AI & Machine Learning Certification Course with AI & ChatGPT
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TechVidvan Online Machine Learning Training Course Curriculum
- 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
- 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
- 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
- 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
- Overview of Python operators
- Bitwise, arithmetic, and assignment operators
- Understanding number system conversions
- Identity operators and their uses
- Implementing conditional statements: if, else, elif
- Creating pyramid patterns using control statements
- Using transfer statements: break, continue, and pass
- Introduction to loops and their types
- Working with while loops and for loops
- Implementing nested for loops
- Understanding what strings are in Python
- Built-in string functions and methods
- String type casting and manipulation using Python’s string class
- Introduction to collections in Python
- Working with lists, tuples, sets, frozen sets, and dictionaries
- Understanding byte arrays and their operations
- 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
- Introduction to lambda functions
- Using filter(), map(), and lambda functions together
- Understanding arrays and their operations
- NumPy arrays, multidimensional arrays, and matrix operations
- The difference between views and copies in NumPy
- Implementing binary search and bubble sort algorithms in Python
- Understanding the difference between procedural and object-oriented programming
- Classes, objects, and self variables
- Polymorphism, operator overloading, inheritance, and constructor overloading
- Introduction to exception handling in Python
- Using try, except, finally blocks
- Writing user-defined exceptions
- File modes and operations (read, write, append)
- Working with CSV files and pickling in Python
- Using tell() and seek() methods to navigate files
- What is NumPy?
- NumPy Installation in Python
- NumPy Installation in PyCharm
- Different ways to create arrays
- Practical examples of array creation
- Attributes of NumPy ndarray
- Hands-on practice with array attributes
- 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
- 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
- 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
- What is Python Pandas
- Why Python Pandas
- Python Pandas Installation on Windows
- Python Pandas Installation on PyCharm
- How to Download Kaggle Dataset
- What is Series in Python Pandas
- Pandas Series Property
- Practical Implementation of Pandas Series Properties
- Mathematical Operations on Series in Pandas
- 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
- 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
- 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
- Pandas where() Function
- Practical Implementation of Pandas where() Function
- Pandas Groupby Method
- Practical Implementation of Pandas Groupby Method
- Pandas Aggregate Functions
- 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
- What is Matplotlib?
- Matplotlib Installation in PyCharm
- Installation of Matplotlib Library in Python
- How to Design a Chart
- 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
- Matplotlib Legend Function
- Practical Implementation of Legend Function
- Add Grid Lines in Chart using Matplotlib
- Apply Grid in Graph Plot in Matplotlib
- 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
- 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
- Draw Line Plot
- Draw Line Plot using Seaborn Github
- Types of Parameters in Line Plot
- Histogram Plot
- Displot
- ECDF Plot
- Bar Plot
- Heatmap Plot
- Scatter Plot
- Pair Plot
- Relationship Graph
- Seaborn E-Commerce Sales Visualization Project
- Seaborn Covid-19 Case Analysis Project
- Seaborn Movie Rating Explorer Project
- What is Statistics
- Understanding different types of data
- Measures of Central Tendency
- Coefficient of Variation
- Shape of Distribution
- Visualization Techniques
- Basics of Probability
- Conditional Probability and Bayes Theorem
- Variables and Their Types
- Probability Distribution
- Poission Distribution
- Standard Normal Distribution
- Exponential Probability Distribution
- Uniform Distribution
- Log Normal Distribution
- Central Limit Theorem
- Different sampling methods
- Hypothesis testing explained
- Types of errors in hypothesis testing
- Two-tailed tests
- Understanding p-value
- 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
- 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
- Introduction to algorithm types
- Basics of supervised learning
- Regression techniques in supervised learning
- 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
- Insurance Price Prediction
- Salary Prediction
- Home Price Prediction
- Sales Forecasting for a Retail Store
- Multiple Linear Regression Model to Predict Annual Tuition Fee
- Multiple Linear Regression Model to Predict Annual Tuition Fee GUI Based
- 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
- 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
- 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
- What is Random Forest Algorithm
- Loan Approval Classifier
- Tourist Destination Recommender based on Prefernces
- Credit Card Fraud Detection
- Digits Image Classification
- Iris Flower Prediction
- What is Gradient Boosting
- Salary Prediction based on Skills and Experience
- Diabetes Prediction
- Stock Price Prediction
- Student Dropout Risk Prediction
- Facial Expression Recognition
- What is XGBoost in Machine Learning and why it is powerful
- Insurance Claim Approval
- 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
- 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
- What is Computer Vision?
- Installing OpenCV with pip
- Installing OpenCV in PyCharm
- 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
- 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
- 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
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Your Machine Learning Journey Starts Here — Try before you Buy
Master Machine Learning from Scratch
Join our hands-on AI & Machine Learning course crafted by industry veterans and build real-world skills. It’s not just a course, it’s a job-ready bootcamp.|
Start 📅 25-May-2026 |
Schedule 🕗 6.30 PM IST (Mon-Fri) |
Access Duration 🕗 Lifetime Access |
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Enroll in Self-paced AI & Machine Learning Course
Key Features:
- ✓ Self-paced Expert-led Course
- ✓ Practical-based Curriculum
- ✓ Full Notes + Study Material
- ✓ Real-world Practicals
- ✓ Assessments to Test Skills
- ✓ Interview Ques of Top MNCs
- ✓ Real-time Live Projects
- ✓ Beginner to Advanced
- ✓ 3 years Access Duration
- ✓ Professional Certificate
Key Features:
- ✓ Everything in “PLUS” plus:
- ✓ Job-Ready Skill Training
- ✓ Live Industry Case Studies
- ✓ 1:1 Expert Counselling
- ✓ Dedicated Job Assistance
- ✓ Resume & Interview Prep
- ✓ Additional live Projects
- ✓ Latest Tools/Tech Covered
- ✓ Lifetime Course Access
- ✓ Learn with ChatGPT & AI
Key Features:
- ✓ Everything in “PRO” plus:
- ✓ LinkedIn, job portal optimization
- ✓ Career guidance from experts
- ✓ Lifetime Support
- ✓ Access to Job Portal
- ✓ Interview Ques of MAANG Companies
- ✓ After Job Support
- ✓ Lifetime Upgrades to latest version
- ✓ Industry-grade projects
- ✓ Enhanced Career Opportunities
Machine Learning Training Course Objectives
The online course on machine learning provides a thorough examination of the concepts, methods, and uses of machine learning. This Machine Learning training program, which is appropriate for both novices and seasoned experts, covers a broad range of subjects that are crucial for comprehending and applying machine learning techniques.
TechVidvan machine learning online course will cover basic ideas including reinforcement learning, supervised and unsupervised learning, and deep learning, giving participants a thorough understanding of machine learning algorithms and how to use them to solve issues in the real world. The best machine learning course also covers practical topics like feature engineering, hyperparameter tuning, and model evaluation, giving participants the tools they need to create and implement efficient machine learning models.
Participants will work on practical projects and exercises throughout the course to gain competency in machine learning techniques and apply theoretical knowledge to real-world problems. The ML online course intends to enable learners to become adept in machine learning and harness its powers to drive innovation and solve challenging challenges across diverse domains, with an emphasis on both theoretical understanding and practical application.
The goal of the machine learning training is to teach students a strong foundation in the ideas, procedures, and algorithms of machine learning. Participants will understand fundamental concepts including reinforcement learning and supervised and unsupervised learning through clear explanations and real-world applications.
Online machine learning programming course also places a strong emphasis on providing learners with practical experience so they can effectively apply machine learning techniques to datasets and scenarios from the real world. To ensure that participants have the information and abilities needed to create machine learning solutions that are impartial, transparent, and fair, the course also highlights the significance of ethical considerations and responsible AI practices.
All things considered, the goal of the machine learning certification course is to enable students to become experts in machine learning and capable of using these methods to tackle challenging problems in a variety of fields and spur creativity.
Why should you learn Machine Learning?
In this digital data-driven age, gaining knowledge of Machine Learning gives you unlimited opportunities and widens your scope of success. Here are some factual statements to prove the importance of Machine Learning-
- “Machine learning skills are among the most sought-after by employers.” – LinkedIn
- “Machine learning proficiency can lead to higher-paying jobs and career advancement opportunities.” – Glassdoor
- “Machine learning can help solve some of the world’s most pressing challenges, from healthcare to climate change.” – Sundar Pichai, CEO of Alphabet Inc. (Google)
- “90% of the world’s data has been created in the last two years.” – IBM
- “Machine learning algorithms are driving unprecedented advancements in artificial intelligence.” – Elon Musk, CEO of SpaceX and Tesla
What is Machine Learning?
A subset of artificial intelligence (AI) known as machine learning allows computers to learn from data and become more proficient at a task without the need for explicit programming. The ability of algorithms to find patterns and relationships in big datasets, which enables them to make predictions or choices based on fresh data, is the fundamental component of machine learning.
Fundamentally, machine learning is the process of creating mathematical models and algorithms that are capable of learning from their experiences, adjusting to new inputs, and making defensible decisions on their own. supervised learning, unsupervised learning, and reinforcement learning are a few different kinds of machine learning methodologies. Algorithms learn from labeled data in supervised learning, where each example is linked to a target outcome or label.
While reinforcement learning focuses on learning optimal behavior through trial and error, driven by feedback from the environment, unsupervised learning looks for patterns and structures in unlabeled data. Machine learning techniques are driving innovation and changing how businesses run with their wide range of applications across industries, including marketing, finance, healthcare, autonomous cars, and natural language processing.
What to do before you begin with Machine Learning Training?
Before starting this TechVidvan Machine Learning online journey, students are suggested to go through the prerequisites of this machine learning course. These prerequisites are not mandatory to follow. They are presented to guide you and help you learn from this online machine learning course more efficiently-
- Learn to program in a language like Python, which is great for data science and machine learning.
- Many machine learning methods are based on an understanding of basic mathematical principles including probability theory, calculus, and linear algebra.
- Gain a basic understanding of data analysis methods and resources because machine learning frequently entails sifting through massive datasets to find significant patterns.
- To establish a solid basis for more complex subjects, acquaint yourself with basic machine learning ideas such as supervised and unsupervised learning, model evaluation, and overfitting.
Who should go for this online Machine Learning training course?
If you are interested in machine learning and would like to increase your knowledge and proficiency in this area, this program is perfect for you. To be more precise, it works well with-
- Aspiring Data Scientists
- IT Graduates and Students
- Aspiring Software Engineer
- AI Enthusiasts
- Aspiring Project Managers
- Professional Healthcare Enthusiasts
- Aspiring Data Analysts
By enrolling in our Machine Learning training program, you can expect the following benefits:
Through a thorough exploration of machine learning’s nuances, participants in this program will become proficient in a number of areas related to this game-changing technology. They will gain a thorough grasp of the inner workings of machine learning algorithms and their practical applications by delving into foundational ideas including reinforcement learning, supervised and unsupervised learning, and deep learning.
Participants will also delve into more complex subjects including neural networks, generative adversarial networks (GANs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and neural networks, acquiring knowledge of state-of-the-art methods and approaches in the field of machine learning.
They will also gain knowledge of prejudice, fairness, and ethical issues in machine learning, preparing them to create morally sound and responsible machine learning solutions. All things considered, this machine learning course offers a thorough and hands-on learning environment that develops participants into skilled machine learning professionals who can take on challenging tasks and spur innovation in their fields.
Participants in the machine learning training receive a plethora of advantages, including invaluable knowledge, skills, and real-world experience in this quickly developing industry. Here are a few of the main advantages-
- By completing this ML online course, participants will have a firm grasp of machine learning principles, algorithms, and building blocks, which will enable them to develop sophisticated models and applications.
- Industry leaders with a wealth of machine learning experience are leading the program. They offer insightful advice, mentorship, and support at every stage of the learning process, guaranteeing that participants receive the best Machine Learning training.
- Completing this online ML training improves participants’ employment prospects by opening up diverse opportunities in data science, artificial intelligence, and machine learning engineering roles. Machine learning abilities are in high demand across sectors.
- By making connections with peers, mentors, and business leaders, participants can grow their professional networks and promote cooperation, knowledge exchange, and future job prospects.
- TechVidvan Machine Learning training program provides free ML course materials that participants can access at their own pace, enabling them to fit their studies around their current schedules and obligations.
- After completing the course successfully, participants receive a certificate attesting to their mastery of machine learning ideas and methods, which boosts their credibility and employability.
- By means of hands-on projects and exercises, participants enhance their skills in data analysis, model construction, and evaluation by gaining real-world experience in applying machine learning algorithms to datasets.
Jobs after Learning this Machine Learning online Course
Enrolling in online machine learning course can lead to numerous career prospects in a variety of industries. Following the training, you will be able to pursue the following positions-
- Data Scientist
To extract useful insights and patterns from massive amounts of data, data scientists use machine learning algorithms to examine the data. They create predictive models, streamline procedures, and provide information for business decisions in a variety of industries.
- Machine Learning Engineer
This field develops, builds, and implements machine learning models into operational systems. Their responsibilities include algorithm development, performance optimization, and model integration into platforms and applications.
- AI Engineer
A specialist in creating intelligent systems that are able to see, think, and act on their own is an artificial intelligence (AI) engineer. For applications like robotics, computer vision, and natural language processing, they develop and put into use algorithms.
- Data Analyst
To analyze data, spot trends, and produce insights that guide company plans and decision-making procedures, data analysts employ machine learning techniques. They collaborate with a range of stakeholders to glean insights from data that are useful.
- Research Scientist
Research scientists focus on expanding the field of machine learning through theoretical research, experimentation, and the development of novel algorithms and models. They support state-of-the-art machine learning research and innovation.
- Business Analyst
To find chances for business growth and optimization, business analysts use machine learning to examine operational data, customer behavior, and market trends. They offer recommendations and data-driven insights to stakeholders.
- Software Engineer
A software engineer with a machine learning focus creates systems and apps that use machine learning techniques to offer intelligent functionality. Machine learning capabilities are utilized in the design, development, and upkeep of software solutions.
- AI Product Manager
The creation and execution of AI-driven goods and services are under the direction of AI product managers. They prioritize features, establish product requirements, and guarantee the successful delivery of AI solutions by closely collaborating with cross-functional teams.
- Freelancer
As a machine learning expert, you can work on a range of projects for various clients by doing freelance work or hiring out. In order to provide knowledge on a project basis, you can provide services like data analysis, model development, and consultation.
Our students are working in leading organizations
Features of Machine Learning Online Training Course


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Machine Learning Online Training FAQs
Within the field of artificial intelligence, machine learning focuses on giving computers the ability to learn from data and gradually get better at what they do without needing to be explicitly taught.
Everyone with an interest in machine learning, including those with no prior programming expertise, programmers wishing to switch to data science, and experts trying to advance their knowledge of machine learning techniques and applications, should take this online machine learning course.
This machine learning training covers supervised and unsupervised learning, reinforcement learning, deep learning, neural networks, feature engineering, model evaluation, and practical applications of machine learning.
Python is the primary language used in the course to develop machine learning algorithms because of its ease of use, adaptability, and plenty of libraries like TensorFlow, scikit-learn, SciPy, Pandas and Numpy.
Although there aren’t any hard requirements, it would be helpful to have a basic understanding of Python and programming ideas. It would also be helpful to have a basic understanding of mathematics, including probability theory, calculus, and linear algebra.
The course takes a balanced approach, mixing projects and theoretical explanations with practical exercises and hands-on learning. Through coding exercises and projects, participants will acquire theoretical concepts and methods and apply them to real-world datasets.
Yes, participants will receive Machine Learning certification upon successfully completing the machine learning course requirements, which include quizzes, assignments, and projects. This ML certificate can be utilized to pursue their academic goals or to demonstrate their abilities and expertise to potential employers.
The length of the online Machine Learning course varies based on each student’s learning style and availability of time. With a few hours of study and practice each week, participants should be able to finish the ML course in a few weeks, since this is a self-paced course.
It’s true that students can get help from teachers or course assistants who are on hand to respond to inquiries, make clarifications, and give advice all during the course. In addition, there might be discussion groups or community forums where members can engage with each other and exchange perspectives.
Best machine learning online course gives students the opportunity to apply the knowledge and skills they have learned to solve real-world problems through projects and practical exercises that mimic real-world situations. Apart from the online ML course, participants are also encouraged to work on individual or group projects to further solidify their learning and develop a portfolio of completed projects that highlight their abilities.





