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TechVidvan Online Machine Learning Training Course Curriculum
💻 Python
💻 Python for ML
💻 Statistics
💻 Data Science
1. Introduction to Python
- What is Python, and why should you learn it?
- Python’s broad range of applications
- Understanding platform-dependent vs. platform-independent languages
- Key features and limitations of Python
- A brief history of Python
2. Writing Your First Python Program
- Installing Python and setting up your environment
- Setting up PyCharm and understanding IDLE
- The flow of Python code execution
- Writing your first “Hello World” program
3. Python Data Types and Variables
- Understanding Python statements, indentation, and comments
- Using print() and taking user input
- Methods, variables, and identifiers in Python
- Python’s various data types and rules for declaring identifiers
4. Input and Output in Python
- Mastering input and output functions in Python
- Reading data in a single line
- Using the print() function with formatted strings and the format() method
- Exploring replacement operators in Python
5. Python Operators
- Overview of Python operators
- Bitwise, arithmetic, and assignment operators
- Understanding number system conversions
- Identity operators and their uses
6. Control Statements in Python
- Implementing conditional statements: if, else, elif
- Creating pyramid patterns using control statements
- Using transfer statements: break, continue, and pass
7. Loops in Python
- Introduction to loops and their types
- Working with while loops and for loops
- Implementing nested for loops
8. Strings in Python
- Understanding what strings are in Python
- Built-in string functions and methods
- String type casting and manipulation using Python’s string class
9. Collections in Python
- Introduction to collections in Python
- Working with lists, tuples, sets, frozen sets, and dictionaries
- Understanding byte arrays and their operations
10. Functions and Recursion in Python
- Defining and using functions in Python
- Function arguments, call by value, and returning values
- Passing functions as arguments
- Global variables, keywords, and the eval() method
- Implementing recursion through programs like factorial and Fibonacci series
- Understanding recursion in Python and how to reverse a number
11. Lambda Functions in Python
- Introduction to lambda functions
- Using filter(), map(), and lambda functions together
12. Arrays in Python
- Understanding arrays and their operations
- NumPy arrays, multidimensional arrays, and matrix operations
- The difference between views and copies in NumPy
13. Searching and Sorting in Python
- Implementing binary search and bubble sort algorithms in Python
14. Object-Oriented Programming in Python
- Understanding the difference between procedural and object-oriented programming
- Classes, objects, and self variables
- Polymorphism, operator overloading, inheritance, and constructor overloading
15. Exception Handling in Python
- Introduction to exception handling in Python
- Using try, except, finally blocks
- Writing user-defined exceptions
16. File Handling in Python
- File modes and operations (read, write, append)
- Working with CSV files and pickling in Python
- Using tell() and seek() methods to navigate files
1. Introduction to NumPy
- What is NumPy?
- NumPy Installation in Python
- NumPy Installation in PyCharm
2. NumPy Array Creation and Attributes
- Different ways to create arrays
- Practical examples of array creation
- Attributes of NumPy ndarray
- Hands-on practice with array attributes
3. NumPy Functions
- NumPy arange, linspace, logspace Methods
- Practical Implementation of NumPy arange, linspace, logspace Methods
- NumPy Array Zeros(), Ones(), Full() & Eye() Functions
- Practical Implementation of NumPy Array Zeros(), Ones(), Full() & Eye() Functions
4. NumPy Functions and Operations
- NumPy Array Comparison
- NumPy any(), all() & where() Functions
- NumPy Arithmetic Operations
- Practical Implementation of NumPy Arithmetic Operations
- NumPy Statistical Functions
- Practical Implementation of NumPy Statistical Functions
5. NumPy Concatenate and Split Array
- Reference vs View vs Copy in NumPy
- Practical Implementation of Reference vs View vs Copy in NumPy
- NumPy Array Concatenation
- Join NumPy Array Using Concatenate, Stack, VStack, HStack and Depth Method
- NumPy Splitting Array
- Practical Implementation of NumPy Splitting Array
6. Introduction to Python Pandas
- What is Python Pandas
- Why Python Pandas
- Python Pandas Installation on Windows
- Python Pandas Installation on PyCharm
- How to Download Kaggle Dataset
7. Pandas Series
- What is Series in Python Pandas
- Pandas Series Property
- Practical Implementation of Pandas Series Properties
- Mathematical Operations on Series in Pandas
8. DataFrames in Pandas Part – 1
- Pandas Dataframes
- How to Create Pandas DataFrame
- Create DataFrame using Excel, CSV and Clipboard in Pandas
- Different Ways to Create DataFrame
- Practical Implementation of DataFrame Creation
- Export Pandas DataFrame to CSV and Excel File
- DataFrame Attributes in Python Pandas
9. DataFrames in Pandas Part – 2
- DataFrame Slicing in Pandas
- Practical Implementation of DataFrame Slicing
- Sorting Python Pandas DataFrame in Ascending and Descending Order
- Drop Duplicate Values From Pandas DataFrame
- Handle Missing Data fillna & dropna in Pandas
- Pandas loc vs iloc
- Practical Implementation of Pandas loc vs iloc
- Ways to Filter Python Pandas
10. Pandas Advanced Data Analysis Part – 1
- Advanced Data Analysis using Pandas
- Pandas Join DataFrames
- Apply Join in Pandas DataFrame
- Join in Pandas Without a Common Column
- Practical Implementation of Join in Pandas Without a Common Column
- Concatenate DataFrames in Pandas
- Practical Implementation of Pandas DataFrames Concatenation
11. Pandas Advanced Data Analysis Part – 2
- Pandas where() Function
- Practical Implementation of Pandas where() Function
- Pandas Groupby Method
- Practical Implementation of Pandas Groupby Method
- Pandas Aggregate Functions
12. SQL in Python Pandas
- Pandas Equivalent SQL Queries
- Practical Implementation of Pandas SQL Queries
- isin() and not isin() Method in Pandas DataFrame
- Pandas nlargest() Function
- Insert, Delete, Update in Pandas DataFrames
13. Matplotlib Introduction
- What is Matplotlib?
- Matplotlib Installation in PyCharm
- Installation of Matplotlib Library in Python
- How to Design a Chart
14. Working with Matplotlib Part – 1
- Matplotlib Markers
- Types of Matplotlib Markers
- Line Properties in Matplotlib
- Change Line in Chart using Matplotlib
- Change Color and Font of Title, x-axis & y-axis of Chart Using Matplotlib
15. Working with Matplotlib Part – 2
- Matplotlib Legend Function
- Practical Implementation of Legend Function
- Add Grid Lines in Chart using Matplotlib
- Apply Grid in Graph Plot in Matplotlib
16. Create Beautiful Graphs using Matplotlib Part – 1
- Subplot in Matplotlib
- Practical Implementation fo Matplotlib Subplot
- sxticks(), yticks(), xlabel(), ylabel(), xlim(), ylim() Methods in Matplotlib
- Matplotlib Scatter Plot
- Practical Implementation of Matplotlib Scatter Plot
17. Create Beautiful Graphs using Matplotlib Part – 2
- Cmap and ColorBar in Scatter Plot
- Create Vertical & Horizontal Bar Graph
- Plot Multiple Bars in Single Bar Graph
- Create Pie Graph in Python
- Matplotlib Histogram Graph
18. Plot in Seaborn Part – 1
- Draw Line Plot
- Draw Line Plot using Seaborn Github
- Types of Parameters in Line Plot
- Histogram Plot
- Displot
- ECDF Plot
- Bar Plot
- Heatmap Plot
19. Plot in Seaborn Part – 2
- Scatter Plot
- Pair Plot
- Relationship Graph
20. Seaborn Projects
- Seaborn E-Commerce Sales Visualization Project
- Seaborn Covid-19 Case Analysis Project
- Seaborn Movie Rating Explorer Project
1. Introduction to Statistics
- What is Statistics
- Understanding different types of data
2. Descriptive Statistics
- Measures of Central Tendency
- Coefficient of Variation
- Shape of Distribution
- Visualization Techniques
3. Probability Theory
- Basics of Probability
- Conditional Probability and Bayes Theorem
- Variables and Their Types
4. Common Probability Distribution
- Probability Distribution
- Poission Distribution
- Standard Normal Distribution
- Exponential Probability Distribution
- Uniform Distribution
- Log Normal Distribution
5. Statistical Inferences
- Central Limit Theorem
- Different sampling methods
- Hypothesis testing explained
- Types of errors in hypothesis testing
- Two-tailed tests
- Understanding p-value
1. Data Science Essentials
- Introduction to Data Science
- How Data Science has evolved over the years
- Important terms and concepts you must know
- Difference between Data Science and AI/Machine Learning
- Converting business requirements into data-driven solutions
- Preparing and cleaning data for analysis
2. Data Science Workflow
- Defining the problem statement clearly
- Collecting data from reliable sources
- Processing and cleaning raw data
- Analysing datasets and building models
3. Roles in Data Science
- Data Scientist
- Data Analyst
- Data Engineer
- Machine Learning Engineer
- Business Intelligence Analyst
- Business Analyst
4. Industry Applications in Data Science
- Data Science in Finance and Banking
- Data Science in Retail
- Data Science in Health Care
- Data Science in Logistics and Supply Chain
- Data Science in the Technology Industry
- Data Science in Manufacturing
- Data Science in Agriculture
5. Introduction to Machine Learning
- What is Machine Learning – an easy-to-understand overview
- Difference between AI, ML, DL, and Data Science
- Evolution of ML and its objectives
- Formal definition of Machine Learning
6. Dive into Machine Learning
- Step-by-step Machine Learning process
- Categories of Machine Learning
- Structure and importance of datasets
- Types of datasets used in ML
- Machine Learning project life cycle explained
- Programming languages commonly used for ML
- Understanding labels and features
7. Types of ML Algorithms
- Introduction to algorithm types
- Basics of supervised learning
- Regression techniques in supervised learning
8. Machine Learning with Scikit-learn
- NumPy, Pandas, Matplotlib and Scikit-learn Installation in PyCharm
- Price Prediction Application
- Salary Prediction of Employees
- Marks Prediction of Students
- How to Visualize Predicted Value
9. Linear Regression Model for Multiple Variable
- Insurance Price Prediction
- Salary Prediction
- Home Price Prediction
- Sales Forecasting for a Retail Store
10. Multiple Linear Regression Model
- Multiple Linear Regression Model to Predict Annual Tuition Fee
- Multiple Linear Regression Model to Predict Annual Tuition Fee GUI Based
11. Machine Learning Models
- How to Save Trained Model in File using Pickle
- How to Save Trained Model in File using Joblib
- Dummy Varibales in Dataset
- Jupyter Notebook Installation
- Write ML Algorithm in Jupyter Notebook
- Split Data in Training and Test Data
12. Logistic Regression in Machine Learning
- What is Logistic Regression
- Insurance Prediction
- Employee Retention Prediction
- Digits Prediction
- Car Price Prediction
- Loan Prediction
- Admission Chance Predictor
- Admission Chance Predictor GUI Based
- Loan Approval Classifier using Logistic Regression
- Suspicious Login Detection using Logistic Regression
13. Decision Tree in Machine Learning
- What is Decision Tree?
- Practical Implementation of Decision Tree
- Iris Flower
- Tennis Game
- Titanic Movie
- College Admission Eligibility Predictor using Decision Tree
- Restaurant Preference Classifier using Decision Tree
- Flight Booking Cancellation Prediction using Decision Tree
14. Random Forest Algorithm in Machine Learning
- What is Random Forest Algorithm
- Loan Approval Classifier
- Tourist Destination Recommender based on Prefernces
- Credit Card Fraud Detection
- Digits Image Classification
- Iris Flower Prediction
15. Gradient Boosting in Machine Learning
- What is Gradient Boosting
- Salary Prediction based on Skills and Experience
- Diabetes Prediction
- Stock Price Prediction
- Student Dropout Risk Prediction
- Facial Expression Recognition
16. XGBoost in Machine Learning
- What is XGBoost in Machine Learning and why it is powerful
- Insurance Claim Approval
17. K-means Clustering in Machine Learning
- What is K-means Clustering in Machine Learning
- Customer Segmentation
- Grouping Lifestyle Habits to Predict Health Risk
- Youtube Video Clustreing by Views, Likes and Watch-time
18. Getting Started with Deep Learning
- What is Deep Learning
- What is a Neuron
- Deep Learning ANN Model – Hours Studied vs Exam Score
- Electricity Bill Estimator
- Heart Disease Predictor
- What is Loss Graph
- Student Placement Prediction
- Used Car Price Prediction using Vehicle Specifications
- Air Pollution Estimation
- Image Classification of Fashion Items
- Handwritten Digit Recognition
19. Introduction to OpenCV
- What is Computer Vision?
- Installing OpenCV with pip
- Installing OpenCV in PyCharm
20. Image and Video Processing in OpenCV
- OpenCV imread() & imwrite() Functions
- Create Copy of an Images
- How to Show, Resize and Rotate an Image in OpenCV
- Merge Multiple Images in OpenCV
- Flip an Image in OpenCV Using Bitwise Not Function
- Change Image Color Using cvtColor Function in OpenCV
21. Image and Video Processing in OpenCV
- Capture and Record Video Using OpenCV
- Capture Video From Webcam Camera in OpenCV
- Draw Line on Image & Video in OpenCV
- Draw a Circle and Put Text on Image in OpenCV
- add vs addWeighted Function in OpenCV
- Image Properties (Shape & Size) in OpenCV
- Crop an Image in OpenCV
22. Deep Dive into OpenCV
- OpenCV hconcat() & vconcat() Functions
- OpenCV Concatenating Images
- Blur an Image in OpenCV
- Graphical Image Rotation Application in OpenCV
- Show/Open Dynamic and Multiple Images in OpenCV
- Dynamically Resize Image in OpenCV
- Open Video using Dialog Box
- Resize Video and Image in OpenCV with Dimensions
- Draw Different Shapes in OpenCV
- OpenCV Image Transformations
- Convert BGR Image to RGB Image
- Color Conversion in OpenCV
- Use Different Color Codes in OpenCV
- OpenCV Canny Edge Detection
Tools & Technologies
Python
Seaborn
Hadoop
Jupyter
Scikit-Learn
Power BI
Anaconda
TensorFlow
AI
NumPy
OpenCV
ChatGPT
Pandas
NLTK
GenAI
Matplotlib
Apache Spark
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Course Objectives
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TechVidvan Data Science Course Objectives
Get ready to deep dive into the world of data!
In this Data Science training program, you will:
- Discover What is Data Science: Learn how data shapes our lives every day. Do you know that 90% of the world’s data was made in just last two years?
- Play with Real Data: Work on real-life projects, just like a real data scientist!
- Explore Machine Learning: Find how computers can learn and train from data and make smart guesses. It’s like teaching a computer to think!
- Use Cool Tools: Learn tools like Python and other special libraries that help you handle data easily.
- Solve Big Problems: Use data to find solutions to important problems in health, environment, etc.
- Build Your Own Projects: Create interesting projects that you can share with others and be proud of.
By the end of the online Data Science Certification course, you’ll have all the skills required to start your journey in data science.
Why should you learn Data Science?
Data Science is like having a special key to unlock secrets hidden in tons of information! Every day, we create huge amounts of data—every text message, every video watched, and every game played adds more and more.
By learning Data Science, you can turn this mountain of data into useful ideas. Imagine being able to predict what movies people will love, help doctors find better cures, or even make cool apps that everyone wants to use!
If you’re an engineering student or a professional new to data science, this field is full of exciting opportunities. You’ll learn how to teach computers to think on their own, find patterns that others might miss, and solve big problems that can make the world a better place.
- Unlock Hidden Secrets: Data Science helps you find patterns and answers hidden in big piles of data, just like a detective!
- High Demand for Skills: Companies all around the world need data scientists, so you can find exciting job opportunities.
- Solve Real Problems: Use data to make a difference in healthcare, the environment, technology, and more.
- Learn Cool Tools: Get hands-on with tools like Python, used by NASA and Google!
- Make Smart Decisions: Help businesses and organizations make better choices by understanding what the data tells us.
- Be Part of the Future: Data is growing every day—you can be at the front of new discoveries and innovations.
- Boost Your Problem-Solving Skills: Improve how you think and solve puzzles, which is helpful in everyday life.
- Versatile Career Paths: Work in many fields like sports, music, science, and more—wherever data is used!
- Create Amazing Projects: Build things like apps that recommend songs or predict weather patterns.
- Stand Out: Learning Data Science makes you unique and valuable in today’s technology-driven world.
What is Data Science?
Data Science, the hottest and highest-paid field on the planet, is like playing with data and generating hidden insights. Every day, world creates huge volume of information – photos, videos, social media, sensors, and more. We create 2.5 quintillion bytes of data ie 2.5 trillion GBs are made each day?
How can we handle all this data and generate meaningful information? That’s where Data Science comes into the picture. It helps us find hidden patterns and data-driven answers to drive business. Data scientists use the latest machine learning tools and techniques to identify patterns and generate relevant reports. The ultimate objective is to help businesses make the right decisions at the right time.
How your YouTube knows which video you might like, or how does Amazon suggest items you might want? That’s Data Science at work! It helps doctors find better treatments, makes games more fun, and even helps protect the environment.
In this Data Science online course, we’ll learn how to collect and understand data, use cool tools like Python, and solve real world problems. Join us, and let’s dive into the exciting world of Data Science together!
What to do before you begin?
Prerequisites of this Data Science Online Training Course
Great news! You don’t need to be a data expert to join us.
Here’s what you need:
- Basic Math Skills: If you can handle simple math like adding and subtracting, you’re good to go!
- Computer Know-How: Comfortable using a computer? Perfect! Browsing the internet and typing are all you need.
- Curiosity and Excitement: Bring your eagerness to learn and explore new things.
- Willingness to Learn Coding: Never coded before? No problem! We’ll start from the very beginning.
That’s it! No special experience or fancy degrees required. We’re here to help you every step of the way. So, are you ready to dive into the amazing world of Data Science with us?
Who should go for this Online Data Science Certified course?
This Data Science course is for anyone who is interested to know about data! If you’re:
- An Engineering Student: Discover how data can turn numbers into amazing solutions and solve real-world problems
- An Industry Professional: Add powerful skills to your toolbox to boost your work
- New to Data Science: We’ll guide you step by step on this exciting journey.
- A Curious Learner: Explore new ideas and answers to big questions
All you need is a desire and passion to learn and discover.
By enrolling in our Data Science training program, you can expect the following benefits:
Unlock a world of possibilities by joining this Best Data Science self paced course!
Here’s what you’ll gain:
- High-Demand Skills: Learn Data Science, one of the fastest-growing fields today. Companies everywhere need people who can analyse and use data.
- Hands-On Projects: Work on real-life problems just like a real data scientist. Imagine analyzing data to help save endangered animals or improve healthcare!
- Learn Cool Tools: Dive into Python and other powerful tools used by professionals. Did you know NASA uses Python for their space missions?
- Boost Your Career: Open doors to exciting jobs and opportunities. Data scientists are needed in every industry, from tech to sports.
- Solve Real-World Challenges: Use data to find answers to big questions about our world. Help make important decisions that can change lives.
- Build Your Own Portfolio: Create impressive projects that you can show to future employers or schools. Stand out from the crowd!
- Understand the World Better: Data is everywhere. Learn how to make sense of it and see patterns others might miss.
- Join a Learning Community: Connect with other curious minds. Share ideas, help each other, and make new friends.
- No Experience Needed: Start from the basics. Even if you’re new to data science, we’ll guide you every step of the way.
- Fun and Engaging Lessons: Enjoy learning through interactive activities and exciting challenges.
- Become a Data Detective: Find hidden clues in big piles of information. It’s like solving a mystery!
- Stay Ahead of the Curve: Be part of the future where data drives decisions in every field.
- Improve Problem-Solving Skills: Think critically and find smart solutions. These skills are useful everywhere!
- Flexible Learning: Learn at your own pace anytime and from anywhere. Fit the course into your busy schedule.
- Get Certified: Receive a certificate to show off your new skills. Add it to your resume or LinkedIn profile to show to employers.
- Make a Difference: Use what you learn to help others and make the world a better place.
Jobs after Learning TechVidvan Data Science Course
Great news! Learning Data Science opens the door to many amazing job roles.
Here are some job options you can look forward to:
- Data Scientist: Be the detective and find hidden answers from the data. Companies like Google and Amazon hire data scientists to improve their product quality.
- Data Analyst: Help businesses make smart decisions by studying data trends. Did you know that data analysts can help save companies millions of dollars?
- Machine Learning Engineer: Teach computers to learn from data. You could help create self-driving cars or smart home devices!
- Business Analyst: Use data to solve business problems and make companies better. Your ideas could change how businesses work.
- Data Engineer: Build systems that collect and store data. Imagine working with huge amounts of data like Facebook or YouTube!
- Artificial Intelligence Specialist: Work on cutting-edge AI projects. AI is used in healthcare, finance, and even space exploration!
- Statistician: Use math to understand data. You can help in sports, where statisticians help teams win games.
- Research Scientist: Explore new ways to use data in science. You could help discover new medicines or protect the environment.
- Data Visualization Expert: Turn complex data into easy-to-understand pictures. Help people see what the data means at a glance.
- Big Data Engineer: Handles massive amounts of data. Did you know that 90% of the world’s data was created in the last two years?
These jobs are in high demand, and companies are looking for people like you! With the skills you’ll learn in this online Data Science course, you’ll be ready to take on these exciting roles.
Start your journey now, and you could be the next big name in Data Science!
Our students are working in leading organizations
Features of Online Data Science Course
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Data Science Online Training FAQs
Do I need to know programming before joining TechVidvan Data Science course?
No worries! You don’t need any prior programming experience. We’ll start from scratch and teach you everything you need to know, especially using Python.
What materials or software will I need?
Just a computer with internet access! We’ll guide you on how to install free tools like Python and Jupyter Notebooks.
What if I’m not strong in math?
That’s okay! We’ll cover the basic math you need in a simple and easy-to-understand way. If you can handle basic algebra, you’ll do great.
Is this course suitable for complete beginners in Data Science?
Absolutely! This Data Science online course is designed for newcomers. We’ll explain everything in simple terms and build up your knowledge step by step.
Will there be hands-on Data Science projects to work on?
Yes! You’ll work on real-life projects like analyzing weather data or creating a simple movie recommendation system. It’s a fun way to learn by doing.
What kind of support will I have during the online Data Science course?
You’ll have access to helpful instructors and a community of fellow learners. We’re here to answer your questions and cheer you on!
Do I get a certificate after completing the Data Science Training course?
Yes, you’ll receive a certificate that you can share with employers or add to your resume to show off your new skills.
How will this Data Science Online Training help me in my career?
Data Science skills are in high demand. You’ll open doors to exciting jobs like Data Analyst, Machine Learning Engineer, and more.
Can I access the course materials after it ends?
Definitely! You’ll have ongoing access to all the lessons and resources so you can revisit them anytime.
How long is the Data Science course, and how much time should I dedicate each week?
The complete course duration is 90 hours. Plan to spend about 20 hours per week on lessons and projects to get the most out of it.
