Data Science & Machine Learning
Data Science & Machine Learning Training Program
Data scientist is one of the best suited professions to thrive this century. It is digital, programmingoriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been limited. Taking up Data Science Training in Delhi & Machine Learning Training will help you build a career in the growing field of Data Science.
Data Science & Machine Learning Training Program
Data science is a multidisciplinary field. It encompasses a wide range of topics.
Understanding of the data science field and the type of analysis carried out
 Mathematics
 Statistics
 Python
 Applying advanced statistical techniques in Python
 Data Visualization
 Machine Learning
 Deep Learning
Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.
We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.
Moreover, our focus is to teach topics that flow smoothly and complement each other.
Audience for this course:
This course is designed for anyone with basic understanding of working with a Computer. There is no major prerequisite.
Module 1: Introduction to Data Science & Machine Learning
 What will you learn in this course
 Understanding the field of Data Science
 Various Disciplines of Data Science
 Data Analysis & Data Analytics
 Business Analytics, Data Analytics & Data Science
 Understanding Business Intelligence, Machine Learning & Artifical Intelligence
 Breaking down Data Science into simple terms
Module 2: Understanding the field of Data Science
 Understanding Traditional Data
 Understanding Big Data
 Understanding Traditional Data Science
 Understanding Machine Learning
 Understand the differences in multiple Data Science Fields
 Popular tools in Data Science
 Career in Data Science
 Common misconceptions in the Data Science Field
Module 3: Probability
 Understanding basic probability
 Computing expected values
 Frequency
 Events and Complement
Module 4: Probability  Combinatorics
 Fundamentals of Combinatorics
 Permutations
 Simple operations
 Solving variations with repetition
 Solving variations without repetition
 Solving combinations
 Symmetry of combinations
 Seperate space samples
 Case Study: Combinatorics in real life
Module 5: Bayesian Inference
 Sets and Events
 Ways Sets can interact
 Sets Intersection
 Union of Sets
 Mutually exclusive Sets
 Dependence and Independence of Sets
 The Conditional Probability Formula
 The Law of Total Probability
 The Additive Rule
 The Multiplication Law
 Bayes’ Law
 Case Study: Bayesian Inference
Module 6: Distributions
 Fundamentals of Probability Distributions
 Types of Probability Distributions
 Characteristics of Discrete Distributions
 Discrete Distributions: The Uniform Distribution
 Discrete Distributions: The Bernoulli Distribution
 Discrete Distributions: The Binomial Distribution
 Discrete Distributions: The Poisson Distribution
 Characteristics of Continuous Distributions
 Characteristics of Continuous Distributions
 Continuous Distributions: The Normal Distribution
 Continuous Distributions: The Standard Normal Distribution
 Continuous Distributions: The Standard Normal Distribution
 Continuous Distributions: The Students’ T Distribution
 Continuous Distributions: The ChiSquared Distribution
 Continuous Distributions: The Exponential Distribution
 Continuous Distributions: The Logistic Distribution
 Case Study: Probability Distributions
Module 7: Statistics
 Introduction to Statistics
 Types of Data
 Levels of Measurement
 Categorical Variables – Visualization Techniques
 Categorical Variables – Visualization Techniques
 Numerical Variables – Frequency Distribution Table
 The Histogram
 Cross Tables and Scatter Plots
 Mean, median and mode
 Skewness
 Variance
 Standard Deviation and Coefficient of Variation
 Covariance
 Correlation Coefficient
 Correlation Coefficient Exercise
 Case Study: Descriptive Statistics
Module 8: Inferential Statistics Fundamentals
 Introduction to Statistics
 Types of Data
 Levels of Measurement
 Categorical Variables – Visualization Techniques
 Categorical Variables – Visualization Techniques
 Numerical Variables – Frequency Distribution Table
 The Histogram
 Cross Tables and Scatter Plots
 Mean, median and mode
 Skewness
 Variance
 Standard Deviation and Coefficient of Variation
 Covariance
 Correlation Coefficient
 Correlation Coefficient Exercise
 Case Study: Descriptive Statistics
Module 9: Inferential Statistics Fundamentals
 Understanding Confidence Intervals
 Confidence Intervals; Population Variance Known; zscore
 Confidence Interval Clarifications
 Student’s T Distribution
 Confidence Intervals; Population Variance Unknown; tscore
 Margin of Error
 Confidence intervals. Two means. Dependent samples
 Confidence intervals. Two means. Independent samples
 Case Study: Inferential Statistics
Module 10: Hypothesis Testing
 Null vs Alternative Hypothesis
 Rejection Region and Significance Level
 Type I Error and Type II Error
 Test for the Mean. Population Variance Known
 pvalue
 Test for the Mean. Population Variance Unknown
 Test for the Mean. Dependent Samples
 Test for the mean. Independent samples
 Case Study: Hypothesis Testing
Module 11: Introduction to Python
 Introduction to Programming
 Why Python?
 Why Jupyter?
 Installing Python and Jupyter
 Understanding Jupyter’s Interface – the Notebook Dashboard
 Prerequisites for Coding in the Jupyter Notebooks
 Python 2 vs Python 3
Module 12: Python Variables
 Variables
 Numbers and Boolean Values in Python
 Python Strings
 Python – Basic Python Syntax
 Using Arithmetic Operators in Python
 The Double Equality Sign
 How to Reassign Values
 Add Comments
 Understanding Line Continuation
 Indexing Elements
 Structuring with Indentation
Module 13: Python Operators
 Comparison Operators
 Logical and Identity Operators
 Python – Conditional Statements
 The IF Statement
 The ELSE Statement
 The ELIF Statement
 Boolean Values
Module 14: Python Functions
 Defining a Function in Python
 How to Create a Function with a Parameter
 Defining a Function in Python – Part II
 How to Use a Function within a Function
 Conditional Statements and Functions
 Functions Containing a Few Arguments
 Builtin Functions in Python
 Python Functions
Module 15: Python Sequences
 Lists
 Using Methods
 List Slicing
 Tuples
 Dictionaries
 For Loops
 While Loops and Incrementing
 Lists with the range() Function
 Conditional Statements and Loops
 Conditional Statements, Functions, and Loops
 How to Iterate over Dictionaries
Module 16: Advanced Python Tools
 Object Oriented Programming
 Modules and Packages
 What is the Standard Library?
 Importing Modules in Python
Module 17: Advanced Statistical Methods in Python

The Linear Regression Model

Correlation vs Regression

Regression Model



How to Interpret the Regression Table

Decomposition of Variability

What is the OLS?

RSquared
Module 18: Advanced Statistical Methods in Python  Multiple Linear regression with StatsModels
 Multiple Linear Regression
 Adjusted RSquared
 Test for Significance of the Model
 OLS Assumptions
 A1: Linearity
 A2: No Endogeneity
 A3: Normality and Homoscedasticity
 A4: No Autocorrelation
 A5: No Multicollinearity
 Dealing with Categorical Data – Dummy Variables
 Making Predictions with the Linear Regression
Module 19: Advanced Statistical Methods in Python  Multiple Linear regression with StatsModels
 What is sklearn and How is it Different from Other Packages
 Simple Linear Regression with sklearn
 Simple Linear Regression with sklearn – A StatsModelslike Summary Table
 Simple Linear Regression with sklearn –
 Multiple Linear Regression with sklearn
 Calculating the Adjusted RSquared in sklearn
 Feature Selection (Fregression)
 Creating a Summary Table with pvalues
 Feature Scaling (Standardization)
 Feature Selection through Standardization of Weights
 Predicting with the Standardized Coefficients
 Underfitting and Overfitting
 Train – Test Split Explained
Module 20: Advanced Statistical Methods  Logistic Regression
 Introduction to Logistic Regression
 A Simple Example in Python
 Logistic vs Logit Function
 Building a Logistic Regression
 Understanding Logistic Regression Tables
 Binary Predictors in a Logistic Regression
 Calculating the Accuracy of the Model
 Underfitting and Overfitting
 Testing the Model
Module 21: Advanced Statistical Methods  Cluster Analysis
 Introduction to Cluster Analysis
 Some Examples of Clusters
 Difference between Classification and Clustering
 Math Prerequisites
Module 22: Advanced Statistical Methods  KMeans Clustering

KMeans Clustering

A Simple Example of Clustering

Clustering Categorical Data

How to Choose the Number of Clusters

Pros and Cons of KMeans Clustering

To Standardize or not to Standardize

Relationship between Clustering and Regression

Market Segmentation with Cluster Analysis

How is Clustering Useful?
Module 23: Mathematics

What is a matrix?

Scalars and Vectors

Linear Algebra and Geometry

Arrays in Python – A Convenient Way To Represent Matrices

What is a Tensor?

Addition and Subtraction of Matrices

Errors when Adding Matrices

Transpose of a Matrix

Dot Product

Dot Product of Matrices

Why is Linear Algebra Useful?
Module 24: Deep Learning

Introduction to Neural Networks

Training the Model

Types of Machine Learning

The Linear Model (Linear Algebraic Version)

The Linear Model with Multiple Inputs

The Linear model with Multiple Inputs and Multiple Outputs

Graphical Representation of Simple Neural Networks

What is the Objective Function?

Common Objective Functions: L2norm Loss

Common Objective Functions: CrossEntropy Loss

Optimization Algorithm: 1Parameter Gradient Descent

Optimization Algorithm: nParameter Gradient Descent
Module 25: Deep Learning  TensorFlow 2.0: Introduction

How to Install TensorFlow 2.0

TensorFlow Outline and Comparison with Other Libraries

TensorFlow 1 vs TensorFlow 2

Types of File Formats Supporting TensorFlow

Outlining the Model with TensorFlow 2

Interpreting the Result and Extracting the Weights and Bias

Customizing a TensorFlow 2 Model
Module 26: Deep Learning  Digging Deeper

What is a Layer?

What is a Deep Net?

Digging into a Deep Net

NonLinearities and their Purpose

Activation Functions

Activation Functions: Softmax Activation

Backpropagation

Backpropagation picture

Backpropagation – A Peek into the
Mathematics of Optimization
Module 27: Deep Learning  Overfitting

What is Overfitting?

Underfitting and Overfitting for Classification

What is Validation?

Training, Validation, and Test Datasets

NFold Cross Validation

Early Stopping or When to Stop Training
Module 28: Deep Learning  Digging into Gradient Descent

Stochastic Gradient Descent

Problems with Gradient Descent

Momentum

Learning Rate Schedules, or How to Choose the Optimal Learning Rate

Learning Rate Schedules Visualized

Adaptive Learning Rate Schedules (AdaGrad and RMSprop )

Adam (Adaptive Moment Estimation)
Module 29: Deep Learning  Preprocessing

Preprocessing Introduction

Types of Basic Preprocessing

Standardization

Preprocessing Categorical Data

Binary and OneHot Encoding
Module 30: Deep Learning  Classifying on the MNIST Dataset

MNIST: The Dataset

MNIST: How to Tackle the MNIST

MNIST: Importing the Relevant Packages and Loading the Data

MNIST: Preprocess the Data – Create a Validation Set and Scale It

MNIST: Preprocess the Data – Shuffle and Batch

MNIST: Outline the Model

MNIST: Select the Loss and the Optimizer

MNIST: Learning

MNIST: Testing the Model
Module 31: Deep Learning  Real Life Example

Business Case: Exploring the Dataset and Identifying Predictors

Business Case: Outlining the Solution

Business Case: Balancing the Dataset

Business Case: Preprocessing the Data

Business Case: Load the Preprocessed Data

Business Case: Learning and Interpreting the Result

Business Case: Setting an Early Stopping Mechanism

Business Case: Testing the Model
Module 32: Deep Learning  Conclusion

Summary on What You’ve Learned

What’s Further out there in terms of Machine Learning

DeepMind and Deep Learning

An overview of CNNs

An Overview of RNNs

An Overview of nonNN Approaches
Module 33: Software Integration

What are Data, Servers, Clients, Requests, and Responses

What are Data Connectivity, APIs, and Endpoints?

Taking a Closer Look at APIs

Communication between Software Products through Text Files

Software Integration – Explained
Module 34: Case Studies
 Case Study – Preprocessing the ‘Absenteeism_data’
 Case Study – Applying Machine Learning to Create the ‘absenteeism_module’
 Case Study – Loading the ‘absenteeism_module’
 Case Study – Analyzing the Predicted Outputs
What's Included
 Complete Training
 Certificate of Completion
 Resume Building
 Placement Assistance
 Training Videos
Why Data Science & Machine Learning Training by Network Nuts?

Network Nuts provides Training Labs in Data Science and Machine Learning Training in Delhi.

We provide guaranteed job assistance with real job opportunities.

Need help with Resumes? We provide our students with sample resumes.

We use simple explanations to ensure all the students are able to understand the concepts.

We conduct in class quizes using mobile apps to keep the class interesting.

All our instructors work full time with us and have decades of industry experience.

We are the oldest Training Institute in New Delhi. Network Nuts provides the best Data Science and Machine Learning Training in Delhi.
Data Science & Machine Learning Course Details
 Duration: 50 Hours
 Weekdays: MondayThursday
 Weekends: SaturdaySunday
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