What You'll Learn
Grasp fundamentals of Data Science: data types, data wrangling, and exploration with Python.
Data Science and Machine Learning with Python Online Course using libraries like Matplotlib, Seaborn, and Plotly.
Apply machine learning algorithms: regression, classification, clustering, and ensemble methods.
Data Science and Machine Learning with Python Online Training Understand model evaluation metrics and tune hyperparameters effectively.
Use Python libraries such as Pandas, NumPy, and Scikit-learn for practical data processing.
Develop end-to-end machine learning pipelines and deploy models.
Machine Learning (Data Science and Deep Learning) with Python Training Objectives
- A massive library.
- Freedom from the platform.
- There is a lot of community support.
- Integration of Enterprise Applications
- Simplicity.
- TensorFlow and Keras are used to create Deep Learning / Neural Networks.
- Python data visualization using MatPlotLib and Seaborn.
- Learning should be transferred.
- Analyze the emotions.
- Recognition and classification of images.
- Analysis of regression.
- Clustering using K-Means.
- Principal Component Analysis (PCA)
- Python data visualization using MatPlotLib and Seaborn.
- Learning should be transferred.
- Analyze the emotions.
- Recognition and classification of images.
- The average salary for a data scientist is Rs.708,012.
- For less than a year of experience, an entry-level data scientist will receive about Rs.500,000 per year.
- Early-career computer scientists with 1 to 4 years of experience earn about Rs.610,811 a year.
- Machine Learning (Data Science and Deep Learning) with Python teaches you the methods used by real data scientists and machine learning professionals in the tech industry, preparing you for a future in this hot field.
- Create artificial neural networks using Tensorflow and Keras.
- Deep learning is used to classify photographs, details, and sentiments.
- Make forecasts using linear regression, polynomial regression, and multivariate regression.
- Data visualisation with MatPlotLib and Seaborn.
- Apache Spark's MLLib can be used to implement machine learning at a large scale.
- Learn about reinforcement learning and how to make a Pac-Man bot.
- You'll need a desktop machine (Windows, Mac, or Linux) that can run Anaconda 3 or later.
- The course will take you through the process of downloading and installing the necessary free software.
- It is essential to have a previous coding or scripting experience.
- Math skills equivalent to those used in high school would be expected.
- Machine Learning is the most important subfield of artificial intelligence.
- It causes computers to enter a self-learning mode in the absence of explicit programming.
- When presented with new data, these computers learn, evolve, improve, and build on their own.
- Machine Learning (Data Science and Deep Learning) with Python focuses on machine learning, Tensorflow, artificial intelligence, and neural networks—all of which are in high demand from the world's leading tech companies.
- Prototypes in data science should be studied and transformed.
- Create machine-learning applications.
- Investigate and incorporate effective machine learning algorithms and tools.
- Create machine learning software based on the specifications.
- Choose suitable datasets and data representation approaches.
- Conduct machine learning trials and evaluations.
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Benefits of Data Science and Machine Learning with Python Course
Data Science and Machine Learning with Python Certification Course to analyze complex datasets, build predictive models, and make data-driven decisions. Data Science and Machine Learning with Python Internship Data Scientist, ML Engineer, AI Specialist, and Business Analyst. Data Science and Machine Learning with Python Projects reinforce skills in Python programming, data analytics, and real-time model deployment.
- Designation
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Annual SalaryHiring Companies
About Your Data Science and Machine Learning with Python Training
Data Science and Machine Learning with Python Online Course covers data collection, cleaning, analysis, visualization, predictive modeling, and deployment. Data Science and Machine Learning with Python Course with Placement on both theoretical foundations and practical skills essential for solving business problems using Python’s rich ecosystem.
Top Skills You Will Gain
- Data wrangling
- Data visualization
- Supervised and unsupervised
- Model evaluation
- Feature engineering
- Python programming
- Building and deploying ML pipelines
- Statistical analysis
12+ Data Science and Machine Learning with Python Tools
Online Classroom Batches Preferred
No Interest Financing start at ₹ 5000 / month
Corporate Training
- Customized Learning
- Enterprise Grade Learning Management System (LMS)
- 24x7 Support
- Enterprise Grade Reporting
Not Just Studying
We’re Doing Much More!
Empowering Learning Through Real Experiences and Innovation
Data Science and Machine Learning with Python Course Curriculam
Trainers Profile
Trainers are certified professionals with 11+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Data Science and Machine Learning with Python Course are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Syllabus of Data Science and Machine Learning with Python Course Download syllabus
- 1. Introduction to the Course
- 2. Course Help and Welcome
- 3. Course FAQs
- 1. Python Environment Setup
- 1. Updates to Notebook Zip
- 2. Jupyter Notebooks
- 3. Optional: Virtual Environments
- 1. Welcome to the Python Crash Course Section!
- 2. Introduction to Python Crash Course
- 3. Python Crash Course - Part 1
- 4. Python Crash Course - Part 2
- 5. Python Crash Course - Part 3
- 6. Python Crash Course - Part 4
- 7. Python Crash Course Exercises - Overview
- 8. Python Crash Course Exercises - Solutions
- 1. Welcome to the NumPy Section!
- 2. Introduction to Numpy
- 3. Numpy Arrays
- 4. Quick Note on Array Indexing
- 5. Numpy Array Indexing
- 6. Numpy Operations
- 7. Numpy Exercises Overview
- 8. Numpy Exercises Solutions
- 1. Welcome to the Pandas Section!
- 2. Introduction to Pandas
- 3. Series
- 4. DataFrames - Part 1
- 5. DataFrames - Part 2
- 6. DataFrames - Part 3
- 7. Missing Data
- 8. Groupby
- 9. Merging Joining and Concatenating
- 10. Operations
- 11. Data Input and Output
- 1. Welcome to the Data Visualization Section!
- 2. Introduction to Matplotlib
- 3. Matplotlib Part 1
- 4. Matplotlib Part 2
- 5. Matplotlib Part 3
- 6. Matplotlib Exercises Overview
- 7. Matplotlib Exercises - Solutions
- 1. Introduction to Seaborn
- 2. Distribution Plots
- 3. Categorical Plots
- 4. Matrix Plots
- 5. Grids
- 6. Regression Plots
- 7. Style and Color
- 8. Seaborn Exercise Overview
- 9. Seaborn Exercise Solutions
- 1. Welcome to the Data Capstone Projects!
- 2. 911 Calls Project Overview
- 3. 911 Calls Solutions - Part 1
- 4. 911 Calls Solutions - Part 2
- 5. Bank Data
- 6. Finance Data Project Overview
- 7. Finance Project - Solutions Part 1
- 8. Finance Project - Solutions Part 2
- 9. Finance Project - Solutions Part 3
- 1. Welcome to the Machine Learning Section!
- 2. Supervised Learning Overview
- 3. Evaluating Performance - Classification Error Metrics
- 4. Evaluating Performance - Regression Error Metrics
- 5. Machine Learning with Python
- 1. Linear Regression Theory
- 2. Linear Regression with Python - Part 1
- 3. Linear Regression with Python - Part 2
- 4. Linear Regression Project Overview
- 5. Linear Regression Project Solution
- 1. Logistic Regression Theory
- 2. Logistic Regression with Python - Part 1
- 3. Logistic Regression with Python - Part 2
- 4. Logistic Regression with Python - Part 3
- 5. Logistic Regression Project Overview
- 6. Logistic Regression Project Solutions
- 1. KNN Theory
- 2. KNN with Python
- 3. KNN Project Overview
- 4. KNN Project Solutions
- 1. Introduction to Tree Methods
- 2. Decision Trees and Random Forest with Python.
- 3. Decision Trees and Random Forest Project Overview
- 4. Decision Trees and Random Forest Solutions Part 1
- 5. Decision Trees and Random Forest Solutions Part 2
- 1. Natural Language Processing Theory
- 2. NLP with Python - Part 1
- 3. NLP with Python - Part 2
- 4. NLP with Python - Part 3
- 5. NLP Project Overview
- 6. NLP Project Solutions
- 1. Welcome to the Deep Learning Section!
- 2. Introduction to Artificial Neural Networks (ANN)
- 3. Perceptron Model
- 4. Neural Networks
- 5. Activation Functions
- 6. Multi-Class Classification Considerations
- 7. Cost Functions and Gradient Descent
- 8. Backpropagation
- 9. TensorFlow vs Keras
- 10. TF Syntax Basics - Part One - Preparing the Data
- 11. TF Syntax Basics - Part Two - Creating and Training the Model
- 12. TF Syntax Basics - Part Three - Model Evaluation
- 13. TF Regression Code Along - Exploratory Data Analysis
- 14. TF Regression Code Along - Exploratory Data Analysis - Continued
- 15. TF Regression Code Along - Data Preprocessing and Creating a Model
- 16. TF Regression Code Along - Model Evaluation and Predictions
- 17. TF Classification Code Along - EDA and Preprocessing
- 18. TF Classification - Dealing with Overfitting and Evaluation
- 19. TensorFlow 2.0 Project Options Overview
- 20. TensorFlow 2.0 Project Notebook Overview
- 21. Keras Project Solutions - Dealing with Missing Data
- 22. Keras Project Solutions - Dealing with Missing Data - Part Two
- 23. Keras Project Solutions - Categorical Data
- 24. Keras Project Solutions - Data PreProcessing
- 25. Keras Project Solutions - Data PreProcessing
- 26. Keras Project Solutions - Creating and Training a Model
- 27. Keras Project Solutions - Model Evaluation
- 28. Tensorboard
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Industry Projects
Exam & Certification
- Participate and Complete One batch of Machine Learning (Data Science and Deep Learning) with Python Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Machine Learning (Data Science and Deep Learning) with Python Certification course
- Successful completion and evaluation of any one of the given projects
- Oracle Certified Associate (OCA)
- Oracle Certified Professional (OCP)
- Oracle Certified Expert (OCE)
- Oracle Certified Master (OCM)
- Learn About the Certification Paths.
- Write Code Daily This will help you develop Coding Reading and Writing ability.
- Refer and Read Recommended Books Depending on Which Exam you are Going to Take up.
- Join LearnoVita Online Training Course That Gives you a High Chance to interact with your Subject Expert Instructors and fellow Aspirants Preparing for Certifications.
- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.
Our learners
transformed their careers
A majority of our alumni
fast-tracked into managerial careers.
Get inspired by their progress in the Career Growth Report.
Our Student Successful Story
How are the Data Science and Machine Learning with Python Course with LearnoVita Different?
Feature
LearnoVita
Other Institutes
Affordable Fees
Competitive Pricing With Flexible Payment Options.
Higher Data Science and Machine Learning with Python Fees With Limited Payment Options.
Live Class From ( Industry Expert)
Well Experienced Trainer From a Relevant Field With Practical Data Science and Machine Learning with Python Training
Theoretical Class With Limited Practical
Updated Syllabus
Updated and Industry-relevant Data Science and Machine Learning with Python Course Curriculum With Hands-on Learning.
Outdated Curriculum With Limited Practical Training.
Hands-on projects
Real-world Data Science and Machine Learning with Python Projects With Live Case Studies and Collaboration With Companies.
Basic Projects With Limited Real-world Application.
Certification
Industry-recognized Data Science and Machine Learning with Python Certifications With Global Validity.
Basic Data Science and Machine Learning with Python Certifications With Limited Recognition.
Placement Support
Strong Placement Support With Tie-ups With Top Companies and Strong Mock Interviews.
Basic Placement Support
Industry Partnerships
Strong Ties With Top Tech Companies for Internships and Placements
No Partnerships, Limited Opportunities
Batch Size
Small Batch Sizes for Personalized Attention.
Large Batch Sizes With Limited Individual Focus.
Additional Features
Lifetime Access to Data Science and Machine Learning with PythonCourse Materials, Alumni Network, and Hackathons.
No Additional Features or Perks.
Training Support
Dedicated Mentors, 24/7 Doubt Resolution, and Personalized Guidance.
Limited Mentor Support and No After-hours Assistance.
Data Science and Machine Learning with Python Course FAQ's
- LearnoVita is dedicated to assisting job seekers in seeking, connecting, and achieving success, while also ensuring employers are delighted with the ideal candidates.
- Upon successful completion of a career course with LearnoVita, you may qualify for job placement assistance. We offer 100% placement assistance and maintain strong relationships with over 650 top MNCs.
- Our Placement Cell aids students in securing interviews with major companies such as Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, Sports One , Infosys, MindTree, and MPhasis, among others.
- LearnoVita has a legendary reputation for placing students, as evidenced by our Placed Students' List on our website. Last year alone, over 5400 students were placed in India and globally.
- We conduct development sessions, including mock interviews and presentation skills training, to prepare students for challenging interview situations with confidence. With an 85% placement record, our Placement Cell continues to support you until you secure a position with a better MNC.
- Please visit your student's portal for free access to job openings, study materials, videos, recorded sections, and top MNC interview questions.
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