To learn machine learning, we will use the Python programming language in this tutorial. So, in order to use Python for machine learning, we need to install it in our computer system with compatible IDEs (Integrated Development Environment).

In this blog, we will learn to install Python and an IDE with the help of Anaconda distribution.

Anaconda distribution is a free and open-source platform for Python/R programming languages. It can be easily installed on any OS such as Windows, Linux, and MAC OS. It provides more than 1500 Python/R data science packages which are suitable for developing machine learning and deep learning models.

Anaconda distribution provides installation of Python with various IDE’s such as Jupyter Notebook, Spyder, Anaconda prompt, etc. Hence it is a very convenient packaged solution which you can easily download and install in your computer. It will automatically install Python and some basic IDEs and libraries with it.

Below some steps are given to show the downloading and installing process of Anaconda and IDE:

Step-1: Download Anaconda Python:

• To download Anaconda in your system, firstly, open your favorite browser and type Download Anaconda Python, and then click on the first link as given in the below image. Alternatively, you can directly download it by clicking on this link, https://www.anaconda.com/distribution/#download-section.

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• After clicking on the first link, you will reach to download page of Anaconda, as shown in the below image:

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• Since, Anaconda is available for Windows, Linux, and Mac OS, hence, you can download it as per your OS type by clicking on available options shown in below image. It will provide you Python 2.7 and Python 3.7 versions, but the latest version is 3.7, hence we will download Python 3.7 version. After clicking on the download option, it will start downloading on your computer.

Note: In this topic, we are downloading Anaconda for Windows you can choose it as per your OS.

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Step- 2: Install Anaconda Python (Python 3.7 version):

Once the downloading process gets completed, go to downloads → double click on the “.exe” file (Anaconda3-2019.03-Windows-x86_64.exe) of Anaconda. It will open a setup window for Anaconda installations as given in below image, then click on Next.

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• It will open a License agreement window click on “I Agree” option and move further.

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• In the next window, you will get two options for installations as given in the below image. Select the first option (Just me) and click on Next.

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• Now you will get a window for installing location, here, you can leave it as default or change it by browsing a location, and then click on Next. Consider the below image:

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• Now select the second option, and click on install.

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• Once the installation gets complete, click on Next.

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• Now installation is completed, tick the checkbox if you want to learn more about Anaconda and Anaconda cloud. Click on Finish to end the process.

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Note: Here, we will use the Spyder IDE to run Python programs.

Step- 3: Open Anaconda Navigator

• After successful installation of Anaconda, use Anaconda navigator to launch a Python IDE such as Spyder and Jupyter Notebook.

• To open Anaconda Navigator, click on window Key and search for Anaconda navigator, and click on it. Consider the below image:

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• After opening the navigator, launch the Spyder IDE by clicking on the Launch button given below the Spyder. It will install the Spyder IDE in your system.

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Run your Python program in Spyder IDE.

• Open Spyder IDE, it will look like the below image:

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• Write your first program, and save it using the .py extension.

• Run the program using the triangle Run button.

• You can check the program’s output on console pane at the bottom right side.

Step- 4: Close the Spyder IDE.

So, this brings us to the end of blog. This Tecklearn ‘Installing Anaconda and Python’ blog helps you with commonly asked questions if you are looking out for a job in Machine Learning. If you wish to learn Machine Learning and build a career in Data Science or Machine Learning domain, then check out our interactive, Machine Learning Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

Machine Learning

Machine Learning Training

About the Course

Tecklearn’s Machine Learning training will help you develop the skills and knowledge required for a career as a Machine Learning Engineer. It helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning Certification Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. With these key concepts, you will be well prepared for the role of Machine Learning (ML) engineer. In addition, it is one of the most immersive Machine Learning online courses, which includes hands-on projects

Why Should you take Machine Learning Training?

• The average machine learning salary, according to Indeed’s research, is approximately $146,085 (an astounding 344% increase since 2015). The average machine learning engineer salary far outpaced other technology jobs on the list.

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• The Machine Learning market is expected to reach USD $8.81 Billion by 2022, at a growth rate of 44.1-percent, indicating the increased adoption of Machine Learning among companies. By 2020, the demand for Machine Learning engineers is expected to grow by 60-percent.

What you will Learn in this Course?

Introduction to Machine Learning

• Need of Machine Learning

• Types of Machine Learning – Supervised, Unsupervised and Reinforcement Learning

• Applications of Machine Learning

Concept of Supervised Learning and Linear Regression

• Concept of Supervised learning

• Types of Supervised learning: Classification and Regression

• Overview of Regression

• Types of Regression: Simple Linear Regression and Multiple Linear Regression

• Assumptions in Linear Regression and Mathematical Concepts behind Linear Regression

• Hands On

Concept of Classification and Logistic Regression

• Overview of the Concept of Classification

• Comparison of Linear regression with Logistic regression

• Mathematics behind Logistic Regression: Detailed Formulas and Functions

• Concept of Confusion matrix and Accuracy Measurement

• True positives rate, False positives rate

• Threshold evaluation with ROCR

• Hands on

Concept of Decision Trees and Random Forest

• Overview of Tree Based Classification

• Concept of Decision trees, Impurity function and Entropy

• Concept of Impurity function and Information gain for the right split of node and

• Concept of Gini index and right split of node using Gini Index

• Overfitting and Pruning Techniques

• Stages of Pruning: Pre-Pruning, Post Pruning and cost-complexity pruning

• Introduction to ensemble techniques and Concept of Bagging

• Concept of random forests

• Evaluation of Correct number of trees in a random forest

• Hands on

Naive Bayes and Support Vector Machine

• Introduction to probabilistic classifiers

• Understanding Naive Bayes Theorem and mathematics behind the Bayes theorem

• Concept of Support vector machines (SVM)

• Mathematics behind SVM and Kernel functions in SVM

• Hands on

Concept of Unsupervised Learning

• Overview of Unsupervised Learning

• Types of Unsupervised Learning: Dimensionality Reduction and Clustering

• Types of Clustering

• Concept of K-Means Clustering

• Mathematics behind K-Means Clustering

• Concept of Dimensionality Reduction using Principal Component Analysis (PCA)

• Hands on

Natural Language Processing and Text Mining Concepts

• Overview of Concept of Natural Language Processing (NLP)

• Concepts of Text mining with Importance and applications of text mining

• Working of NLP with text mining

• Reading and Writing to word files and OS modules

• Text mining using Natural Language Toolkit (NLTK) environment: Cleaning of Text, Pre-Processing of Text and Text classification

• Hands on

Introduction to Deep Learning

• Overview of Deep Learning with neural networks

• Biological neural network Versus Artificial neural network (ANN)

• Concept of Perceptron learning algorithm

• Deep Learning frameworks and Tensor Flow constants

• Hands on

Time Series Analysis

• Concept of Time series analysis, its techniques and applications

• Time series components

• Concepts of Moving average and smoothing techniques such as exponential smoothing

• Univariate time series models

• Multivariate time series analysis and the ARIMA model

• Time series in Python

• Sentiment analysis using Python (Twitter sentiment analysis Use Case) and Text analysis

• Hands on

Got a question for us? Please mention it in the comments section and we will get back to you.

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