Lecture 110 More on Mnist-Dataset

Anyone who wants to pass the TensorFlow Developer exam so they can join Google's Certificate Network and display their certificate and badges on their resume, GitHub, and social media platforms including LinkedIn, making it easy to share their level of TensorFlow expertise with the world,Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow,Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning,Anyone looking to master building ML models with the latest version of TensorFlow