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A python is an essential tool for implementing data science applications.
The Python for Data Science training course begins with an introduction to cloud computing and Python. Understand Flow Control and Command-Line and discuss Python scrips on Windows/Unix. Learn about basic programming in Python and different applications and companies where Python is used.
Learn about object-oriented concepts in this module. Get a detailed understanding of standard libraries, Python functions and Parameters, Lambda functions-their features and syntax, and Modules used in Python. Learn how to handle errors and exceptions.
Get complete knowledge of what Time Series Analysis (TSA) is. The module gets into the detail of the components and significance of TSA. Understand how to use TSA in Python. Learn how to implement Dicky-Fuller Test and understand how to convert non-stationary data to stationary.
The curriculum of the course is designed by industry professionals keeping in mind the real-world requirements of the corporate world. Other than general guidelines, you will have help with preparing a resume, potential interview questions, mock interviews, and a reliable certification to go with it.
Great learning experience through the platform. The curriculum is updated and covers all the topics. The trainers are experts in their respective fields and follow more of a practical approach.
I was astonished by the way of training material and learning methodology they were using for their courses was amazing. Totally worth your money and time.
You are a fresher interested in learning programming.
You do not have any experience in coding but are looking to start a career in IT.
You have a basic knowledge of Python.
You wish to become a Data Scientist.
12+ Years in Cloud
Microsoft, Adobe & AWS certified young and dynamic professional with 14+ years of industry experience. Trained 8200+ professionals, and counting, on all cloud technologies including AWS, Azure and GCP, DevOps, AEM, iOS, UI/UX, Digital Marketing & advanced Front End and Backend development technologies.
As a Data Engineer, I have 15+ yrs experience working on different cloud environments on various projects.I currently lead multi-work stream projects as a Cloud analyst and DevOps Engineer. Technologies I work on AWS, AZURE,Git hub, Jenkins, Ansible, Docker, Kubernetes ,Python ,Data Science On Azure and many more.
Engaging, understanding, and knowledgeable technical trainer with over 14+ years of experience in a variety of software programs and technologies including Data Science, Artificial Intelligence, Data Engineering, Business Intelligence AWS, Azure and GCP, DevOps, AEM, iOS, UI/UX.
Always learning and always yearning with experience of more than 10+ yrs, have delivered more than 200+ mentoring/ training sessions online on Microsoft Azure, Amazon web services, Cyber Security, Python, Tableau and Power BI to more than 20000 students.
1.1: Introduction to Cloud Computing
1.2: Overview of Python
1.3: The Companies using Python
1.4: Discuss Python Scripts on UNIX/Windows
1.5: Different Applications where Python is Used
1.6: Conditional Statements
1.7: Values, Types, Variables
1.9: Writing to the Screen
1.10: Operands and Expressions
1.11: Command Line Arguments
2.1: Python files I/O Functions
2.2: Strings and their related operations
2.3: Lists and their related operations
2.4: Tuples and their related operations
2.5: Sets and their related operations
2.6: Dictionaries and their related operations
3.2: Function Parameters
3.3: Modules Used in Python
3.4: Module Search Path
3.5: Lambda Functions
3.6: Global Variables
3.7: Variable Scope and Returning Values
3.8: Object-Oriented Concepts
3.9: Standard Libraries
3.10: Package Installation Ways
3.11: The Import Statements
3.12: Errors and Exception Handling
3.13: Handling Multiple Exceptions
4.1: Data Analysis
4.2: Pandas – data structures & index operations
4.3: Reading and Writing data from Excel/CSV formats into Pandas
4.4: NumPy – arrays
4.5: Operations on arrays
4.6: Reading and writing arrays on files
4.7: Indexing, slicing and iterating
4.8: Matplotlib library
4.9: Metadata for imported Datasets
4.10: Types of plots – bar graphs, pie charts, histograms
4.11: Contour plots
4.12: Markers, colors, fonts, and styling
4.13: Grids, axes, plots
5.1: Basic Functionalities of a data object
5.2: Merging of Data objects
5.3: Concatenation of data objects
5.4: Types of Joins on data objects
5.5: Exploring a Dataset
5.6: Analysing a dataset
6.1: Python Revision (NumPy, Pandas, scikit learn, matplotlib)
6.2: What is Machine Learning?
6.3: Machine Learning Categories
6.4: Machine Learning Process Flow
6.5: Machine Learning Use-Cases
6.6: Linear regression
7.1: What are Classification and its use cases?
7.2: What is a Decision Tree?
7.3: Creating a Perfect Decision Tree
7.4: Algorithm for Decision Tree Induction
7.5: Confusion Matrix
7.6: What is Random Forest?
8.1: Introduction to Dimensionality
8.2: Why Dimensionality Reduction
8.3: Factor Analysis
8.6: Scaling dimensional model
9.1: Illustrate how the Support Vector Machine works.
9.2: Hyperparameter Optimisation
9.3: What is a Support Vector Machine?
9.4: Implementation of Support Vector Machine for Classification
9.5: What is Naïve Bayes?
9.6: How Naïve Bayes works?
9.7: Implementing Naïve Bayes Classifier
9.8: Grid Search vs. Random Search
10.1: What is Hierarchical Clustering?
10.2: How does Hierarchical Clustering work?
10.3: What is K-means Clustering?
10.4: How does the K-means algorithm work?
10.5: How to do optimal clustering?
10.6: What is C-means Clustering?
10.7: What is Clustering & its Use Cases?
11.1: What are Association Rules?
11.2: Association Rule Parameters
11.3: Calculating Association Rule Parameters
11.4: Recommendation Engines
11.5: How do Recommendation Engines work?
11.6: Content-Based Filtering
11.7: Collaborative Filtering
12.1: What is Reinforcement Learning?
12.2: Why Reinforcement Learning?
12.3: Elements of Reinforcement Learning
12.4: Epsilon Greedy Algorithm
12.5: Exploration vs. Exploitation dilemma
12.6: Q – Learning
12.7: Q values and V values
12.9: Markov Decision Process (MDP)
13.1: What is Time Series Analysis?
13.2: Components of TSA
13.3: Importance of TSA
13.4: MA model
13.5: White Noise
13.6: AR model
13.7: ARIMA model
13.8: ARMA model
13.9: ACF & PACF
14.1: What is Model Selection?
14.2: Need for Model Selection
14.3: What is Boosting?
14.4: Types of Boosting Algorithms
14.5: Adaptive Boosting
14.6: How do Boosting Algorithms work?
15.1: What is Exploratory Data Analysis?
15.2: EDA Classification
15.3: EDA Techniques
15.4: Univariate Graphical EDA
15.5: Univariate Non-graphical EDA
15.6: Multivariate Graphical EDA
15.7: Multivariate Non-graphical EDA
15.8: Heat Maps
16.1: Data Visualisation
16.2: Business Intelligence tools
16.3: VizQL Technology
16.4: Connect to data from the Database
16.5: Connect to data from File
16.6: Chart Operations
16.7: Basic Charts
16.9: Combining Data
17.1: Trend lines
17.4: Reference lines
17.6: Using charts effectively
17.7: Geographic Maps
17.8: Publish to Tableau Online
17.9: Visual best practices
17.10: Story Points
Our tutors are real business practitioners who hand-picked and created assignments and projects for you that you will encounter in real work.
Resume Parsing aids in collating the crucial information in the resumes into labels or cardinal categories. Essentially, a resume parser converts the processes of resumes into a format that only has vital information, thereby making the recruiter’s work less tiring and more manageable.
The project aims to extract faces from images and identify/classify a person's face in images and videos. The project helps you understand how to extract frames from a video, train using faces, and identify where the classified person is located in a video or an image.
The project aims at providing personal hotel recommendations based on the choices and needs of the user. The dataset for this project comprises: User details such as user name, age, location, and booking history are included.
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