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Fast Track your career in Data Science with SkillEnable!

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:

  • Data Science Enthusiasts

  • Graduates

Eligibility Criteria:

  • Individuals with basics knowledge in Programming & Statistics

  • Should be ready for an extremely intensive and demanding program.

  • 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 Science Involve

  • Tools of data Science

  • 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

  • R-SQUARED

  • 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

  • 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 a scholarship of upto 50,000

Students will have to pay the course fee upfront or via financing.

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!

Learn now pay later

180+ HOURS OF LIVE EXHAUSTIVE TRAINING SESSIONS SPLIT OVER 12 WEEKS.

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100% LIVE ONLINE CLASSES

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40+ HOURS OF ADVANCED APTITUDE TRAINING AND 20+ HOURS OF HOLISTIC DEVELOPMENT TRAINING

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LEARN OVER 2 INDUSTRY RELEVANT TOOLS WITH 10+ LIBRARIES.

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ACTIVE JOB REFERRALS FROM THE BEST COMPANIES.

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3 MONTHS INTERNSHIP FOR FRESHERS AVAILABLE

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GLOBALLY RECOGNISED CERTIFICATION FROM SKILLENABLE

Course Details

Data Science Course with Python
Data Science Course with TensorFlow

Get Certified in 180+ Hours!

Start Learning Today!

  • Globally recognized certification from SkillEnable.

  • 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

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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.

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