Find top courses on Machine Learning in 2020 is a difficult task. There are dozens of courses/certifications accessible to self-start your career in Machine Learning. These courses are given in either online or offline. The main difficulty of students is facing to choose the best out of these courses. In this post, you will find your […]

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]]>Find top courses on Machine Learning in 2020 is a difficult task. There are dozens of courses/certifications accessible to **self-start** your career in Machine Learning. These courses are given in either online or offline. The main difficulty of students is facing to choose the best out of these courses. In this post, you will find your solution because I am giving you an in-depth review of the top 10 best online machine learning courses in 2020.

I have categorized all these courses based on the following essential criteria.

**Instructor****Course Content/Description****Career Opportunities****Highest Reviewed****Best Sellers****Cost****Highest Rated****Newest etc****Course Quality**

These courses are suitable for beginner level, intermediate as well as advanced level experts.

For most of the Udemy courses, you will get the following benefits.

**Full lifetime access****Access on mobile and TV****Certificate of Completion****30-Day Money-Back Guarantee**

So below is the one by one in-depth review of the Top 10 Best Advanced Machine Learning Courses, Training program accessible online in 2020 to enable you to Learn Machine Learning.

In this course, **2,769,435** students had already enrolled!

This is by far one of the best machine learning course on the internet.

Andrew Ng creates the course who is** Co-Founder of Coursera and Professor at Stanford University**, the program has been **attended by more than 2.7 million students & professionals globally**, who have given it the course an average rating of a whopping 4.9 out of 5.

This course gives a broad introduction to machine learning, data mining, and statistical pattern recognition.

The following topics are included in the course.

- Supervised learning which includes non-parametric algorithms, support vector machines, kernels, neural networks.
- Unsupervised learning which includes clustering, recommender systems, deep learning.
- Best practices in machine learning include variance theory, innovation process in machine learning and Artificial Intelligence.
- The course will also cover some case studies and applications so that you will also study how to apply learning algorithms to building real-time applications like smart robots, text understanding (web search, anti-spam), computer vision, audio, medical informatics, database mining, and other different areas.

- Logistic Regression.
- Artificial Neural Network.
- Machine Learning (ML) Algorithms.

Deep Dive Into The Modern Artificial Intelligence Techniques.

You will teach a computer to see, read, draw, talk, play games, and solve real-time industry problems.

The advanced ML specialization gives the introduction to deep learning, reinforcement learning, natural language understanding, computer vision, and Bayesian methods.

In this course, Top Kaggle machine learning practitioners and CERN scientists will give their experience of solving real-world problems and help you to bridge the gaps between theory and practical.

Upon completion of the following 7 courses, you can apply modern machine learning methods in the enterprise and understand the caveats of real-world data and settings scenarios.

In this course, **10,157** students had already enrolled!

Mikhail Hushchyn and its colleagues create the course.

- Introduction to Deep Learning
- How to Win the Data Science Competition
- Bayesian Methods for Machine Learning
- Practical Reinforcement Learning
- Deep Learning in Computer Vision
- Natural Language Processing
- Addressing the large Hadron Collider Challenges by Machine Learning

All the courses 100% online courses.

Start instantly and study at your schedule. Flexible Schedule.

Set and maintain flexible deadlines.

The course is available in the English language, and You can find the Subtitles in English and Korean languages.

The Coursera Specialization is a series of courses that help you master the skill.

To begin, enroll in the Specialization immediately or review its courses and choose the one you would like to start with.

When you subscribe to a particular course that is part of the Specialization, you’re automatically subscribed to the full Specialization.

Every Specialization covers a hands-on project.

You need to have successfully finished the project(s) to complete the specialization and earn your certificate.

If the specialization covers a separate course for the hands-on project, you’ll need to complete each of the other courses before you can start it.

When you complete every course and complete the hands-on project, you will receive the Certificate that you can share with the prospective employers and your professional network.

The course is sharable on LinkedIn. You can add your certificates in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

Learn how to create Machine Learning Algorithms in Python programming and R programming language from two Data Science experts.

The Author of the course is Jose Portilla.

The course has above 525,000+ students already enrolled.

The course has 4.5 (61,741 ratings) out of 5 stars.

- Data Preprocessing.
- Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.
- Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification.
- Clustering: K-Means, Hierarchical Clustering.
- Association Rule Learning: Apriori, Eclat.
- Reinforcement Learning: Upper Confidence Bound, Thompson Sampling.
- Natural Language Processing: Bag-of-words model and algorithms for NLP.
- Deep Learning: Artificial Neural Networks, Convolutional Neural Networks.
- Dimensionality Reduction: PCA, LDA, Kernel PCA.
- Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost.

- You will learn Machine Learning on Python & R programming languages.
- Have a great foreknowledge of many Machine Learning models.
- Make accurate predictions.
- Make a robust analysis.
- Make robust Machine Learning models.
- Create a secure added value for your business.
- Handle particular topics like Reinforcement Learning, NLP, and Deep Learning.
- Build an army of powerful Machine Learning models and know-how to combine them to solve any problem.

Learn how to use Python NumPy, Pandas, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more libraries and frameworks.

The author of the course is Jose Portilla.

The course had 290,000+ students enrolled.

The ratings for the course are 4.5 (61,741 ratings) out of 5, which is pretty impressive.

- Programming with Python programming language.
- NumPy with Python.
- Using pandas Data Frames to solve complex tasks.
- Use pandas to handle Excel Files.
- Web scraping with python.
- Connect Python to SQL.
- Use matplotlib and seaborn for data visualizations.
- Use plotly for interactive visualizations.
- Machine Learning with SciKit Learn, including.
- Linear Regression and much more

- How to use Python for Data Science and Machine Learning.
- How to use Spark for Big Data Analysis.
- How to implement Machine Learning Algorithms.
- Learn how to use NumPy for Numerical Data.
- Learn how to use Pandas for Data Analysis.
- Learn how to use Matplotlib for Python Plotting.
- Learn how to use Seaborn for statistical plots.
- How to use Plotly for interactive dynamic visualizations.
- How to use SciKit-Learn for Machine Learning Tasks.
- How to use K-Means Clustering.
- How to use Linear Regression.
- How to use Logistic Regression.
- How to use Natural Language Processing and Spam Filters.
- How to use Random Forest and Decision Trees.
- How to use Support Vector Machines.
- How to use Neural Networks.

Kickstart your Career in Data Science & ML. Master data science, learn Python & SQL, analyze & visualize data, build machine learning models.

SAEED AGHABOZORGI instructs the course**.**

The course has over **100,000+ **students already enrolled!

This course deep-dives into the basics of machine learning using an approachable and popular programming language which is Python.

In this course, we will be going through two main components:

- You will learn about the purpose of Machine Learning and where it applies to real-world apps.
- You will get a primary overview of Machine Learning topics such as supervised vs. unsupervised learning, model evaluation, and Machine Learning algorithms.

This course is a part of the multiple programs.

This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

Learn all about Mathematics for Machine Learning which the necessity of data science and machine learning.

For a lot of higher-level courses in the Machine Learning and Data Science, you will find that, you need to freshen up on the basics in the mathematics stuff you may have studied before in school or in the university, but which was taught in complete different context, or not very seriously, such that you struggle to relate it to how it’s practiced in real-world Computer Science.

This Mathematics course is especially for machine learning aims to bridge that gap, getting you up to speed in the underlying mathematics, building the natural understanding, and comparing it to Machine Learning and Data Science.

- Mathematics for Machine Learning: Linear Algebra
- Mathematics for Machine Learning: Multivariate Calculus
- Mathematics for Machine Learning: PCA

In the first course on Linear Algebra, we deep dive into what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

In the second course, there is Multivariate Calculus in which we look at how to optimize the fitting functions to get good fits to data.

It starts with basic calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reducing with a Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data.

This course is of intermediate level devs and will require Python and numpy knowledge.

- Eigenvalues And Eigenvectors.
- Principal Component Analysis (PCA).
- Multivariable Calculus.
- Linear Algebra.

It is a complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks.

Sundog Education creates the course by Frank Kane.

The course has over 115,000+ students enrolled.

The ratings for the course are excellent. It has 4.5(18,428 ratings) out of 5.

It was last updated in December 2019, so all the latest updates are included in the course.

- Build artificial neural networks with Tensorflow and Keras.
- Classify images, data, and sentiments using deep learning.
- Make predictions using linear regression, polynomial regression, and multivariate regression.
- Data Visualization with MatPlotLib and Seaborn.
- Implement machine learning at a massive scale with Apache Spark’s MLLib.
- Understand reinforcement learning – and how to build a Pac-Man bot.
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA.
- Use train/test and K-Fold cross-validation to choose and tune your models.
- Build a movie recommender system using item-based and user-based collaborative filtering.
- Design and evaluate A/B tests using T-Tests and P-Values.

Learn how to use the R language for data science and machine learning and data visualization!

Jose Portilla creates the course.

The course has over 50,000 students enrolled.

The ratings for the course are excellent. It has 4.6 (9,261 ratings) out of 5.

- Program in R.
- Use R for Data Analysis.
- Create Data Visualizations.
- Use R to handle csv, excel, SQL files, or web scraping.
- Use R to manipulate data easily.
- Use R for Machine Learning Algorithms.
- Use R for Data Science.

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts.

The course is Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team.

The course has over 200,000+ students enrolled.

The ratings for the course are excellent. It has 4.5 (26,941 ratings) out of 5.

- Understand the presentiment behind Artificial Neural Networks.
- Apply Artificial Neural Networks in the application.
- Understand the intuition behind Convolutional Neural Networks.
- Apply Convolutional Neural Networks in practice.
- Understand the foreknowledge behind Recurrent Neural Networks.
- Apply Recurrent Neural Networks in practice.
- Understand the foreknowledge behind Self-Organizing Maps.
- Apply Self-Organizing Maps in an application.
- Understand the foreknowledge behind Boltzmann Machines.
- Apply Boltzmann Machines in an application.
- Understand the foreknowledge behind AutoEncoders.
- Apply AutoEncoders in an application.

Learn how to use Google’s Deep Learning Framework – TensorFlow with Python! Solve problems with cutting edge techniques.

Jose Portilla creates the course.

The course has over 70,000+ students enrolled.

The ratings for the course are excellent. It has 4.4 (13,352 ratings) out of 5.

- Understand how Neural Networks work.
- Build your own Neural Network from Scratch using Python.
- Use TensorFlow for Classification and Regression Tasks.
- Use TensorFlow for Image Classification with Convolutional Neural Networks.
- Use TensorFlow for Time Series Analysis with Recurrent Neural Networks.
- Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders.
- Learn how to conduct Reinforcement Learning with OpenAI Gym.
- Create Generative Adversarial Networks with TensorFlow.

Machine Learning is currently one of the most prominent careers in the IT industry.

If you want to stay ahead in the competition, then you had to take online machine learning courses.

In this article, we have listed the best machine learning courses from Udemy and Coursera.

If you want to work in big tech companies like Amazon, Facebook, Google, Microsoft, IBM, then you need to have deep knowledge of Artificial Intelligence, Machine Learning, and Deep Learning.

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]]>TensorFlow Variables and Placeholders Tutorial With Example is today’s topic. TensorFlow is an open source machine learning framework developed by Google which can be used to the build neural networks and perform a variety of all machine learning tasks. TensorFlow works on data flow graphs where nodes are the mathematical operations, and the edges are the data […]

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]]>TensorFlow Variables and Placeholders Tutorial With Example is today’s topic. TensorFlow is an open source machine learning framework developed by Google which can be used to the build neural networks and perform a variety of all machine learning tasks. TensorFlow works on data flow graphs where nodes are the mathematical operations, and the edges are the data in the for tensors, hence the name Tensor-Flow.

A tensor is a central unit of data in TensorFlow. It consists of primitive values stored in the shape of a multidimensional array. The number of dimensions a tensor has is called its **rank**.

A rank 0 tensor is just a **scalar**. To keep things simple, we can say that a tensor in TensorFlow is instead a fancy name of an array and now we call dimension number as rank. One dimensional array or list is rank one tensor, and two-dimensional array or list is two rank tensor.

When we train the model, we need to assign some weights and biases throughout the session.

TensorFlow variables can hold the values of biases and weights throughout the session.

You need to one thing keep in mind thatTensorFlow variables need to be initialized.

In TensorFlow variables are of great use when we are training models. As constants, we have to call a constructor to initialize a variable; the initial value can be passed in as an argument.

Variables can easily be added to the computational graph by calling a constructor.

TensorFlow placeholders are initially empty and are used to feed in the actual training examples.

If we want to inject the data into a computation graph, we have to use the mechanism named as a placeholder. Placeholders are bound inside some expressions. The syntax of the placeholder is following.

placeholder(dtype, shape=None, name=None)

Placeholders allow us to not to provide the data in advance for operations and computation graphs, and the data can be added in runtime from external sources as we train the Machine Learning models.

TensorFlow Placeholder does need to declare as a float32 datatype within an optional shape parameter.

Okay, we have covered enough theory, let’s see some practical example of TensorFlow Variables and Placeholders in Python Jupyter Notebook.

You can find the guide about how to install TensorFlow on Mac on this article.

Also, you can find the basics of TesorFlow post.

Now, fire up the Jupyter Notebook and import the TensorFlow.

import tensorflow as tf

You can run the cell by keyboard shortcut **Ctrl + Enter.**

In the next cell, we will write the following code.

sess = tf.InteractiveSession()

The only difference with a regular **Session** is that an **InteractiveSession** installs itself as the default session on construction. We do not need to write that with** tf.Session() as sess** code whenever we need to perform some operations.

Once we run the above code, we do not need to start the session again for that Jupyter Notebook file.

Now, let’s define a random tensor using the following code.

tensorA = tf.random_uniform((4, 4), 1, 2) tensorA

Here, we have defined the **4*4 **matrix between the value 1 and 2. The values are random between 1 to 2.

When we try to display the tensorA, we will get the following output.

Here, you can see that the datatype of tensorA is **float32**.

Now, in the next step, we will define a TensorFlow variable called **tensor_var_A.**

tensor_var_A = tf.Variable(initial_value=tensorA)

Okay, now run the **tensor_var_A **variable.

sess.run(tensor_var_A)

You will get an error like below.

So, the error is saying that **FailedPreconditionError: Attempting to use uninitialized value Variable.**

That means, we need to first initialize the TensorFlow variable and then we can run that variable.

So, let’s do that first. Write the following code in the next cell.

init = tf.global_variables_initializer()

Run the above cell and then write the following code in the next cell.

sess.run(init)

Run the above cell, and all the variables are initialized. Now, we write that failed code again, and now you can see the 4*4 matrix.

sess.run(tensor_var_A)

See the output below.

Now, let’s create a TensorFlow Placeholder Example.

Define one placeholder using the following code in the next cell.

tfph = tf.placeholder(tf.float32, shape=(None, 5))

The above code creates a TensorFlow placeholder, and its datatype is float32, and here **None **is the placeholder’s initial value of data. As time goes and our machine learning mode starts training, the data is filled in the placeholder. But, at the starting point, it is None.

We can use another example of TensorFlow Placeholder, which is the following code.

a = tf.placeholder(tf.float32, name='a') b = tf.placeholder(tf.float32, name='b') c = tf.add(a, b, name='c') sess.run(c, feed_dict={a: 2.1, b: 1.9})

Here, we have defined two placeholders and then create the third node to add both placeholders and run the operation. Remember, we are using Interactive Session. The output is following.

So, this is how you can create TensorFlow Variables and Placeholders.

Finally, TensorFlow Variables and Placeholders Tutorial With Example is over.

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]]>In this article, we will see Tensorflow Basics Tutorial With Example | Getting Started With Tensorflow. Machine learning is the complex discipline. But implementing the machine learning models is far less daunting and difficult than it used to be, thanks to the machine learning frameworks—such as Google’s TensorFlow: that ease the process of acquiring data, training models, serving […]

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]]>In this article, we will see Tensorflow Basics Tutorial With Example | Getting Started With Tensorflow. Machine learning is the complex discipline. But implementing the machine learning models is far less daunting and difficult than it used to be, thanks to the machine learning frameworks—such as Google’s TensorFlow: that ease the process of acquiring data, training models, serving predictions, and future results.

TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together the slew of machine learning and deep learning (also known as neural networking) models and algorithms and makes them useful by way of the common metaphor. It uses Python to provide the convenient front-end API for building applications with the framework while executing those applications in high-performance C++.

For this example, I am using Jupyter Notebook to perform some Tensorflow practicals. So, if your notebook has not installed the Tensorflow library then you can install it using Anaconda Navigator and find the environment section of Anaconda Navigator. You will see something like this.

Here, if a tensorflow package is uninstalled on your machine, then you can install it from here.

Now, launch a Jupyter Notebook and create a new book and verify that you have installed the **tensorflow** using the following code in the first cell of Jupyter Notebook.

import tensorflow as tf tf.__version__

Run the cell using the Ctrl + Enter command and see the output.

Let’s define the Tensorflow constants using the following code. You need to write that code in the next cell.

app = tf.constant('app') type(app)

Here, I have defined the app tensorflow constant and also we have checked the type of that constant. Run the cell See the below output.

Let’s define one more tensorflow constant and see its datatype as well.

dividend = tf.constant('dividend') type(dividend)

See the output below.

Now, print the constant and see the output.

print(app)

See the output below.

See the datatype of the constant is String. The object is Tensor.

You can not run any operation in Tensorflow without using the sessions. If you try to add any integers or concat the strings, you need to start a session and then add the operational code and then run that operation. Otherwise, nothing will have happened. Let’s concat the above two strings we have defined earlier. See the following example.

with tf.Session() as sess: result = sess.run(app + dividend) print(result)

Here, we have used the **tf.Session() **function to start a session and perform the concat operation between two strings and store that output in the result variable and then print that variable. See the output.

Here, in the output **b **represents the byte literal.

Let’s perform the addition operation computation in Tensorflow. Write the following code in the next cell.

a = tf.constant(10) b = tf.constant(10) with tf.Session() as sess: result = sess.run(a + b) print(result)

Here, we have defined the two constants, which are integers and then we have performed the addition operation inside the tensorflow session and store the output in result variable and display that variable. See the following output.

We can create a Matrix using Tensorflow functions like, we have created using NumPy library.

We can create the matrix using **tf.fill() **method provided by the tensorflow framework. See the following example.

mat = tf.fill((5,5), 21) with tf.Session() as sess: op = sess.run(mat) op

See the output below.

In the above code, we have created the 5*5 matrix with the 21 values filled inside each element. Remember, we need to perform any operation inside the tensorflow sessions.

So these are some basics of Tensorflow.

Finally, Tensorflow Basics Tutorial | Getting Started With Tensorflow is over.

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]]>In this tutorial, I will show you How To Build Simple Model In Tensorflow. Tensorflow Framework is the popular framework to design a neural network in Machine Learning. Tensorflow is created at Google. It is an open source machine learning framework for everyone. TensorFlow is an open source library for high-performance numerical computation. Tensorflow has a flexible […]

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]]>In this tutorial, I will show you How To Build Simple Model In Tensorflow. Tensorflow Framework is the popular framework to design a neural network in Machine Learning. Tensorflow is created at Google. It is an open source machine learning framework for everyone. TensorFlow is an open source library for high-performance numerical computation. Tensorflow has a flexible architecture that allows easy deployment of calculation across a variety of platforms like CPUs, GPUs, TPUs, and from desktops to clusters of servers to mobile and edge devices. It is initially developed by researchers and engineers from the Google Brain team within Google’s AI organization, and it comes with strong support for machine learning and deep learning, and the flexible numerical computation core is used across many other scientific domains.

If we want to work with Tensorflow then first, we need to define the tensorflow graph. In this model, we will define two tensors or nodes, and then we add those nodes. Now, let’s dive into what is tensors.

TensorFlow, as the name indicates, is the framework to define and run computations involving tensors. The **tensor** is the generalization of vectors and matrices to potentially higher dimensions. Internally, TensorFlow represents tensors as n-dimensional arrays of base datatypes.

When writing a TensorFlow program, the primary object you manipulate and pass around is the **tf.Tensor**. The **tf.Tensor **object represents a partially defined computation that will eventually produce a value. TensorFlow programs work by first building a graph of **tf.Tensor** objects, detailing how each tensor is computed based on the other available tensors and then by running parts of this graph to achieve the desired results.

TensorFlow programs use the tensor data structure to represent all data only tensors are passed between operations in the computation graph. You can think of the TensorFlow tensor as an n-dimensional array or list.

For example, a scaler is a tensor, a vector is a tensor, and a matrix is a tensor. A tensor has a rank, a shape, and a static type so that a tensor can be represented as the multidimensional array of numbers.

Okay, now, if you do not know how to install and configure the Tensorflow on virtualenv then check out my post on how to install tensorflow on this blog.

Okay, now we will create a file called **tflow.py **and write the following code inside that file.

# app.py import tensorflow as tf import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' X = tf.placeholder(tf.int32, name='X') Y = tf.placeholder(tf.int32, name='Y') add = tf.add(X, Y, name='add') with tf.Session() as session: result = session.run(add, feed_dict={ X: [19, 1, 2], Y: [21, 4, 3] }) print(result)

In the above code, first, we have imported the **tensorflow **and **os module **from the Python library.

We have used the **os **module’s **environ attribute **to disable the logs which are unnecessary for this demo model. If you want that log in the output, then you can comment that statement.

Then we have created the computational graph with two nodes using **a placeholder **and defined their datatypes and names.

So, we have two tensors up to now which is X and Y.

Then we are adding those tensors using tensorflow’s add method and get the third node which is an **add node.**

If we want to execute operation in the Graph, we need to use sessions.

We can create a session in tensorflow using **with tf.Session as session **code.

Next step is that we have called the run method on the session, which takes the two arguments.

- add node
- data

We have used the dictionary data, which has two nodes, X and Y. So we will add those and generate a new node with the session.

Go to the terminal and run the following command.

python3 tflow.py

The output of the above code is following.

So, we have built a tensorflow model, which can add two nodes and gives the output node.

Here, the computation is to add the nodes and nothing complicated. But in real life application, there are lots of variables and iterables to go through and finally predict the future value.

Finally, How To Build Simple Model In Tensorflow Tutorial With Example is over.

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]]>In this tutorial, we will see How To Install Tensorflow on Mac. For this tutorial, you must have installed Python 3 in your mac machine. If not then go to python.org website and install version 3 of Python. Other then that, you need to have an editor like Visual Studio Code. You can always welcome with PyCharm IDE as well. […]

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]]>In this tutorial, we will see How To Install Tensorflow on Mac. For this tutorial, you must have installed **Python 3 **in your mac machine. If not then go to **python.org **website and install version 3 of Python. Other then that, you need to have an editor like Visual Studio Code. You can always welcome with PyCharm IDE as well. I am using VSCode and have installed Python extension.

I have installed version 3 of the Python. You can check it by going to the terminal and see the output by **python3 -v **command.

If you don’t have still Python 3 and other packages like Otherwise, install Python, the pip package manager, and Virtualenv then you can follow the below steps.

First, Install using the Homebrew package manager.

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Then add a global path. Modify and add the following line inside **.bash_profile **or** .zshrc **file.

export PATH="/usr/local/bin:/usr/local/sbin:$PATH"

The hit the one by one following command.

brew update brew install python # Python 3 sudo pip3 install -U virtualenv

I have already created a Virtual Environment, but you can also create by following steps.

Create a new virtual environment by choosing a Python interpreter and making a **./pythonenv** directory to hold it. Type the following command then.

virtualenv --system-site-packages -p python3 ./pythonenv

Go inside that folder.

cd pythonenv

Activate the virtual environment using a shell-specific command.

source bin/activate

If you have installed virtual environment perfectly then virtualenv is active, your shell prompt is prefixed with (**pythonenv**). See the below image.

Install packages within a virtual environment without affecting the host system setup. Start by upgrading **pip. **See the following command to upgrade pip.

pip install --upgrade pip

After that, you can see all the packages by typing the following command.

pip list

You can see that, I have already installed the **TensorFlow package.**

If your system has not tensorflow package then you can install it using the following command.

pip install tensorflow

Basically, package dependencies are already installed, if you have not the newer version of tensorflow then you can upgrade using the following command.

pip install --upgrade tensorflow

So, finally, the Tensorflow is installed on your machine.

Let’s check if it has installed or not.

Create a new folder inside the **pythonenv **folder called **tflow **and inside that, create a new file called **tflow.py **and add the following code inside it.

import tensorflow as tf import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' hello = tf.constant('Kudos, TensorFlow!') sess = tf.Session() print(sess.run(hello))

In the above code, we have imported the **tensorflow** as well as the **os** module.

If you don’t import the os module and use the **environ **function then it will give us a warning and avoid the warning, we have applied the setting export TF_CPP_MIN_LOG_LEVEL=2.

For more information, you can check out this StackOverflow link.

Finally, run the **tflow.py **file using the following command and see the output.

python3 tflow.py

If you have seen like this then congratulations!! You have successfully installed Tensorflow on your mac.

Finally, How To Install Tensorflow on Mac Tutorial From Scratch is over.

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]]>How To Prepare Dataset For Machine Learning in Python. Machine Learning is all about train your model based on current data to predict future values. So we need the proper amounts to train our model. So in real life, we do not always have the correct data to work with. If the data is not processed […]

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]]>**How To Prepare Dataset For Machine Learning in Python. ****Machine Learning** is all about train your model based on current data to predict future values. So we need the proper amounts to train our model. So in real life, we do not always have the correct data to work with. If the data is not processed correctly, then we need to prepare it and then start training our model. So in this post, we will see step by step to transform our initial data into Training and Test data. For this example, we use python libraries like **scikit learn, numpy, **and **pandas.**

We use the **Python** programming language to create a perfect dataset. For preparing a dataset, we need to perform the following steps.

**Get the dataset and i****mport the libraries.****Handle missing data.****Encode categorical data.****Splitting the dataset into the Training set and Test set.****Feature Scaling, if all the columns are not scaled correctly.**

So, we will be all the steps on the dataset one by one and prepare the final dataset on which we can apply regression and different algorithms.

Okay, now we are going to use Indian Liver Patient data. So we first prepare the complete dataset for this kind of data. I am putting the link here to download the data. Remember, this is not a real dataset; this is just the demo dataset. It looks like the actual dataset.

**Download File:** patientData

Now, we need to create a project directory. So let us build using the following command.

mkdir predata

Now go into the directory.

cd predata

We need to move the **CSV** file inside this folder.

Now, open the **Anaconda Navigator** software. If you are new to Anaconda, then please check out this **How To Get Started With Machine Learning In Python.** After opening Navigator, you can see a screen like below.

Now, launch the **Spyder **application and navigate to your project folder. You can see, we have already moved the **patientData.csv **file so that you can see that file over there.

Okay, now we need to create one Python file called **datapre.py **and start importing the mathematical libraries.

Write the following code inside the **datapre.py **file. So, your file looks like this. Remember, we are using **Python 3**

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jul 25 18:52:15 2018 @author: your name """ import numpy as np import matplotlib.pyplot as plt import pandas as pd

Now, select code of three import statements and hit the **command + enter **and you can see at the right side down; the code is running successfully.

That means we have successfully imported the libraries. If you found any error then possibly the **numpy, pandas, or matplotlib **library is missing. So you need to install that, and that is it.

In real-time, missing the data happens quite a lot. If you are finding the real-time data set like for the patients, then there is always missing the data. To train the model correctly, we need to fill the data somehow. Otherwise, the model will mispredict the values. Luckily libraries are already available to do that; we need to use the proper function to do that. Now, in our dataset, there is missing data, so we need to fill the data with either mean values or to use some other algorithms. In this example, we are using MEAN to supply the values. So let us do that.

But first, let us divide the dataset into our X and Y axis.

Okay, now write the following code after importing the libraries.

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jul 25 18:52:15 2018 @author: krunal """ import numpy as np import matplotlib.pyplot as plt import pandas as pd dataset = pd.read_csv('patientData.csv')

Now, select the following line and hit the **command + enter.**

dataset = pd.read_csv('patientData.csv')

Okay, so we have included our initial dataset, and you can see here.

Here, you can see that if the value is empty, then the **nan **is displaying. So we need to change it with the **MEAN** values. So let us do that.

Write the following code.

X = dataset.iloc[:, :-1].values Y = dataset.iloc[:, 3].values

So, here in the **X**, we have selected the first four columns and leave the fifth column. It will be our **Y**.

Remember, indexes are starting from 0. So -1 means the last column. So we are selecting all the columns except for the last column.

For Y, we have explicitly selected the fourth column, and the index is 3.

Okay, now we need to handle the missing data. We will use a library **Scikit learn.**

Write the following code.

... from sklearn.preprocessing import Imputer imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0) imputer = imputer.fit(X[:, 1:3]) X[:, 1:3] = imputer.transform(X[:, 1:3])

So, here, we have to use the Imputer module to use the strategy ‘mean’ and fill the missing values with the mean values. Run the above lines and type the **X** in the Console. You can see something like below. Here, columns 1 and 2 have missing values, but we have written **1:3** because the upper bound is excluded; that is why we have taken 1 and 3, and it is working fine. Finally, transform the whole column values which have NaN values, and now we have got the filled values.

Here, you can see that the mean values of that particular column fill the missing values.

So, we have handled the missing data. Now, head over to the next step.

In our dataset, there are two categorical columns.

**Gender**-
**Liver Disease**

So, we need to encode these two columns of data.

# Encode Categorical Data from sklearn.preprocessing import LabelEncoder, OneHotEncoder labelencoder_X = LabelEncoder() X[:, 0] = labelencoder_X.fit_transform(X[:, 0]) onehotencoder = OneHotEncoder(categorical_features = [0]) X = onehotencoder.fit_transform(X).toarray() labelencoder_Y = LabelEncoder() Y = labelencoder_Y.fit_transform(Y)

Here, we have encoded the values of the first column. Now, here, we have only two cases for the first column, and that is **Female **and **Male. **Now, after the transform, the values are **1** for Female, and **0** for Male.

Run the above line and see the changes in categorical data. So, here for Female, it is **1 **and Male is** 0. **It has created one more column and replaces **Male** and **Female** according to 1 and 0. That is why it becomes from 3 columns to 4 columns.

Now, generally, we split the data with a ratio of 70% for the Training Data and 30% to test data. For our example, we divided into 80% for training data and 20% for the test data.

Write the following code inside the **Spyder**.

# Split the data between the Training Data and Test Data from sklearn.model_selection import train_test_split X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2 ,random_state = 0)

Run the code, and you can get the **four** more variables. So, we have a total of **seven** variables.

So, here, we have split the both Axis X and Y into X_train and X_test

Y-axis becomes Y_train and Y_test.

So, you have 80% data on the X_train and Y_train and 20% data on the X_test and Y_test.

In a general scenario, machine learning is based on Euclidean Distance. Here for the column **Albumin** and **Age **column has an entirely different range of values. So we need to convert those values and make it under the range of values. That is why this is called feature scaling. We need to scale the values for the **Age** column. So let us scale the X_train and X_test.

# Feature Scaling from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test)

Here, we do not need for Y because it is already scaled. Now run the above code and hit the following command.

Here, we can see that all the values are appropriately scaled and also you can check the **X_test **variable as well.

So, we have successfully cleared and prepared the data.

Here is the final code of our **datapre.py.**

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jul 25 18:52:15 2018 @author: krunal """ # Importing Libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing Dataset dataset = pd.read_csv('patientData.csv') X = dataset.iloc[:, :-1].values Y = dataset.iloc[:, 3].values # Handing Missing Dataset from sklearn.preprocessing import Imputer imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0) imputer = imputer.fit(X[:, 1:3]) X[:, 1:3] = imputer.transform(X[:, 1:3]) # Encode Categorical Data from sklearn.preprocessing import LabelEncoder, OneHotEncoder labelencoder_X = LabelEncoder() X[:, 0] = labelencoder_X.fit_transform(X[:, 0]) onehotencoder = OneHotEncoder(categorical_features = [0]) X = onehotencoder.fit_transform(X).toarray() labelencoder_Y = LabelEncoder() Y = labelencoder_Y.fit_transform(Y) # Split the data between the Training Data and Test Data from sklearn.model_selection import train_test_split X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2 ,random_state = 0) # Feature Scaling from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test)

So, we have successfully **Prepare Dataset For Machine Learning in Python.**

Machine Learning has very complex computations. It depends on how you get the data and in which condition. Based on the condition of the data, you will start to preprocess the data and split the data into the Train and Test model.

Finally, **Prepare Dataset For Machine Learning in Python **is over. Thanks for taking it.

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]]>Hello Devs, Today I will guide you How To Get Started With Machine Learning In Python. In this tutorial, you will learn to install the Python3, Jupyter Notebook, Scikit Learn, and other useful Python libraries step by step. Getting Started With Machine Learning In Python 2019 Step 1: What is machine learning: Overview. Step 2: Machine Learning […]

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]]>Hello Devs, Today I will guide you **How To Get Started With Machine Learning In Python. **In this tutorial, you will learn to install the Python3, Jupyter Notebook, Scikit Learn, and other useful Python libraries step by step.

- Step 1: What is machine learning: Overview.
- Step 2: Machine Learning Workflow.
- Step 3: Install the Anaconda dependencies.
- Step 4: Or you can install the individual libraries.
- Step 5: Getting Started With Python Jupyter Notebook.
- Step 6: Learn the markdown editor.
- Step 7: Write the code in Markdown editor.
- Step 8: Practice Machine Learning with Small In-Memory Datasets.
- Step 9: Build a Machine Learning Portfolio.
- Step 10: Machine Learning For Money or Apply the job for machine learning.

Also if you do not want to install all the packages one by one, then you can also install only one software called Anaconda. It will install Python3 and other I guess 150 packages that will help you to build your development environment very smoothly and you do not even think about installing all the dependencies.

**Machine Learning** is the application of **Artificial Intelligence (AI)** that provides systems the ability to learn and improve from the previous experience without being explicitly programmed automatically.

**Machine learning focuses on the development of computer programs** that can access the data and use it to determine themselves for future making decisions.

The process of learning begins with the measurements or observations of data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the models that we can provide.

The primary focus is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

**Machine Learning** is the art of computing, in which we need first to prepare the data, if not in well-formed condition and then use that accurate data to train the models.

The models are built on different algorithms, which the programmers need to write in programming language and our case it is Python.

Based on the previous output of the data and algorithmic logic, the Machine starts to learn themselves, and the accuracy of the future values is accurate more and more based on the vast amount of data that we have collected.

The necessary steps are the following.

- We need to ask our selves, what are the main problems that can solve machine learning.
- After identifying the problem, we need to start preparing for the data. If the data is not well organized than we need to organize it very well so that we can train the model based on accurate data.
- The next step is to select an algorithm that can solve the problem. There are so many algorithms out there to work with, but we need to identify the perfect algorithm to meet our requirements.
- After selecting the Algorithm, we need to train the model based on that.
- The last step is to test that model to predict future values.

The first thing we will do is to install Python 3 and other machine learning libraries that we can use to develop and train the models.

I am using Mac, but if you are the windows user, then you can also follow up on this tutorial although installation instructions might be different on the Windows platform.

You can easily google for that, not a big deal. Once you install all the libraries, you are ready to go with this tutorial.

There are lots of ways, you can install all the dependencies, but I am showing two methods from them

- Install the Anaconda package that covers all the installation libraries. You need to launch the Navigator and open the Jupyter Notebook, and you are good to go.
- We will install all the dependencies one by one on Mac.

Go to the downloads directory https://www.anaconda.com/download/#macos

For Windows users, choose your platform’s executables and download the package and install it on your machine.

During the installation, you have an option that tells us that, whether or not you need to install the Visual Studio Code or not. Guys, if you have not downloaded already then, please download and install it on your machine.

After installing, we need to open the application from **Launchpad **called **Anaconda-Navigator.**

I have already installed VSCode, so do not need to install it from here. But almost our interface looks same.

It will start a server at default PORT: 8888. Also, it will open the browser window and take to this local URL: http://localhost:8888/tree, Please do not close the terminal that opens with **Jupyter Notebook** otherwise our local server will stop working and we will not be able to run the **Jupyter Notebook**. So we all set up to start Machine Learning.

First, check your **Python version **using the following command.

python

On my MacBook, I have preinstalled **Python**, and its version is 2.7. But we need a different version, and that is **Python 3. **Do not uninstall the old version because otherwise, some of the mac applications will be stopped. Instead, pull another version of Python by the following command.

brew install python3

Okay, so it will install the Python3, and now you can check it using the following command.

python3

Now, you can see that **Python3 **is installed on our machine.

Finally, install the following packages using **Pip. **It is the Python, package manager.

python3 -m pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose

So it will install all the dependencies. Now, go to your terminal and hit the following command to start the Jupyter server.

jupyter notebook

Now, switch to the browser and type this URL: **http://localhost:8888/tree**

You can see that our Jupyter Notebook server is running correctly.

So, now we need to create one notebook.

Go into your project folder, and in my case it is **AnacondaProjects. **It is empty so press **new **drop-down in the upper right corner and select **python3. **You will see something like below.

I have renamed the title to **FirstML. **Yours would be untitled. My Browser URL is like this: http://localhost:8888/notebooks/AnacondaProjects/FirstML.ipynb Your URL will be different based on your folder structure.

If you have used Github before then, I guess you are familiar with Markdown Language.

**Markdown** is a lightweight markup language with plain text formatting syntax. It is designed so that it can be converted to HTML and many other formats using a tool by the same name.

In Github, we are writing this language inside **readme.md **file. So that the project can be described very well and it can be documented very well.

In **Jupyter Notebook**, when we need to write something that is not code, we can change it to via drop-down and select the markdown instead and write some text in markdown language.

For example, If we need to write H1 text in markdown, then we can write it using the following syntax.

# Hello Machine Learning

So it will print as an **H1** text. Same if we define two hashes then **H2** text and go on.

We can write the code and run the code like the following.

First, if your dropdown says markdown then change it to code and then write the code. After that, to run that code type shortcut **shift + enter(return)**

You will get an output like the above image. Great!!. So we have set up all correctly.

We will start working with data in the next tutorial. **How To Get Started With Machine Learning In Python **tutorial is over.

This is a just basic overview of machine learning and installing the dependencies related to machine learning. Stay tuned for the next **machine learning tutorial**.

Finally, How To Get Started With Machine Learning In Python 2019 is over.

How To Prepare Dataset For Machine Learning

How To Install Tensorflow on Mac

TensorFlow Variables and Placeholders

How To Build Simple Model In Tensorflow

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