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Machine Learning Certification Training using Python ( L054 )

4.5 + (25,859) Students Ratings

Machine Learning using Python course at IQ Training’s will help you to gain knowledge in various machine learning algorithms such as regression, decision trees, clustering, random forest, Naïve Bayes and Q-Learning Algorithms. This course focuses on concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Upon completion of this course, you will be able to create and implement real-world projects on Media, Healthcare, Social Media, Aviation, chatbot creation, etc.

Course Price :

₹20,695
₹22,994
10%
off
Available

Live Instructor

Self Paced

Think Bigger Advantage

Live Online Classes

All our Classes are Live Instrucotor led online sessions. You can attend at the comfort of your place and Login to our Classes.

LMS (Learning Management System)

LMS will help you to organize your all training material, session videos and review at later date. You can access LMS anytime and review your completed classes. If you miss any class, then you can review the missed class in LMS.

Flexible Schedule

For some reasons, you can not attend the Classes, we can enroll you in the next possible classes. we assure flexibility in class schdules.

Lifetime Access to Learning Platform

You will get Lifetime free access to LMS(Learning Mangement System) You can access all Videos, class room assignments, quizzes, Projects for Life time. You will also get free video sessions for Life time.

Highest Completion Rate

We have the highest course completion rate in the Industry. If you miss a class, you can opt for the missed class in different batch. We assure you the best training possible for you to succeed.

Certificate of Completion

We provide you the Industry recognized Certification of Course completion This certificate will sometimes helps you to get reimbursement of training expenses by your company.

Training Scheule
Batch Start Date Days of Training Weekday/ Weekend Timings
28-Mar-2020 Available SAT & SUN (6 WEEKS) Weekend Batch 11:00 AM - 02:00 PM (EST)
 
 
 
 
 

Course Curriculum

Goal: To give an introduction to data science. To make understand how Data Science helps to analyze large and unstructured data by using different tools. 

Objectives: At the end of this Data Science Introduction Module, you should be able to:

  • Define Data Science
  • History of Data Science
  • Understand the Data Scientist role.
  • Illustration of Data Science life cycle.  
  • Discussion on Data science tools 
  • Understanding the role of Big Data and Hadoop In Data Science. 
  • State what role does R and Machine Learning play in Data Science.

Topics:

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • The Business Intelligence vs Data Science
  • The Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Python

Goal:  Learn how to extract data with the available sources, arrange the data in a structured form, analyze the data and also learn how to represent data in a graphical format. 

Objectives: At the end, you will

  • Learn techniques of Data Acquisition.
  • Listing out different types of Data
  • Evaluation of Input Data
  • Explain the Data Wrangling techniques
  • Discuss Data Exploration

 

Topics:

  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Data Visualization

Hands-On/Demo:

  • Loading different types of the dataset in Python
  • Arranging the data
  • Plotting the graphs

Goal: This module helps you to understand the concept of Machine Learning and its types. 

 

Objective: At the end of this Machine Learning with Python Introduction module, you will be able to 

  • Essential Python Revision
  • Discuss Machine Learning Python libraries
  • Define Machine Learning
  • Understand Use cases of Machine Learning
  • List out Machine Learning categories.
  • Illustrate Supervised Learning Algorithms
  • Identify and recognize algorithms of machine learning.
  • Get comprehensive Knowledge on various elements of machine learning algorithms like parameters, hyper parameters, loss function and optimization.

Topics:

  • Python Revision (numpy, Pandas, scikit learn, matplotlib)
  • What is Machine Learning?
  • Use-Cases of Machine Learning 
  • Process of Machine Learning Categories
  • Linear regression
  • Gradient descent

 

Hands On:

  • Linear Regression – Using Boston Dataset

Goal: This module helps you to understand the techniques of Supervised Learning. You will also learn how to implement them.

Objective: At the end, you will be able to

  • Understand What is Supervised Learning?
  • Illustrate Logistic Regression
  • Define Classification
  • Discuss different Types of Classifiers such as Decision Tree and Random Forest

 

Topics:

  • What is Classification and its use cases?
  • What is Decision Tree?
  • Decision Tree Induction Algorithm
  • Creation of Decision Tree
  • Confusion Matrix
  • What is the Random Forest?

 

Hands On:

  • Implementing Logistic regression, Decision tree, Random forest

Goal: This module explains to you the impact of dimensions within data. Using PCA and compressing dimensions you will understand how to perform factor analysis. You will learn how to develop LDA models.  

 

Objective: At the end of this Dimensionality Reduction module, you should be able to:

  • Define the importance of Dimensions
  • Explore PCA and its implementation
  • Understand LDA and its implementation

 

Topics:

  • Dimensionality Introduction
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA

 

Hands On:

  • PCA
  • Scaling

Goal: This module helps you to learn the techniques of Supervised Learning. You will also learn how to implement these techniques.

Objective: At the end of this, you will be able to

  • Understand What is Naïve Bayes Classifier
  • Learn How Naïve Bayes Classifier works?
  • Understand Support Vector Machine
  • Learn How Support Vector Machine works?
  • Hyperparameter optimization

 

Topics:

  • What is Naïve Bayes?
  • How Naïve Bayes works?
  • Implementation of Naïve Bayes Classifier
  • What is a Support Vector Machine?
  • Illustrate how Support Vector Machine works?
  • Hyperparameter optimization
  • Grid Search vs Random Search
  • Implementing Support Vector Machine for Classification

 

Hands On:

  • Implementation of Naïve Bayes, SVM

Goal: This module covers Unsupervised learning and various types of clustering. By using these you will learn how to analyze the data.

Objective: At the end of this Unsupervised Learning module, you will be able to:

  • Define Unsupervised Learning
  • Discuss K-means, C-means, Hierarchical Clustering Analysis.

Topics:

  • What is meant by Clustering & its Use Cases?
  • What is K-means Clustering?
  • How does K-means algorithm work?
  • How to do optimal clustering
  • What is C-means Clustering?
  • What is Hierarchical Clustering?
  • How Hierarchical Clustering works?

 

Hands On:

  • Implementation of K-means Clustering and Hierarchical Clustering.

Goal: This module helps you to understand Association rules and their extension towards recommendation engines.

 

Objective: At the end of this Association Rules Mining and Recommendation module, you should be able to:

  • Define Association Rules
  • Learn the backend of recommendation engines
  • Develop your own recommendation engine using python

 

Topics:

  • What are Association Rules?
  • Parameters of Association Rule
  • Calculating Parameters of Association Rule 
  • Recommendation Engines
  • How do Recommendation Engines work?
  • Collaborative Filtering
  • Content Based Filtering

 

Hands On:

  • Apriori Algorithm
  • Market Basket Analysis

Goal: In this module, you will understand how to select one model over another. Also, you will understand the concept of Boosting and its importance in Machine Learning. You will learn how to convert weaker algorithms to stronger ones.

 

Objective: At the end of this Model Selection and Boosting module, you should be able to:

  • Discuss Model Selection
  • Define Boosting
  • Express the need of Boosting
  • Explain the working of Boosting algorithm

 

Topics:

  • What is the Model Selection?
  • Need  Selection
  • Cross – Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting

 

Hands on:

  • Cross Validation
  • AdaBoost

Goal: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will understand how to define an optimal solution for an agent based on agent environment interaction.

 

Objective: At the end of this Reinforcement module, you will be able to

  • Explain the concept of Reinforcement Learning
  • Generalize a problem using Reinforcement Learning
  • Explain Markov’s Decision Process
  • Demonstrate Q Learning

 

Topics:

  • 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

Goal: In this module, you will understand the analysis of Time Series Analysis which helps to forecast dependent variables based on time. Also, you will understand various different models for time series modeling such that it enables you to analyze real time dependent data for forecasting.

 

 

Objective: At the end of this Time Series Analysis module, you should be able to:

  • Explain Time Series Analysis (TSA)
  • Discuss the need for TSA
  • Describe ARIMA modelling
  • Forecast the time series model

 

Topics:

  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • White Noise
  • AR model
  • MA model
  • ARMA model
  • ARIMA model
  • Stationarity
  • ACF & PACF

 

Hands on:

  • Checking Stationarity
  • Converting a non-stationary data to stationary
  • Implementing Dickey Fuller Test
  • Plot ACF and PACF
  • Generating the ARIMA plot
  • TSA Forecasting
Like the course? Enroll Now

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Course Details

After completing this Machine Learning Training course using Python at IQ Online, you should be able to:

  • Understand the roles of Machine Learning Engineer
  • Learn how to Automate data analysis using python
  • Describe Machine Learning
  • Gain insights on how to Work with real-time data
  • Understand various tools and techniques for predictive modeling
  • Understand the algorithms of Machine Learning and their implementation
  • Learn how to Validate Machine Learning algorithms
  • Explain Time Series and understand it’s related concepts

IQ Training’s Python Machine Learning Certification Course is best suitable for the following professionals:

  • Developers who aspire to become ‘Machine Learning Engineer'
  • Analytics Managers 
  • Business Analysts who aspire to learn Machine Learning (ML) Techniques
  • Information Architects who aspire to enhance knowledge on predictive Analytics. 
  • 'Python' professionals who wish to design automatic predictive models

Following are the pre-requisites for this course:

 

  • Development experience with Python. 
  • Fundamentals of Data Analysis practiced with data analysis tools.

 

IQ Training provides self-paced ‘Python Statistics for Data Science’ course as complementary to those you enroll in the course.

Machine Learning Certification Training using Python Ceritficate

Machine Learning Certification Training using Python Reviews

25,859

Total number of reviews

4.5

Aggregate review score

80%

Course completion rate

Machine Learning Certification Training using Python Features

Live Online Classes

All our Classes are Live Instructor led online sessions. You can attend at the comfort of your place and Login to our Classes.

LMS (Learning Management System)

LMS will help you to organize your all training material, session videos and review at later date. You can access LMS anytime and review your completed classes. If you miss any class, then you can review the missed class in LMS.

Flexible Schedule

For some reasons, you can not attend the Classes, we can enroll you in the next possible classes. we assure flexibility in class schedules.

Lifetime Access to Learning Platform

You will get Lifetime free access to LMS(Learning Mangement System) You can access all Videos, class room assignments, quizzes, Projects for Life time. You will also get free video sessions for Life time.

Highest Course Completion Rate

We have the highest course completion rate in the Industry. If you miss a class, you can opt for the missed class in different batch. We assure you the best training possible for you to succeed.

Certificate of completion

We provide you the Industry recognized Certification of Course completion This certificate will sometimes helps you to get reimbursement of training expenses by your company.

Like the course? Enroll Now

Structure your learning and get a certificate to prove it.

Machine Learning Certification Training using Python FAQs

You will never miss a class at IQ Online Training! You can choose either of the two options:

  1. View the recorded session of the class available in your LMS or
  2. You can attend the missed session in any other live batch.

After the enrolment, the LMS access will be instantly provided to you able to access for lifetime which includes complete set of previous class recordings/PPTs/PDFs/assignments. You can start learning right away.

Your access to the Support Team is for lifetime. Our team will help you in resolving queries, during and after the course.

Yes, once enrollment has done for course. Access to the course material will be available for lifetime.

You can Call our support numbers listed in site OR Email us at info@iqtrainings.com.

You can view in-depth class sample recordings before the enrollment. Experience the complete learning instead of a demo session with our expertise.

All the instructors are Industry experts with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are well trained for providing an awesome learning experience to the participants.

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