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Machine Learning with Python Course

Machine Learning Course for People with no Prior Programming Knowledge 

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• 2.3 Million Machine Learning Jobs will be created by 2020
• Average Salary Base for Machine Learning Jobs is $146,085
• 10,000+ Monthly Machine Learning Job Openings
• Initial Salary 5,00,000 – 12,00,000 INR Per Annum
• Machine Learning Engineers Rule The Top 10 AI Jobs List

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In-Depth Advanced Modules 

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Hands-on Projects

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Expert Trainers 

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Hours of Live Classes

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Certifications

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Assignments Hours

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Exclusive Offers!

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Certification Validity 

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Job Assistance

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Why Machine Learning with Python

Should be Your Immediate Pick?

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Machine Learning (ML) is one of the most exploding career choices of the day. Every day numerous aspirants are paving the way to catch the fire. To meet the expanding demands of the Machine Learning skillset, the  Machine Learning with Python is a course high in demand. 

Digital Vidya’s Machine Learning with Python course is thoughtfully rooted in Machine Learning using Python. Python is the most loved language in the market and the easiest to learn as well. Learn Machine Learning Using Python gives your resume the boost it needs to get you going in the Data Science & the Machine Learning Industry. 

Who can opt for Machine Learning using Python course?

• Non-programmers who wish to shift their career to Machine Learning
• Programmers who don’t have an expertise in Python coding but know Advanced Excel & basic statistics well 
• Aspiring ML Engineers with the knowledge of advanced excel, statistics, calculus and probability

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Machine Learning with Python Course Enrollments Map

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24 Modules

Our Python Machine Learning Course Syllabus

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The course curriculum of our Machine Learning with Python program is wisely designed for a newbie into technology. Through this Python Machine Learning Course, a fresher with knowledge of excel and basic statistics would be able to fulfil his dream of becoming a Machine Learning Engineer.

The curriculum starts with getting in-depth with Python programming. The aim is to ease the learner into Machine Learning using the Python language. Once the candidate is done with this part, he can take up the second part of the course which is specific to Machine Learning and other advanced Data Science applications.

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• Installation
• Python – Syntax
• Python – Variables and Datatypes
• Python – Numbers
• Strings
• Sequences
• List
• Tuples
• Ranges
• Dictionary
• Sets
• Operators
• If..Else.. Statements
• For Loop
• While Loop
• Break
• Continue
• Pass
• Date & Time
• Functions
• Packages and modules
• Reading a File
• Writing into File
• Class & Objects
• Python – Exceptions
• Regular Exp
• Mathematics

[/et_pb_tab][et_pb_tab title=” Python MySQL” _builder_version=”3.26.6″ global_colors_info=”{}”]

• Environment Setup
• Database Connection
• Creating a New Database
• Creating Tables
• Insert Operation
• Read Operation
• Update Operation
• Join Operation
• Performing Transactions

[/et_pb_tab][et_pb_tab title=”Numpy” _builder_version=”3.26.6″ global_colors_info=”{}”]

• ndarray
• Array Creation
• Data Type Objects
• Data type Object (dtype) in NumPy
• Indexing
• Basic Slicing and Advanced Indexing
• Iterating Over Array
• Binary Operations
• Mathematical Function
• String Operations
• Linear Algebra
• Sorting, Searching and Counting
• Set 1 (Introduction)
• Set 2 (Advanced)
• Multiplication of two Matrices in Single line using Numpy in Python

[/et_pb_tab][et_pb_tab title=”Pandas DataFrame” _builder_version=”3.26.6″ global_colors_info=”{}”]

• Creating a Pandas DataFrame
• Dealing with Rows and Columns in Pandas DataFrame
• Indexing and Selecting Data with Pandas
• Boolean Indexing in Pandas
• Conversion Functions in Pandas DataFrame
• Iterating over rows and columns in Pandas DataFrame
• Working with Missing Data in Pandas
• Working With Text Data
• Working with Dates and Times
• Merging, Joining and Concatenating

[/et_pb_tab][et_pb_tab title=”Data Analysis” _builder_version=”3.26.6″ inline_fonts=”Trebuchet” global_colors_info=”{}”]

• Data visualization using Bokeh
• Exploratory Data Analysis in Python
• Data visualization with different Charts in Python
• Data Analysis and Visualization with Python
• Math operations for Data analysis

[/et_pb_tab][et_pb_tab title=”Object-Oriented Concepts” _builder_version=”3.26.6″ global_colors_info=”{}”]

• Class, Object and Members
• Data Hiding and Object Printing
• Inheritance, examples of an object, subclass and super
• Polymorphism in Python
• Class and static variable in Python
• Class method and static method in Python
• Changing class members
• Constructors in Python
• Destructors in Python
• First-class function
• str() vs repr()
• str() vs vpr()
• Metaprogramming with metaclasses
• Class and instance attribute
• Reflection
• Barrier objects
• Timer objects
• Garbage collection

[/et_pb_tab][et_pb_tab title=”Functions” _builder_version=”3.26.6″ global_colors_info=”{}”]

• Functions in Python
• class method vs static method in Python
• Write an empty function in Python – pass statement
• Yield instead of Return
• Return Multiple Values
• Partial Functions in Python
• First Class functions in Python
• Precision Handling
• *args and **kwargs
• Python closures
• Function Decorators
• Decorators in Python
• Decorators with parameters in Python
• Memoization using decorators in Python
• Python bit functions on int (bit_length, to_bytes and from_bytes)

[/et_pb_tab][et_pb_tab title=” Statistics Fundamentals” _builder_version=”3.26.6″ global_colors_info=”{}”]

• Graphically Displaying Single Variable
• Measures of Location
• Measures of Spread
• Displaying relationship – Bivariate Data
• Scatterplot
• Measures of association of two or more variables
• Covariance and Correlation
• Probability
• Joint Probability and independent events
• Conditional probability
• Bayes’ Theorem
• Prior, Likelihood and Posterior
• Discrete Random Variable
• Probability Distribution of Discrete Random Variable
• Binomial Distribution
• Continuous Random Variables
• Probability Distribution Function
• Uniform Distribution
• Normal Distribution
• Point Estimation
• Interval Estimation
• Hypothesis Testing
• Testing a one-sided Hypothesis
• Testing a two-sided Hypothesis

[/et_pb_tab][et_pb_tab title=”Machine Learning with Python ” _builder_version=”3.26.6″ global_colors_info=”{}”]

• Applications of Machine Learning
• Supervised vs Unsupervised Learning
• Python libraries suitable for Machine Learning

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• Regression – Features and Labels
• Regression – Training and Testing
• Regression – Forecasting and Predicting
• Regression – Theory and how it works
• Regression – How to program the Best Fit Slope
• Regression – How to program the Best Fit Line
• Regression – R Squared and Coefficient of Determination Theory
• Model evaluation methods

[/et_pb_tab][et_pb_tab title=”Classification” _builder_version=”3.26.6″ global_colors_info=”{}”]

• Classification Intro 
• Applying K Nearest Neighbors to Data
• Euclidean Distance theory
• Decision Trees
• Regression Trees
• Random Forests
• Boosting Algorithm
• Principal Component Analysis
• Linear Discriminant Analysis

[/et_pb_tab][et_pb_tab title=”Support Vector Machine Introduction” _builder_version=”3.26.6″ global_colors_info=”{}”]

• Vector Basics
• Support Vector Machine Fundamentals
• Constraint Optimization with Support Vector Machine
• Beginning SVM from Scratch in Python
• Support Vector Machine Optimization in Python
• Visualization and Predicting with our Custom SVM
• Kernels Introduction
• Soft Margin Support Vector Machine

[/et_pb_tab][et_pb_tab title=”Machine Learning – Clustering Introduction” _builder_version=”3.26.6″ global_colors_info=”{}”]

• Handling Non-Numerical Data for Machine Learning
• K-Means with Titanic Dataset
• K-Means from Scratch in Python
• Finishing K-Means from Scratch in Python
• Hierarchical Clustering with Mean Shift Introduction
• Introduction Naive Bayes Classifier
• Naive Bayes Classifier with Scikit
• Introduction into Text Classification using Naive Bayes
• Python Implementation of Text Classification

 

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• Content-based recommender systems
• Collaborative Filtering

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• Text Preprocessing
• Noise Removal
• Lexicon Normalization
• Lemmatization
• Stemming
• Object Standardization
• Text to Features (Feature Engineering on text data)
• Syntactical Parsing
• Dependency Grammar
• Part of Speech Tagging
• Entity Parsing
• Phrase Detection
• Named Entity Recognition
• Topic Modelling
• N-Grams
• Statistical features
• TF – IDF
• Frequency / Density Features
• Readability Features
• Word Embeddings
• Important tasks of NLP
• Text Classification
• Text Matching
• Levenshtein Distance
• Phonetic Matching
• Flexible String Matching
• Important NLP libraries

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Python Machine Learning Course Assignments

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Python Machine Learning is a new booming entry in Advanced AI culture. The irreplaceable heights of the AI technology have raised the demand for Machine Learning Engineers. Since Python is a relatively easy language, learn Python for Machine Learning makes a lot of sense for non-techies. 

Python Machine Learning Course at Digital Vidya offers very engaging assignments for developing the practical skills of a professional. A student who chooses to work on all these assignments will hit the ground running at any given Machine Learning position. 

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The first assignment of Python programming wraps:
    – The basic foundation of variables
    – Data types
    – Arithmetic, logical and comparison operators.

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The second assignment covers:
    – Data types of Python List
    – Data Cleansing
    – Dictionary
    – Conditional and Iterative loops

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The second assignment covers:
    – Data types of python List
    – data cleansing,
    – Dictionary
    – Conditional and Iterative loops

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Here you will get a deep dive of NumPy Library assignments which are critical for Data Science.

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Data Manipulation envelopes the assignments on:
    – The Pandas,
    – Functions (iloc, tail, head, groupby, fillna, etc)

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This task is structured to unfold the complex faces of manipulating data. In this assignment, you will participate in:

    – Building functions
    – Apply those on the Pandas data frames

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This task will drive you to clear the concepts of:
    – Data Analyzing
    – Data manipulation 
    – Making sense out of this data

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    – Create and visualize stories/insights from the data
    – Implement various visualizations using Matplotlib and Seaborn Python libraries

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    – Merge/concatenate datasets using Python
    – Melting/changing dimensions of datasets by converting rows to columns and vice versa

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Python Machine Learning Course Capstone Projects

Capstone projects of this Machine Learning Using Python Course help students to understand the real-life value of Analytics and Machine Learning applications. The Python programming coding course will make the student eligible to get deep into performing Machine Learning tasks with ease. 

Furthermore, the hands-on and practical aspect of working on Python Machine Learning projects will sharpen your saw i.e. give your skills more strength. Since all these projects are based on real industry data, students of Machine Learning Using Python training program get a clear indication of the problems to be solved in the real world, coupled by a readiness to face industry challenges. 

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Natural Language Processing

Duration: 3 Weeks

Project Description:

This is one of the most applied areas for AI, Data Science, and Machine Learning across domains and industries. The real world is filled with mostly messy text data, and handling text is an important step towards making smarter algorithms. Using IMDB dataset from the movie domain, the learner will apply the most common concepts of NLP.

Key Takeaway:

This project will empower the learners to build intermediate skills in the natural language processing domain. A few of the fundamentals of working with textual data covered in this project are:

1. Remove stop words
2. Apply Stemming and Lemmatization
3. Create a cluster of words
4. Build a sentiment analysis model and a clustering model

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Healthcare Analysis

Duration: 3 Weeks

Project Description:

Electroencephalography (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain. For this project, we will use the large EEG database at UCI Machine learning repository. This data arises from a large study to examine EEG correlates of genetic predisposition to alcoholism. One fascinating question is whether the patterns are different for an alcoholic and regular subject?

Key Takeaway:

This capstone project focuses on EEG data analysis, giving an opportunity for students to learn through complexities in dealing with such complex real-world data. The project contains the following exercises:

1. Parse and store in an easily understandable and readable form
2. Exploratory data analysis to better understand the data
3. Using Statistical concepts like Hypothetical testing
4. Identify features to predict whether a subject is alcoholic or not
5. Use machine learning algorithms to develop a suitable classifier

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Bank Marketing

Duration: 3 Weeks

Project Description:

The banking industry is working in a very competitive environment and needs to strategize to grow its business.  This project is related to the marketing campaigns related to term deposits, making an interesting multi-disciplinary work that mixes both the finance and the marketing domain.

Key Takeaway:

The approach to this project is to think, define, design, code, test and tune your solution, in such a way that you apply all aspects of the data science process. The data is a real-world data with unclean and null values.

The objective is to:

1. Build the model to predict if a customer will subscribe
2. Identify influential factors to form marketing strategies
3. Improve long-term relationship with the clients

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Deep Learning Based Project

Duration: 3 Weeks | Price: ₹5000 (Including Tax)

Project Description:

E-Commerce has experienced considerable growth since the dawn of the internet as a commercial enterprise. Deep Learning excels at identifying patterns in unstructured data and can predict the class of an uploaded image applied on eCommerce context. This project is an attempt to replicate virtual store assistance through image recognition over an eCommerce Fashion MNIST dataset.

Key Takeaway:

This project focuses on the implementation of Neural Networks to solve complex unstructured data problems. The objective is to:

1. Build the model to classify the various categories (analytic vertical) of clothing/fashion related images.
2. Understanding the implementation of deep learning concepts through Tensorflow and Keras.
3. Model optimization by tuning hyper-parameters and implementing dropout layers.

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Machine Learning Using Python Course Schedule

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81+ Hours of in-depth Live Sessions

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27+ Weeks

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54+ Hours

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Upcoming batches of Python Machine Learning Online Training

 

 

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14th Mar 2020 (Sat+Sun)

10 AM – 1:30 PM (IST)

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Enquire Now

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100% Interview Guarantee for Machine Learning with Python Course 

Digital Vidya offers a 100% Interview Guarantee for its Online Machine Learning using Python Course.

We have a dedicated placement cell, which works closely with our participants for their placement needs. Here is a snapshot of our placement process.

Note: We do not commit to a base salary standard. Your salary will depend on your own skills and interview only. 

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On successful completion of the course, which includes submission of assignments & attaining necessary certifications, we work with the candidates to create an effective resume.

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01

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The updated resume is shared with relevant organisations and agencies including our partners. On shortlisting, we help the candidate to pass the initial round of discussion.

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02

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Based on the organizations needs & candidates ability, we train them to maneuver themselves to crack the interview. This stage helps the candidate to be 100% ready.

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03

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After a successful interview, we guide the candidate from accepting the offer to joining the organization for a successful career. We help him to stand out at his workplace.

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04

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Python Machine Learning Course Programming Languages, Platforms and Tools

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Python Machine Learning Training Details

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Why Learn Machine Learning with Python Online?

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If you are serious about becoming a Machine Learning Engineer, you got to start learning from an online course! 

Digital Vidya has a legacy of 9+ years in which we have trained 35,000+ Professionals from 55+ Countries of the World.

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We counsel 100s of candidates every day : )

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Learn from Academic Researchers & Industry Practitioners based around the Globe. 

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Travelling, out of home or any other emergency doesn’t become a barrier now. Learn on the move. 

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Leverage technology to save inconvenient & time-wasting commutes. 

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Every module comes with a module assignment, to ensure that you learn hands-on.

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Our doubt-clearing sessions and class recordings ensure that you can revise your course at will. 

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Get access to updated content for a lifetime. 

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Machine Learning using Python Combo Course Trainers

We have a team of 10+ trainers and 3-course advisors who in tandem to create a learning experience that’s par excellence. Our trainers are academic research & industry practitioners from across the Globe. 

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Our Machine Learning with Python Course Advisers

Veterans in the field of technology with 20+ years of experience have ideated this course. 

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2 Certifications

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Machine Learning Using Python Certifications

[/et_pb_text][/et_pb_column][/et_pb_row][et_pb_row column_structure=”1_2,1_2″ use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” module_class=”learning-python-course” _builder_version=”3.26.6″ custom_margin=”||10px” border_color_all=”#d55528″ global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”3.25″ use_background_color_gradient=”on” background_color_gradient_start=”#d55528″ background_color_gradient_end=”rgba(213,85,40,0)” background_color_gradient_start_position=”26%” background_color_gradient_end_position=”53%” custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Digital Vidya’s Machine Learning Using Python Certification ” image=”https://www.digitalvidya.com/wp-content/uploads/2019/09/Machine-learning-with-python_39eb81d19896f4da384a372a773d22b3.jpg” _builder_version=”3.26.6″ header_font_size=”23px” text_orientation=”center” custom_padding=”10px||10px” custom_css_blurb_title=”font-size: 23px !important;” border_width_all=”2px” border_color_all=”#d55528″ border_style_all=”double” global_colors_info=”{}”]

How to get this Certificate?

On successful completion of all assignments and the project, the participant will get a Machine Learning Certificate issued by Digital Vidya. He has to have a minimum 80% attendance too.

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How to get this Certificate?

On successful passing of the Vskills examination, the participant will get a Machine Learning Certificate issued by Vskills. (A nominal examination fee of Rs. 1770/- involved) 

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Get all details regarding the Maching Learning Using Python Course including trainer details, assignments, and topics taught

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Our Python Machine Learning Course Student Reviews

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It was a tremendous journey right from the beginning. A huge opportunity opened in front of us in the Data Science world.

[/et_pb_testimonial][et_pb_testimonial author=”Prerna Sathiyal” job_title=”Student” portrait_url=”https://www.digitalvidya.com/wp-content/uploads/2019/01/3_cbb80b195b507c6eb15f056b1103ae1a.jpg” quote_icon=”off” quote_icon_color=”#15314E” quote_icon_background_color=”#ffffff” portrait_width=”100px” portrait_height=”100px” _builder_version=”3.26.6″ body_font=”Open Sans||||||||” body_font_size=”16px” body_line_height=”2em” background_color=”#ffffff” custom_padding=”||60px||false|false” animation_style=”fold” animation_delay=”400ms” border_width_all=”1px” border_color_all=”rgba(21,49,78,0.2)” border_radii_portrait=”on|100px|100px|100px|100px” border_width_all_portrait=”3px” border_color_all_portrait=”#d55528″ box_shadow_style_image=”preset1″ locked=”off” global_colors_info=”{}”]

Great experience. Easy and organized learning, great approach.

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Digital Vidya gave me a comprehensive knowledge of Data science within a very short period of time.

[/et_pb_testimonial][et_pb_testimonial author=”Arvind S” job_title=”Academician” portrait_url=”https://www.digitalvidya.com/wp-content/uploads/2019/01/5_52db069967f53fbfd65e397b8344d650.jpg” quote_icon=”off” quote_icon_color=”#ffffff” quote_icon_background_color=”#15314E” portrait_width=”100px” portrait_height=”100px” _builder_version=”3.19.4″ body_font=”Open Sans||||||||” body_font_size=”16px” body_line_height=”2em” background_color=”#2F3B5F” background_layout=”dark” animation_style=”fold” animation_delay=”150ms” border_radii_portrait=”on|100px|100px|100px|100px” border_width_all_portrait=”3px” border_color_all_portrait=”#ffffff” box_shadow_style_image=”preset1″ global_colors_info=”{}”]

Case studies and projects improved my skills and gave me the confidence to call myself a data scientist.

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This course is best for beginners and it will give you complete exposure of every field of Data Science and Machine Learning.

[/et_pb_testimonial][et_pb_testimonial author=”Lipi Sahu” job_title=”Student” portrait_url=”https://www.digitalvidya.com/wp-content/uploads/2019/01/4_c117dfb9b46b80063da8c3661d9d8442.jpg” quote_icon=”off” quote_icon_color=”#ffffff” quote_icon_background_color=”#ffa727″ portrait_width=”100px” portrait_height=”100px” _builder_version=”3.26.6″ body_font=”Open Sans||||||||” body_font_size=”16px” body_line_height=”2em” background_color=”#d55528″ background_layout=”dark” custom_padding=”||63px||false|false” animation_style=”fold” border_radii_portrait=”on|100px|100px|100px|100px” border_width_all_portrait=”3px” border_color_all_portrait=”#ffffff” box_shadow_style_image=”preset1″ locked=”off” inline_fonts=”Montserrat” global_colors_info=”{}”]

It was a great experience. Got to learn many new things going on in the present industry.

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Reviews of Expert Industry Leaders

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Machine Learning Course FAQs

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Machine Learning is the subunit of thriving AI. Here, you meet the exciting opportunities to train the machine to learn, analyze and solve data. Although an old topic but Machine Learning has discrete spark these days.

The prerequisite to developing a Machine Learning Program is the optimum knowledge of a coding language.

In our Python Machine Learning Course, Digital Vidya provides you with prior training on Python language. This new introduction clears your concepts on codings before developing your skills on Machine Learning.

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The course goes well for the aspiring candidates who want to mount their future in AI. Integrated Python Machine Learning Course is advised for:
    – IT professionals
    – Software Engineers
    – Data professionals
    – Freshers graduated with BE/MCA

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This combo program offers you a reasonable fee structure of 44,990-/. It is a 27-29 weeks program which gears up your skills to meet your needs with the market demands.

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According to Indeed, Machine Learning is the most in-demand job in the market now. You have varied choices to work in any of the following sectors:

    – Finance Services
    – Retail
    – Healthcare
    – Transportations
    – Government 

After this course, you can expect to find a Machine Learning Engineer’s position with ease. 

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According to Indeed, Machine Learning is the most in-demand job with the highest salary package. The average salary made by a Machine Learning Engineer is $121,833.

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Machine Learning works on the Algorithms. There are many coding languages that can be used, but Python is the most productive programming tool.

This is the reason that Digital Vidya has come up with a combo offer to efficiently introduce you to the best programming language and hence spark your capabilities in Machine Learning.

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Opting for this course at Digital Vidya provides you 90+ hours assignments, real-time case studies and hands-on projects. These essentials of our course guarantee you full time exposure for optimizing your learning graph.

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To overall encapsulate the course level, machine learning is hard. Stepping forefront to cover the difficulty issue, Digital Vidya has designed its curriculum to make it approachable for both freshers and professionals. Here step by step, you will be introduced from scrap to full model of Machine Learning.

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The integrated course of Python Machine Learning paves a journey to master your skills in Machine Learning irrespective of your background. Our training at Digital Vidya starts from the scrap. We provide all prerequisites for our course to gather both freshers and professionals on the same plate.

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The course is an integrated program of Python Language and Machine Learning of 36 weeks. It gears up with the introduction to Python programming for 14 weeks and then gradually teaches you to apply the python coding in Machine Learning. The duration of Machine Learning course is 12 weeks. There are a total of 90+ assignment Hours.

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The course is for aspirants who want to start from scrap. The only prerequisites are general calculus, probability and mathematical backbone.

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Digital Vidya gives you a 100% Interview Guarantee for Python Machine Learning course. We have a committed placement cell which assures to fix your interview in the following steps:

• – Resume creation: After the successful completion of the course, Digital Vidya works effectively for creating your resume
• – Job Application: After updating, Digital Vidya shares your resume with it’s partner agencies and other relevant organizations.
• – Interview Readiness: After fixing the interview, Digital Vidya prepares its candidates to crack the interview.
• – Selection and Joining: After clearing the interview, we provide assistance from accepting the offer to joining it.

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After the successful completion of Python Machine Learning Course, you will be provided with 2 certificates, one from Digital Vidya and second one from our partner agency, Vskills.

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Machine Learning is a highly responsible job in all the sectors. Some of their vital responsibilities include:

• – Enhancing the data scalability
• – Establishing Machine Learning tools in manufacturing
• – Data Engineering
• – Optimizing the database and practicing new experiments

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Yes, if by any chance you miss the live class, you can watch and learn from the recorded session. Other than this, we provide free lifetime update of the course to match your steps with the advancing technology.

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