Statistics is a vital input in the field of data science. The lesson covers an introduction to statistics, descriptive V/S inferential statistics, variables and types of variables, and other important concepts.

After learning the basics of statistics, the lesson will now cover the advanced concepts of statistics. The learner will explore hypothesis testing, null and alternative hypotheses, type I error Vs type II error, P-value, T-test, about Anova, Chi-Square analysis, and other important concepts.

This lesson provides an introduction to Probability, why we need probability, concepts of simple probability, and the Bayes Theorem. The learner will also learn about independent & dependent events, distributions, skewness & kurtosis, and different techniques of sampling.

During this lesson, the learner will master different concepts of linear algebra such as scalars, vectors, matrices, and tensors with some basic properties of tensor arithmetic. Along with other important concepts like reduction, non- reduction, dot products, vector products, and matrix multiplication.

This module will give you the core idea of calculus and how calculus is an integral part of data science. Also, understand the derivatives and differentiation of calculus with visualization utilities. Get to know the partial derivatives And gradients chain rule.

Get complete exposure to what is linear regression in data science. Learn what is linear regression, the types of regression, its advantages and disadvantages, and lastly certain examples of linear regression.

Optimization is a tool to make the existing features make work more efficiently. The lesson will begin with an introduction to optimization and will proceed towards explaining the types of Optimization.

Learn what is python programming language, why python is important, and which industries work with python. Get to know the application and features of python in different industries and components of python.

Get In-depth knowledge of different built-in datatypes such as immutable data types, numeric data types, and mutable data types. We will further learn about the list, tuples, set, and dictionary.

Operators are used to perform different operations on variables and values. In this lesson, you'll master arithmetic, comparison, logical, bitwise, identity, and membership operator.

This lesson will take you through understanding what is regex functions with different python regex such as Metacharacters, Special Sequences, and Sets.

Learn about several different statements in Python such as If statement, If- Else statement, and nested IF statement in this lesson. Also, gain some practical knowledge by building a program to check the greatest among three numbers.

In Computer programming, loops are used to execute blocks of code multiple times without writing that code repeatedly. In this lesson, you'll learn While, For, and Nested Loops.

Functions are the block of code that can only run when it is called. In this Chapter, you'll learn different types of functions, Functions Arguments, how to create a Function, calling different Functions, then return values from a Function, and at last some function arguments.

Get a new perspective on problem-solving by using Recursion. Also Learn how to calculate factorial, Detect Palindromes and how to sort by using Quicksort.

In this lesson, you'll learn about Object-Oriented Programming ( OOP) in Python and its Classes and Objects. Also, there will be concepts of Polymorphism, Encapsulation, Inheritance, and Data Abstraction.

Introduce yourself to the core data structure of the python programming language by mastering different types of Data structures in python such as Built-In Data Structure and User-Defined Data Structure.

Errors detected during execution are called exceptions, In this lesson get a detailed overview of Exceptions Versus Syntax Errors, Try and except statements, Assertions, and how to raise an exception.

The lesson will let you play with numbers and statistical concepts. It covers statistical concepts like Mean, Mode, Median, Range, Standard Deviation, Variance, Quartile, IQR Covariance, Correlation between Data, Histogram, etc.

Unlike basics, this lesson will cover in-depth insight into Data science, a detailed overview of data analytics, and detailed insights into the concepts of business intelligence.

In the module of Data Science Fundamental, this lesson will give you an overview of why data science is a so booming field. The lesson covers the importance and benefits of Data science, its applications, and finally the growing future of Data science.

In the module of Data Science Fundamental, this lesson will give you an overview of why data science is a so booming field. The lesson covers the importance and benefits of Data science, its applications, and finally the growing future of Data science.

A Database is a collection of a large amount of organized data, stored for easy access of data. The lesson covers the overview of Databases, types of Databases, Database components, and finally advantages & disadvantages of Databases.

The lesson is the ultimate guide for all aspirants. It will guide the learners with the Data Science process, Machine Learning steps, and how to implement all the steps and processes practically to excel in the field.

Get introduced to the machine learning program and reinforcement learning. Details on the types and used cases of machine learning. This sub-module is designed to understand the machine learning modeling flow and explore the supervised & unsupervised learning with the challenges of ML.

Ensure complete knowledge of what is feature selection. Get to know the reasons why it is such an important part of ML. Procedures to choose a feature selection model. Understand various models and feature selection with python.

Learn the importance of feature scaling. The lesson gives you a good idea of the absolute maximum scaling in ML with the data ranging from min-max scaling to normalization, standardization, and robust scaling of data. Find out the reason if feature scaling is helpful.

Learners will explore the disciplines of business analytics and discover the Hypothesis Testing Part 1 and 2. The details of the Principal Component Analysis and Principal Component Analysis Case Study will clear the concepts of PCA.

All the details on Linear Discriminant Analysis (LDA), will be covered in this sub-module. This lesson also highlights important aspects of the statistical methods of quadratic discriminant analysis with extensions to linear discriminant analysis and common LDA applications.

It will be an informative experience to understand all about Linear Discriminant Analysis (LDA) with its assumptions. The sub-module will elaborate more on intuitions and mathematical descriptions of LDA. Learning the model parameters will add more value to your skills.

NumPy is one of the libraries of python that is used to work with arrays. Learn Arrays and Matrices, Array Indexing, Array Math, Inspecting A NumPy Array, NumPy With Conditional Expressions, and other important concepts.

Panda is a combination of two different libraries i.e. Matplotlib and NumPy. Learn various concepts in this module such as DataFrame in Pandas, Series object in pandas, Loading and handling data with Pandas, merging with joins, and much more.

Data visualization is the process of representing the data in the form of charts, graphs, and other visual designs to understand the data better. Learn what is Data Visualization, and Matplotlib, and use Matplotlib for Plotting Graphs.

The first lesson in the module includes a basic understanding of concepts, types of supervised learning, real-life applications, and the flow of supervised learning algorithms.

Linear Regression is using a straight line to find a relation between the independent and dependent variables. The lesson will cover Multiple Linear Regression, Lasso Regression, Ridge Regression, and other topics.

Logistic Regression is used to estimate the relationship between a dependent variable and one or more than one independent variables. This lesson will cover Sigmoid Function and Decision Boundary, different assumptions of logistic regression, advantages, and disadvantages, and other topics.

This module will cover Decision trees, classification, and differences in regression trees, Their advantages, and limitations. It will also have in-depth knowledge of how Classification and Regression trees work.

Get yourself ready to Set-up the environment of Decision Trees and Random Forest in this lesson. This module will cover all of the concepts like Decision Tree Metrices and Random Forest as Ensemble learning.

At the start of this lesson learn about support vector machines, their advantages, and disadvantages with some popular applications of SVMs, and Kernel functions. Also, Get a demonstration of Support Vector Machine in python.

This lesson will start with a complete In-depth understanding of K-Nearest Neighbor with complete insights into the Support vector machine.

Unsupervised learning is the machine learning technique algorithms that analyze untagged data. This sub-module will help you understand the common approaches to unsupervised learning. You will understand the different applications and challenges of unsupervised learning.

Spherical K-Means is an unsupervised algorithm that maintains the length of the vector parameters which may differ the direction but not the magnitude. Understand the types of clustering. Get introduced to spherical k-means clustering & Incremental Clustering.

K-means clustering is a refined method that clusters the analysis in data mining & statistics. This lesson will help you find out applications of k-means clustering with distance measure tasks. Get engaged with the details and demo on the k-means clustering.

Hierarchical Agglomerative Clustering is a free machine learning algorithm used to group untagged datasets into a cluster. It is the most important method in the clustering process. This chapter will give you more exposure to the process of agglomerative hierarchical clustering and Density-Based Spatial Clustering (DBSCAN).

Mean-sift clustering is an ace data clustering algorithm used in image processing and computer vision. Get to know the important aspects of kernel density estimation, mean shift, and implementations in Python.

Probabilistic Latent Semantic Analysis (PLSA) is a statistical technique for the analysis of semantic data. You will learn the concepts and applications of sequence pattern mining with different types of sequence learning pattern mining patterns.

The Association mining rule is a process of observing similar patterns or connections from datasets found in different datasets like a transactional or relational database. It will be an informational experience to attend the session on understanding the types and algorithms of association mining rules in data mining.

In this lesson, you'll learn about the basics of cloud computing, its features, Cloud services with pricing, and how you can easily build a business case for the cloud.

Start with the main concepts of Machine learning, different types of machine learning tools, and some cloud-based services like Amazon Web Service, Microsoft Azure, and Google Cloud Platform.

Start with the basic concepts of deep learning, the History of Deep learning, Challenges, Applications, and the difference between Machine Learning and Deep Learning. Also, you'll learn about the general flow of deep learning projects.

Get yourself introduced to Tensorflow, the Hello World Program of TensorFlow, and Deep Neural Networks. This Module will also cover Linear and Logistic Regression With TensorFlow.

In this lesson on Deep Learning, you will understand how Computer Vision and Convolutional Neural Network has evolved, covering CNN design and Architecture, Convolutional layer, Padding, Stride, and different layers. Also, you'll work on Feature detectors and Feature maps.

This lesson will start with an Introduction to CNN and how CNN recognizes different images. Also, there will be a detailed introduction about the layers in a Convolutional Neural Network with some use case implementation using CNN.

Learn about one of the best Deep Learning architectures yet! This lesson will teach you about the architecture of RNN, backpropagations in RNN, and Some of its applications and problems. You'll also learn when to use Long short-term memory ( LSTM).

Open source computer vision library is an open source that includes numerous computer vision algorithms. You can probably do all the computer vision tasks in a wide range. Learn the technics of the tool that perform multiple image processing functions.

Signal processing is a technique that shapes content in signals to provide the ASR (Automated speech recognition). It helps to filter the data from speech signals and translate it into words. Get the opportunity to get details on signal processing on GPUs and FPGAs and how signal processing will forefront data analysis.

In this lesson you will end up experiencing Scipy, it is all about the fundamental algorithms for scientific computing in Python. All important aspects related to Scipy will be covered in this sub-module.

Natural language processing (NLP) is an important branch of AI. It is related to the interaction system between the computer and human language. This class will throw light on text mining and NLP details.

This will be an interesting lesson targeting language models. A language model is a machine learning model designed to equate the language domain. Language model Importance and various uses of LM. You will be getting a good idea of how language models work.

Vector semantics is a model that explains various prospects of words such as word similarity, word senses, grammatical explanations, etc. It will be an engaging session to know why the vector model is important and what is TF-IDF.