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Master the depth of Machine Learning and Deep Learning using Python

Deep Dive in Advanced Python

Join Booming Data
Analytics Industry

SkillEnable focuses on developing you as the prime target of recruiters by developing  interpersonal and interview skills along with regular curriculum.

Become a Target Hire

SkillEnable focuses on developing you as the prime target of recruiters by developing interpersonal and interview skills along with a regular curriculum.

Industry Relevant Tools

Get hands-on learning experience of various relevant industry tools and also practice real-life cases studies and Capstone projects using the Latest Data Science tools.

Tailor-made Curriculum

Individual candidate focused curriculum and soft skill enhancement.

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Who is it for?

Recommended For:

  • Working Professionals 

  • Graduates

Eligibility Criteria:

  • Anyone, whether a newcomer or a professional, willing to learn Data Science can opt for it.

  • No prior coding experience required..

  • Should be a motivated individual who is looking to get hired in the data science industry or switch their career in the field of data science.

  • What is Data?

  • What is Data Science?

  • What does Data Sc. Involve?

  • Tools of data Sc.

  • What is Machine Learning?

  • Where is Machine Learning used?

  • Job Roles

Unit 1 - Week 1 - Week 5

  • Introduction to probability

  • Sampling and sampling distributions

  • Unions of Events and Addition Rules

  • Unions of Events and Addition Rules

  • Conditional Probability

  • Independence

  • Random Variables

  • Baye'S Theorem

  • Average, Mean, Median, Mode

  • Range, Quartiles & Percentiles

  • Interquartiles Range


  • Variance & Standard Deviation

  • Statistical Inference

  • Normal Distribution

  • Standard Normal Distribution

  • Poisson Distribution

  • Bernoulli Distribution

  • Point Estimate

  • Confidence level & Confidence Intervals

  • Margin of Error

  • Population Means

  • Hypothesis Testing

  • Hypothesis Testing : Two Sample Test

  • Hypothesis Testing Proportion & Mean

  • Z- Test & T-Test

  • One-tailed and two-tailed tests

  • The F distribution

  • The chi-square distribution

  • The chi-square test of independence

Unit 2 - Week 6 - Week 15

  • Introduction to Python

  • Computer Programming Data Types

  • Variables & Data Types

  • Basic Input-Output Operations

  • Basic Operators

  • Boolean Values

  • Strings

  • Lists & Tuples

  • Dictionary

  • Sets

  • Conditional Expressions

  • Loops

  • Logical and Bitwise Operations

  • Functions & Recursions

  • File Input & Output

  • Exceptions handling

  • Object Oriented Programming

  • Virtual environment & Python libraries

  • Some special functions in python e.g Lambda, bin, format, map, filter, reduce

  • Web scraping

  • Ipython

  • Jupyter

  • NumPy Array

  • Sorting Array

  • NumPy Universal Functions

  • Array Indexing: Accessing Single Elements

  • Array Slicing: Accessing Subarrays

  • Reshaping of Arrays

  • Array Concatenation and Splitting

  • NumPy Arrays: Universal Functions

  • Aggregations: Min, Max, and Everything in Between

  • Sorting Arrays

  • Fast Sorting in NumPy: np.sort and np.argsort

  • Partial Sorts: Partitioning

  • Structured Data: NumPy’s Structured Arrays

  • Pandas Series Object

  • Pandas DataFrame Object

  • Pandas Index Object

  • Data Indexing and Selection

  • Operating on Data in Pandas

  • Handling Missing Data

  • Hierarchical Indexing

  • Combining Datasets: Concat and Append

  • Combining Datasets: Merge and Join

  • Aggregation and Grouping

  • Pivot Tables

  • Vectorized String Operations

  • Working with Time Series

  • Motivating query() and eval(): Compound Expressions

  • pandas.eval() for Efficient Operations

  • DataFrame.eval() for Column-Wise Operations

  • DataFrame.query() Method

  • 4. Visualization with Matplotlib

  • Line Plots

  • Scatter Plots

  • Scatter Plots with plt.scatter

  • Visualizing Errors

  • Density and Contour Plots

  • Histograms, Binnings, and Density

  • Customizing Plot Legends, Colorbars

  • Multiple Subplots

  • Text and Annotation

  • Configurations and Stylesheets

  • Three-Dimensional Plotting in Matplotlib

  • Basemap

  • Visualization with Seaborn

  • Matplotlib Vs. Seaborn

Unit 3 - Week 16 - Week 18

  • Introduction

  • Types of Machine Learning

  • Supervised Vs. Unsupervised Learning

  • Instance-Based Vs. Model-Based Learning

  • Insufficient quantity of Training Data

  • Nonrepresentative Training Data

  • Poor-Quality Data

  • Irrelevant Features

  • Overfitting the Training Data

  • Underfitting the Training Data

  • Supervised Learning & Types

  • Classifiction & Types

  • Regression & Types

  • Acuracy Metrics

  • Training a Binary Classifier

  • Performance Measures

  • Measuring Accuracy Using Cross-Validation

  • Confusion Matrix

  • Precision and Recall

  • Multiclass Classification

  • Error Analysis

  • Multilabel Classification

  • Multioutput Classification

  • Projection & Manifold Learning

  • Principal Component Analysis

  • Data wrangling

  • Gradient Descent

  • Polynomial Regression

  • Learning Curves

  • Ridge, Lasso Regression

  • Elastic Net & Early Stopping

  • Logistic Regression

  • Training and Cost Function

  • Sigmoid Probability

  • Accuracy Matrix

  • Decision Boundaries

  • Linear SVM & Classification

  • SVM : Linear Separability

  • SVM : Mathematical Representation

  • Nonlinear SVM & Classification

  • SVM Regression

  • Kernal Trick

  • Decision Function Classifier

  • Overfitting

  • Random Forest Classifier

  • Performance Measure Confusion Matrix & Cost Matrix

  • Estimating Class Probabilities

  • The CART Training Algorithm

  • Gini Impurity or Entropy?

  • Regularization Hyperparameters

  • Instability

  • Overview & Applications

  • Types of Unsupervised Learning

  • Overview

  • Hierarchical Clustering

  • Ensemble Learning

  • Bagging and Pasting with Scikit-Learn

  • Out-of-Bag Evaluation

  • Random Patches and Random Subspaces

  • Random Forests

  • AdaBoost

  • Gradient Boosting

  • XGBoost & Parameters

  • Model Selection

  • Overview of Time Series Modeling

  • Time Series Patterns & Types

  • White Noise

  • Stationarity

  • Removal of Non-Stationarity

  • Time Series Models

  • Purpose of Recommender Systems

  • Paradigms of Recommender Systems

  • Collaborative Filtering

  • Association Rule Mining

  • Text Mining: Overview & Significance

  • Application

  • Text Extraction & Preprocessing

Unit 4 - Week 18 - Week 20

  • Introduction

  • Why Tensorflow

  • Installation

  • Node Value

  • Linear Regression with Tensorflow

  • Gradient Descent

  • Training Machine Learning Program with Tensorflow API

  • Training Deep Neural Nets

  • Distributing Tensorflow across servers

  • Convolutional Neural Network

  • Recurrent Neural Network

  • Autoincoders

  • Reinforcement Learning

Unit 5 - Week 21 - Week 24

  • Quantitative Reasoning

  • Logical Reasoning 

  •  Critical Reasoning

  • Integrated Reasoning

  • Verbal Reasoning

  •  Integrated Reasoning

Unit 6 - Week 1 - Week 24

  • Case Studies

  • Capstone Projects

Unit 7 - Week 1 - Week 24

  • Resume building

  • Email etiquette

  • PowerPoint Presentation

  • Telephone etiquette

  • Linkedin profile building

  • GitHub profile making

  • Mock Interviews

Unit 8 - Week 1 - Week 24

Apply for our Deep Dive in Advanced Python Course.

Appear for your Data Science Aptitude test followed by a Personal Interview.

Selected Students will receive the ISA Track (a simplified version of the ISA Agreement) along with their Minimum Guaranteed CTC amount.

Students will have to pay a registration fees after signing the ISA Track.

Apply for the Premium Data Science Program

Start your Data Science Training

Train with us 24 weeks on 2 industry-relevant tools and 10+ Libraries.

Prepare for Mock Interviews and Placement Process.

Intern for 3 months from week 13- 24 as a part of your course amongst our 500+ Hiring Partners to gain relevant industry experience.

SkillEnable's Placement Fair

Tool focused interview preparation every weekend during the course.

Resume building, GitHub profile building, Cover Letter Preparation, Presentation skills, Business Communication Skills Training.

Personal Placement Mentor for each student.

Get referred to our 500+ Hiring Partners.

Land your Dream Job

Work with the best companies in the country

Program Road Map!(For full ISA)

Learn now pay later


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Learn now pay later


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Learn now pay later


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Placement with a Minimum Course Fee Activation Amount

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Course Details

Data Science Course with Python
Data Science Course with TensorFlow

Why Income Share Agreement is Unique & Helpful?

  • ISA  is an arrangement where you get an opportunity to Learn now and pay the full course fee only after you start earning a sustainable amount of income which is the minimum guaranteed CTC for us. 


  • ISA saves you from worrying about traditional education loans or using your savings and getting trapped in debts, in case, you do not get a high-paying job.


  • You do not have to pay us anything if you do not start earning with a minimum guaranteed CTC.


  • Course fee payment is directly linked to the outcome of the course.

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Get Certified in 180+ Hours!

Start Learning Today!

  • Globally recognized certification from SkillEnable.

  • Pay a part of the tuition fees only after you earn more than or equal to the Minimum Course Fee Activation Amount.

  • Earn between 4 LPA- 18 LPA.

  • 100% Live Online Training.

  • Taught by 15+ years of experienced Industry Experts.

  • Apply your skills with hands-on projects & Case Studies.

  • No prior coding experience is required to do this course. 

  • Industry Approved Certificate upon completion.

Learn now pay later

Top Companies that Hire From Us!

SkillEnable has 500+ Hiring partners who have hired our students for roles like Business Analyst, Data Analyst, Data Scientist, Data Associate, Python & ML Expert, Subject Matter Expert in Data Science, Product Managers, Junior Manager, Financial Analyst & Quality Analyst.

Join #1 Data Science Training & Placement Institution in India


SkillEnable's Minimum Guarantees:


What's better than guaranteed success?


At SkillEnable, promises come before payments. We charge fees only when you start earning equal to or more than the Minimum guaranteed CTC.

For Freshers

  • 4LPA - 18LPA


  • For Working Professionals

    10% - 25% hike or between 4LPA- 8LPA whichever is maximum

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Program Features!

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Registration & ISA Signing

Students selected by a final interview by experts will sign the ISA and start the course.

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Online Assessment & Interview

Students will go through an assessment on Quantitative, Critical Reasoning, and Interpersonal skills followed by an Interview.

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Filling an extensive application forms with academic, professional  and personal background.

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Document Verification 

Students will have to submit documents for verification for their credit profiling.

Our Selection Process

It's an Intensive and Elite 180 Hrs Program with an Extensive Selection Process.

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Get 100% Lifetime Placement Assistance ​

We provide lifetime placement assistance to all our students . Our Virtual hiring drive gives the opportunity to interview with SkillEnable's 500+ hiring partners ensuring the career one desires.

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