Machine Learning Using Python

Are you keen to know more? Come and join us to understand how machine learning and artificial intelligence disciplinaries have adapted Python programming.

The Equation Between Machine Learning and Python

These days, buzzwords like Artificial Intelligence, Machine Learning, Deep Learning, and Neural Networks have taken the IT industry by storm.

Let’s see their definition (source: Wikipedia):

Artificial Intelligence(AI) - the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Machine learning(ML) – is a branch of  AI; It is the scientific study of algorithms and statistical models that computer systems use to perform above tasks without using explicit instructions, relying on patterns and inference instead.

Deep Learning - (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.

Artificial neural networks (ANN) - (connectionist systems) are computing systems that are inspired by, but not identical to, biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules.

Businesses have a vast amount of data that they need to analyze for achieving business goals. ML is all about creating algorithms and systems to analyze such data. Some of these common algorithms include linear progression, logistic progression, k-means clustering, etc.

Python Anaconda supported by all commonly used operating systems like Windows, Linux, MAC provides scikit-learn, matplotlib, pandas, and NumPy library to implement machine learning algorithms. These rich features make Python as one of the most common backbones of machine learning.

The combination of simplicity, shorter development time, and consistent syntax makes Python a juggernaut among Machine Learning experts.

 

Why Learn Python Machine Learning?

Currently, machine learning is one of the most exciting and promising intellectual fields with its applications ranging from e-commerce to healthcare.

Python offers all the skillsets that are required for machine learning or AI projects – stability, flexibility and a large number of tools.

Few machine learning trends by 2020 are:

A)New Approaches to Cybersecurity

Machine learning deployed by cybersecurity firms can increase the level of security.

B)Robotic Process Automation Will Rule the World

Finance, health, and manufacturing industries are using machine learning to deploy robotic process automation where intelligent drones and robots make the task easier.

C)Improved IT Operations

Machine learning helps IT operations teams to capture, refine data, to obtain the root cause of problems and create intelligent business insights to make the companies successful.

D)Transparency in Decision-Making

Machine learning with the help of predictive models brings transparency in decision-making in the field of retail, medicine, healthcare, and logistics.

Machine learning is a boon to the world. As more and more organizations are depending on it, demand for certified and skilled professionals also goes up. The average salary of a machine learning engineer with Python skills is ₹719,646 in India and $111,490 in the USA(source: indeed.com).

So, why wait? Join the best training institute to get hands-on experience in Machine Learning using Python.

From our Python Machine Learning tutorial, you will understand two main components:

a) you will be learning about the purpose and applications of Machine Learning.

b) you will gain an overview of its topics such as supervised vs unsupervised learning, model evaluation, and algorithms - linear regression, K Nearest Neighbors, decision trees, random forest, Support Vector Machines (SVM), flat clustering, hierarchical clustering, Time Series, Naïve Bayes, Q-Learning and neural networks.

We will also discuss how these algorithms are logically meant to work, diving into the inner workings of each of these algorithms, and apply these algorithms in coding using real-world data sets along with Python libraries.

Less Amount of Code

Algorithms are essentials of machine learning. Python makes it simpler for developers as it comes with the potential of implementing the same logic with as little code as required in other OOP languages. Python’s integrated approach lets developers check code methodology and to be more productive from development to deployment and maintenance.

Platform Independence

Many companies use their own machines containing powerful GPUs to train their machine learning models. Python being platform-independent helps in shifting data cost-effectively from one machine to another without making changes to the actual code.

Wide Range of Applications

ML is expanding its applications to real-world scenarios like emotion analysis, error detection, weather forecasting, stock market analysis, speech recognition, fraud detection, customer segmentation.

Flexibility

Developers can select programming styles for several types of problems, can combine Python with other languages to get the desired results, and do not require recompiling source code.

Great Support

Python is an open-source, multi-purpose language and its development is continuously supported by a great community. It offers a great number of resources like modules, packages, toolkits, and libraries that enable machine learning engineers to constantly improve the language.

Check Your Python Machine Learning Skills

How much do you know about Python used in Machine Learning?

Main Modules of Machine Learning using Python Course

Our course covers Python libraries and machine learning concepts in detail.

Machine Learning Approach

  • Overview of Data Science & Life Cycle
  • List the Tools used in Data Science
  • State what role Big Data and Hadoop, Python, R, and Machine Learning play in Data Science
  • Machine Learning Modelling Flow
  • How to treat Data in ML 
  • Types of Machine Learning 
  • Performance Measures 
  • Bias-Variance, Trade-Off 
  • Overfitting & Underfitting
  • Loading different types of dataset in Python
  • Arranging the data
  • Plotting the graphs

Understanding the Dataset

  • Discuss Data Acquisition techniques
  • List the different types of Data
  • Evaluate Input Data
  • Explain the Data Wrangling techniques
  • Data visualization
  • Discuss Data Exploration

How it Works?

  • Importance of Algorithms - Various
  • Simple approaches to Prediction, Predict Algorithms
  • Maxima and Minima, Cost Function, Learning Rate
  • Optimization Techniques
  • Identify the problem type and learning model
  • Model Persistence and Evaluation - Steps in Model Building, Sample the data
  • What is K?
  • Train/Test Data, Validation
  • Model Building, Find the accuracy
  • Rules
  • Iteration
  • Deploy the model

Python Revision

  • Python installation, Anaconda
  • Data Types, Operators
  • strings, arrays, functions, Lambda
  • Classes, objects
  • data structures, multithreading
  • RegEx, exception handling, file handling
  • Necessary Machine Learning Python libraries - (numpy, Pandas, scikit learn, matplotlib)
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories

Predictive Modelling

  • Supervised Learning Model Considerations
  • Supervised Learning Models - Linear Regression, Logistic Regression
  • Unsupervised Learning Models
  • ANOVA
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA
  • CART
  • Splitting Criterion
  • Pruning 
  • Ensemble Techniques - Bagging, Boosting, PCA (Principal Component Analysis)

Regression & Classification Techniques

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Linear Regression
  • Support Vector Regression
  • Decision Tree Regression
  • Random Forest Regression
  • Gaussian Mixture Models
  • Evaluating Regression Model Performance
  • K-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naïve Bayes Classification
  • Decision Tree Classification
  • Random Forest Classification
  • Evaluating Classification Model Performance

Unsupervised Learning

  • Define Unsupervised Learning
  • Discuss Cluster and Clustering with Example
  • Data Points, Grouping Data Points, Manual Profiling, Horizontal & Vertical Slicing
  • How to do optimal clustering
  • Clustering Algorithm
  • K - means Clustering
  • K-mini Batch Clustering
  • C - means Clustering
  • Hierarchical Clustering
  • Exclusive Clustering, Overlapping Clustering
  • Graphical Example
  • Different types of Distances: 1.Manhattan, 2.Euclidean, 3.Consine Similarity
  • Clustering Algorithm in Mahout
  • Probabilistic Clustering

Association Rule Learning

  • What are Association Rules?
  • Association Rule Parameters
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How Do Recommendation Engines work?
  • Collaborative Filtering
  • Content Based Filtering
  • Hands-On:
    Apriori Algorithm
    Market Basket Analysis
  • Explain Time Series Analysis (TSA)
  • Discuss the need of TSA
  • Forecast the time series model
  • White Noise
  • AR model
  • MA model

Reinforcement Learning

  • What is Reinforcement Learning
  • Why Reinforcement Learning
  • Elements of Reinforcement Learning
  • Exploration vs Exploitation dilemma
  • Epsilon Greedy Algorithm
  • Markov Decision Process (MDP)
  • Q values and V values
  • Q – Learning
  • α values
  • Hands-On:
    Calculating Reward
    Discounted Reward
    Calculating Optimal quantities
    Implementing Q Learning
    Setting up an Optimal Action

Natural Language Processing with Scikit Learn

  • NLP Overview
  • NLP Applications
  • NLP Libraries-Scikit
  • Extraction Considerations
  • Scikit Learn-Model Training and Grid Search
  • Assignment 01: Analyze a given spam collection dataset
  • Assignment 02: Analyze the sentiment dataset using NLP

Deep Learning

  • Basics/what is Deep Learning
  • Artificial Intelligence & Neural Networks Overview
  • Introduction to Neural Networks
  • Multi-layered Neural Networks
  • Regularization techniques (L1, l2)
  • CNN: Convolutional Neural Networks
  • LSTM: Long Short Term Memory
  • Dimension Reduction Techniques
  • Linear Discriminant Analysis (LDA)
  • Chatbots

Version Control using Git and Interactive Data Products

  • Need and Importance of Version Control
  • Setting up git and github accounts on local machine
  • Creating and uploading GitHub Repos
  • Push and pull requests with GitHub App
  • Merging and forking projects
  • Introduction to Bokeh charts and plotting
  • Examples of static and interactive data products
  • Discover ways to encode logic and develop agents that plan actions in an environment
  • Q-Learning and Policy gradient

Still Wondering? Evaluate us Before You Enroll.

Ask for a free demo class to know more about Python for Machine Learning.

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Why Choose Online Idea Lab? 

Explore Machine Learning Algorithms

In our Python Machine Learning course, you will work on various algorithms which in turn make you strong in mathematics & statistics.

Opens Doors to Data Science Career

Upon completion of our course, you will master Machine Learning & AI-related topics and can excel in data science interviews.

Gain Hands-On Experience

You will learn Python machine learning libraries, algorithms, and implementation techniques through our practical labs, use cases, & scenario-based examples.

Industry Expert Trainers

Our trainers are machine learning experts who will guide you step-by-step with detailed explanations, live demos, and resolving your queries. 

Be Industry-Ready

Becoming an expert in Python machine learning will make you stand out in the crowd and you will be able to contribute in business and revenue growth.

Talk to trainer

Talk directly with our trainer to get detailed information about this course.

AI and Machine Learning will be a foundational tool for creating social good as well as business success.

Mark Hurd

co-CEO, Oracle Corporation

Testimonials

We are always keen to know what our students have to say about their experience at Online Idea Lab. Read below and know what they have shared.

I am totally happy for the time and money I invested by taking this Machine Learning using Python course. I had a basic knowledge of Python coding. But this training took my expertise to the next level.

Saeed Muneer, Software Programmer

I am from the education industry and I teach machine learning to my students. To get my hands on Python programming, I joined their course and it was an amazing experience. I can impart this extra knowledge to my students with confidence now.

Hari Krishna, Professor

Overall the course is great and the instructor is awesome. Machine learning is fascinating and I now feel like I have a good foundation. Highly recommend this as a starting point for anyone wishing to be an ML programmer or data scientist. 

Hugo Tim, Software Programmer

Introduction to machine learning with Python course fills gaps in your calculus/linear algebra understanding.  It gives a grand picture of how ML works along with Python programming. I strongly recommend them.

Lucy Bakers, Data Engineer

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Few Interesting Facts and Stats

  • According to information obtained in April 2019 by PYPL, Python continues to be the most popular programming language and Python grew the most in the last 5 years (17.6%).
  • Guido Van Rossum, a Dutch programmer, developed Python in 1991.
  • The C variant of Python is known as CPython, The Java variant is known as Jython.
  • Few popular machine learning libraries are Numpy, Scikit-learn, seaborn, matplotlib, scipy, simpy, pandas, keras, etc.
  • Few machine learning algorithms are - KNN, linear regression, SVM, K-Means, decision tree etc.
  • In 2019 as per Stackoverflow Developer Survey, Python moved to third place on the list of most in-demand IT skills.

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