Python Machine Learning Course

BY
Analytixlabs

Develop a practical approach to learning the concepts and principles of machine learning with Python in the Python machine learning course.

Mode

Online

Duration

320 Hours

Fees

₹ 85000

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom +1 more
Mode of Delivery Video and Text Based
Frequency of Classes Weekdays, Weekends
Learning efforts 8-10 Hours Per Week

Course overview

The Python machine learning course is developed and offered online by the leading data science institute Analytixlab partnering with the International Business Machines Corporation(IBM) which provides a dual certification for the candidates. This machine learning program taught along with Python will take a hundred and ten hours to complete with sixteen classes of experiential learning.

This online training in machine learning provides the learners with an interactive learning environment and an option for independent learning. The course curriculum is designed for the students with the skills of Python and data analytics to learn about the real-world challenges in the field of machine learning. The learners are enabled to work with eight industrially relevant projects and assignments wherein they can exhibit their theoretical knowledge of the domain. The Python machine learning course training helps the students gain the technical knowledge necessary to excel in the field of data science using machine learning skills along with expertise in Python. 

The highlights

  • Online mode
  • Interactive E-learning
  • Self-paced learning
  • Demo session
  • Dual certification
  • Mentorship 
  • Student loan
  • Flexible payment options
  • Placement support

Program offerings

  • Course videos
  • Lectures
  • Recordings
  • E-learning
  • Independent learning
  • Student loan
  • Technical support
  • Doubt resolution
  • Assignments
  • Projects
  • Exercises
  • Course completion certificate
  • Dual certification
  • Flexible deadlines.

Course and certificate fees

Fees information
₹ 85,000

The course fee for the Python machine learning course training is specific to the mode of learning as mentioned in the table below and the students can pay them in three installments or can apply for a student loan if they cannot afford the one-time payment.

Python machine learning course fee structure

HeadsAmount in INR (exclusive of taxes)

Classroom & Bootcamp

Rs. 109000 + taxes

Fully Interactive Live Online

Rs. 109000 + taxes 

Blended eLearning

Rs. 85000 + taxes 

certificate availability

Yes

certificate providing authority

Analytixlabs

Who it is for

The Python machine learning course benefits individuals who wish to improve their skill set in machine learning with a preliminary knowledge of Python and data analytics.

Eligibility criteria

The candidates who wish to take the Python machine learning course are required to have a knowledge of Python and the data analytics process done using NumPy and Pandas.

Certificate qualifying details

  • The students of the Python machine learning course will be awarded the course completion certificate from Analytixlab after the submission and evaluation of assignments and projects within one year.
  • For the dual certification, the students have to take the MCQ tests along with the assignments and projects.

What you will learn

Machine learning Knowledge of python Knowledge of artificial intelligence

The Python machine learning course syllabus is developed for the learners to gain practical knowledge and understanding of the techniques and tools associated with machine learning. The students will learn about supervised learning and unsupervised learning strategies in machine learning. The course also provides insights into text mining and natural language programming. By the end of the Python machine learning course classes, the students will get familiar with the important topics which are dealt with in the course linear and logistics regression, decision trees, ensemble learning, support vector machines, K-nearest neighbors, naive Bayesian, neural network models, clustering and principal component analysis. 

The syllabus

Building blocks and data analytics with Excel

Basic Excel
  • Excel Environment 
  • Key Terminologies 
  • Short Cuts 
  • Key Functionalities 
  • Copy-paste-paste special 
  • Formatting & conditional Formatting 
  • Basic Excel Functions - Types of Functions
  • Relational operators
  • Data Sorting, Filtering and Data Validation 
  • Understanding of Name Ranges
  • Pivot tables - Charts 
  • Basics of charts
Recommender Systems
  • Content-based recommender systems
  • Collaborative Filtering
Time Series Forecasting
  • What is forecasting?
  • Applications of forecasting
  • Time Series Components and Decomposition
  • Types of Seasonality
  • Important terminology: lag, lead, Stationary, stationary tests, auto correlation & white noise, ACF & PACF plots, auto regression, differencing
  • Classification of Time Series Techniques (Uni-variate & Multivariate)
  • Time Series Modeling & Forecasting Techniques
    • Averages (Moving average, Weighted Moving Average)
    • ETS models (Holt Winter Methods)
    • Seasonal Decomposition
    • ARIMA/ARIMAX/SARIMA/SARIMAX
    • Regression
    • Evaluation of Forecasting Models
Unsupervised Learning
  • Principal Component Analysis 
  • K-Means Clustering
  • Hierarchical Clustering
  • Density-Based Clustering

Text Mining using NLP

Introduction to Text Mining
  • Text Mining - characteristics, trends
  • Text Processing using Base Python & Pandas, Regular Expressions
    • Text processing using string functions & methods
    • Understanding regular expressions
    • Identifying patterns in the text using regular expressions
Text Processing with modules like NLTK, Sklearn
  • Getting Started with NLTK
  • Introduction to NLP & NLTK
  • Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, toolbox etc)
Initial data processing and simple statistical tools
  • Reading data from file folder/from text file, from the Internet & Web scrapping, Data Parsing
  • Cleaning and normalization of data
  • Sentence Tokenize and Word Tokenize, Removing insignificant words(“stop words”), Removing special symbols, removing bullet points and digits, changing letters to lowercase, stemming /lemmatization /chunking
  • Creating Term-Document matrix
  • Tagging text with parts of speech
  • Word Sense Disambiguation
  • Finding associations
  • Measurement of similarity between documents and terms
  • Visualization of term significance in the form of word clouds
Advanced data processing and visualization
  • Vectorization (Count, TF-IDF, Word Embedding's)
  • Sentiment analysis (vocabulary approach, based on Bayesian probability methods)
  • Name entity recognition (NER)
  • Methods of data visualization
    • word length counts plot
    • word frequency plots
    • word clouds
    • correlation plots
    • letter frequency plot
    • Heat map
  • Grouping texts using different methods
  • Language Models and n-grams -- Statistical Models of Unseen Data (Smoothing)
Text mining - predictive modeling
  • Semantic similarity between texts
  • Text Segmentation
  • Topic Mining (LDA)
  • Text Classification (spam detection, sentiment analysis, Intent Analysis)

Introduction to AI and cloud computing

Introduction to Google Colab
  • Introduction to Google Colab
Introduction to Deep Learning
  • What are the Limitations of Machine Learning?
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • Overview of important python packages for Deep Learning
Introduction to Artificial Intelligence (AI)
  • Modern era of AI
  • Role of Machine learning & Deep Learning in AI
  • Hardware for AI (CPU vs. GPU vs. FPGA)
  • Software Frameworks for AI & Deep Learning
  • Key Industry applications of AI
Artificial Neural Networks
  • Overview of Neural Networks
  • Activation Functions, hidden layers, hidden units
  • Illustrate  & Training a Perceptron
  • Important Parameters of Perceptron
  • Understand limitations of A Single Layer Perceptron
  • Illustrate Multi-Layer Perceptron
  • Understand Backpropagation – Using Example
  • Implementation of ANN in Python- Keras
Introduction to Cloud Computing
  • What is Cloud Computing? Why it matters?
  • Traditional IT Infrastructure vs. Cloud Infrastructure 
  • Cloud Companies (Microsoft Azure, GCP, AWS ) & their Cloud Services (Compute, storage, networking, apps, cognitive etc.)
  • Use Cases of Cloud computing 
  • Overview of Cloud Segments: IaaS, PaaS, SaaS 
  • Overview of Cloud Deployment Models
  • Overview of Cloud Security 
  • AWS vs. Azure vs. GCP 
  • Implementation of ML/DL model in Cloud

Industrial and functional sessions (Domain Understanding)

Industrial and functional sessions (Domain Understanding)
  • Introduction to Data Sources for Various Industries
  • Introduction to Analytics Project Management
  • Marketing Analytics
  • Risk Analytics       
  • Operation Analytics
  • Digital Analytics (Web Analytics)
  • Social Network Analytics                      
  • Banking & Financial Services, Insurance
  • Retail & E-Commerce  
  • Pharma & Health Care                              
  • Telecom & Network

Admission details

The course admission to the Python machine learning course is done online through the Analytixlab website.

Step 1: Go to the course page on the official website of Analytixlab using the link, https://www.analytixlabs.co.in/machine-learning-course-certification-training

Step 2: Select your preferred mode for learning the course and click the respective ‘Enroll Now’ button.

Step 3: Fill in the required details and complete the enrollment.


Filling the form

On the registration page of the Python machine learning course, the applying candidates will have to enter their name, phone number, email address, course name, and city name.

How it helps

The Python machine learning course certification is provided by the leading data science institute in India and qualifies you with the skills for statistical analysis and modeling, predictive and machine learning modeling, supervised machine learning, text mining, and NLP along with data science methods.

FAQs

Which data science institute offers the course on Python machine learning?

The Python machine learning course is provided by Analytixlab in partnership with IBM.

How long is the duration of the Python machine learning course training?

The program is scheduled for 320 hours.

How many projects and assignments are part of the Python machine learning course curriculum?

The students will have to complete thirty three Assignments & Projects.

What are the eligibility criteria for the Python machine learning course classes?

The applicants are required to have basic knowledge of Python and data analytics.

Can I pay the Python machine learning course fee in installments?

Yes, you can pay the course fee in three installments.

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