Machine Learning With TensorFlow The Practical Guide

Machine Learning; an application of AI which is known to provide any system the ability to learn without programming. It allows software to predict the outcome more accurately. Machine learning proces...

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Course Description

Machine Learning; an application of AI which is known to provide any system the ability to learn without programming. It allows software to predict the outcome more accurately. Machine learning processes the input data or instructions to form a pattern by statistical analysis for making better decisions. It is so common today that most of us use it on the daily basis and don’t even notice it. Like...

Machine Learning; an application of AI which is known to provide any system the ability to learn without programming. It allows software to predict the outcome more accurately. Machine learning processes the input data or instructions to form a pattern by statistical analysis for making better decisions. It is so common today that most of us use it on the daily basis and don’t even notice it. Likewise, Tensorflow was recently launched by Google as an open source library which is widely used for high-performance computations as required in machine learning. Knowing this fact, we launched Machine Learning with Tensorflow detailing its every aspects. Why this course is important? From Facebook’s news feed to self-driving cars, machine learning is available everywhere making its knowledge important for every developer. Successfully, it has created its impact on almost every type of businesses. Meanwhile, Tensorflow has become a perfect tool for machine learning by not only performing high computations but also allowing users to build the dataflows. This course comprises numerous topics with the sole aim to understand Tensorflow and machine learning. What makes this course so valuable? This course gives an insight into the basics of Tensorflow covering topics like tensors, operators and variables. It is a good option to master machine learning, its types and various main algorithms including linear regression. Furthermore, this course also covers advanced machine learning like a neural network, convolution neural network and others. Here, you’ll also gain the practice by implementing it in a project on Deep Neural Network.

What you’ll learn
  • 1. Fundamentals of Tensorflow and its installation on Windows, Mac and Linux
  • 2. Basics of Tensorflow including tensors, operators, variables and others
  • 3. Basics of Machine Learning and its types
  • 4. Main algorithms and its implementation - Linear regression, logistic regression, KNN regression and others
  • 5. Clustering and its approaches
  • 6. Advanced Machine Learning- Neural Networks, Convolution Neural Network, Recurrent Neural Networks
  • 7. A project on Deep Neural Networks

Covering Topics

1
Section 1 : Introduction

2
Section 2 : Getting started with Tensorflow

3
Section 3 : Tensorflow Basics

4
Section 4 : Machine Learning Basics

5
Section 5 : Main Algorithms

6
Section 6 : Advance ML

Curriculum

      Section 1 : Introduction
    1
    Introduction
      Section 2 : Getting started with Tensorflow
    2
    Introduction
    3
    Installing Tensorflow on Windows Preview
    4
    Installing Tensorflow on Mac
    5
    Installing Tensorflow on Linux
    6
    Tensorflow Fundamentals
    7
    A Simple Example Using Tensorflow
      Section 3 : Tensorflow Basics
    8
    Tensors
    9
    Operators
    10
    Operators with Sessions
    11
    Variables
    12
    Using Jupyter
    13
    Using Tensorboard
    Quiz:
    Section 3 Quiz
      Section 4 : Machine Learning Basics
    14
    What is Machine Learning
    15
    Learning and Inference
    16
    Types of Machine Learning - Supervised, Unsupervised, Reinforcement
    Quiz:
    Section 4 Quiz
      Section 5 : Main Algorithms
    17
    Linear Regression
    18
    Linear Regression Implementation using Tensorflow
    19
    Logistic Regression
    20
    Logistic Regression Implementation using Tensorflow
    21
    NN Regression
    22
    K Nearest Neighbours - Classification
    23
    K Nearest Neighbours - Pseudocode and Error Rate
    24
    KNN Implementation using Tensorflow
    25
    What is Clustering?
    26
    Clustering Approaches
    27
    K-Means Algorithm
    28
    K-Means Implementation using Tensorflow
    29
    Support Vector Machines (SVM)
    30
    Kernels - Introduction
    31
    Kernels
    32
    SVM Implementation using Tensorflow
      Section 6 : Advance ML
    33
    Neural Networks - Introduction
    34
    Neural Networks - Feedforward, Backpropogation, Error
    35
    Neural Networks Implementation using Tensorflow
    36
    Convolutional Neural Networks (CNN)
    37
    CNN Implementation using Tensorflow
    38
    Recurrent Neural Networks (RNN)
    39
    RNN Implementation using Tensorflow
    40
    A Project on Deep Neural Networks using Tensorflow
    Quiz:
    Section 6 Quiz
    41
    test

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