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Course content

About Machine Learning AI

  • Join Our Online Classroom!
  • Exercise: Meet Your Classmates & Instructor
  • Asking Questions + Getting Help
  • What Is Machine Learning?
  • ZTM Resources
  • Exercise: Machine Learning Playground
  • How Did We Get Here?
  • Exercise: YouTube Recommendation Engine
  • Types of Machine Learning
  • Are You Getting It Yet?
  • What Is Machine Learning? Round 2
  • Section Review
  • Monthly Coding Challenges, Free Resources, and Guides
  • Section Overview
  • Introducing Our Framework
  • Types of Machine Learning Problems
  • Types of Data
  • Types of Evaluation
  • Features In Data
  • Modelling - Splitting Data
  • Modelling - Picking the Model
  • Modelling - Tuning
  • Modelling - Comparison
  • Overfitting and Underfitting Definitions
  • Experimentation
  • Tools We Will Use
  • Optional: Elements of AI
  • What is Conda?
  • Conda Environments
  • Mac Environment Setup
  • Mac Environment Setup 2
  • Windows Environment Setup
  • Windows Environment Setup 2
  • Linux Environment Setup
  • Sharing your Conda Environment
  • Jupyter Notebook Walkthrough
  • Section Overview
  • Downloading Workbooks and Assignments
  • Pandas Introduction
  • Series, Data Frames and CSVs
  • Data from URLs
  • Quick Note: Upcoming Videos
  • Describing Data with Pandas
  • Selecting and Viewing Data with Pandas
  • Quick Note: Upcoming Videos
  • Selecting and Viewing Data with Pandas Part 2
  • Manipulating Data
  • Manipulating Data 2
  • Manipulating Data 3
  • Assignment: Pandas Practice
  • How To Download The Course Assignments
  • NumPy Introduction
  • Quick Note: Correction In Next Video
  • NumPy DataTypes and Attributes
  • Creating NumPy Arrays
  • NumPy Random Seed
  • Viewing Arrays and Matrices
  • Manipulating Arrays
  • Manipulating Arrays 2
  • Standard Deviation and Variance
  • Reshape and Transpose
  • Dot Product vs Element Wise
  • Exercise: Nut Butter Store Sales
  • Comparison Operators
  • Sorting Arrays
  • Turn Images Into NumPy Arrays
  • Exercise: Imposter Syndrome
  • Assignment: NumPy Practice
  • Optional: Extra NumPy resources
  • Matplotlib Introduction
  • Importing And Using Matplotlib
  • Anatomy Of A Matplotlib Figure
  • Scatter Plot And Bar Plot
  • Histograms And Subplots
  • Subplots Option 2
  • Quick Tip: Data Visualizations
  • Plotting From Pandas DataFrames
  • Quick Note: Regular Expressions
  • Plotting From Pandas DataFrames
  • Scikit-learn Introduction
  • Quick Note: Upcoming Video
  • Refresher: What Is Machine Learning?
  • Quick Note: Upcoming Videos
  • Typical scikit-learn Workflow
  • Optional: Debugging Warnings In Jupyter
  • Getting Your Data Ready: Splitting Your Data
  • Quick Tip: Clean, Transform, Reduce
  • Getting Your Data Ready: Convert Data To Numbers
  • Note: Update to next video (OneHotEncoder can handle NaN/None values)
  • Getting Your Data Ready: Handling Missing Values With Pandas
  • Extension: Feature Scaling
  • Note: Correction in the upcoming video (splitting data)
  • Getting Your Data Ready: Handling Missing Values With Scikit-learn
  • NEW: Choosing The Right Model For Your Data
  • NEW: Choosing The Right Model For Your Data 2 (Regression)
  • Quick Note: Decision Trees
  • Quick Tip: How ML Algorithms Work
  • Choosing The Right Model For Your Data 3 (Classification)
  • Fitting A Model To The Data
  • Making Predictions With Our Model
  • predict() vs predict_proba()
  • NEW: Making Predictions With Our Model (Regression)
  • NEW: Evaluating A Machine Learning Model (Score) Part 1
  • NEW: Evaluating A Machine Learning Model (Score) Part 2
  • Evaluating A Machine Learning Model 2 (Cross Validation)
  • Evaluating A Classification Model 1 (Accuracy)
  • Evaluating A Classification Model 2 (ROC Curve)
  • Evaluating A Classification Model 3 (ROC Curve)
  • Reading Extension: ROC Curve + AUC
  • Evaluating A Classification Model 4 (Confusion Matrix)
  • NEW: Evaluating A Classification Model 5 (Confusion Matrix)
  • Evaluating A Classification Model 6 (Classification Report)
  • NEW: Evaluating A Regression Model 1 (R2 Score)
  • NEW: Evaluating A Regression Model 2 (MAE)
  • NEW: Evaluating A Regression Model 3 (MSE)
  • Machine Learning Model Evaluation
  • NEW: Evaluating A Model With Cross Validation and Scoring Parameter
  • NEW: Evaluating A Model With Scikit-learn Functions
  • Improving A Machine Learning Model
  • Tuning Hyperparameters
  • Tuning Hyperparameters 2
  • Tuning Hyperparameters 3
  • Note: Metric Comparison Improvement
  • Quick Tip: Correlation Analysis
  • Saving And Loading A Model
  • Saving And Loading A Model 2
  • Putting It All Together
  • Data Engineering Introduction
  • What Is Data?
  • What Is A Data Engineer?
  • What Is A Data Engineer 3?
  • What Is A Data Engineer 4?
  • Types Of Databases
  • Quick Note: Upcoming Video
  • Optional: OLTP Databases
  • Optional: Learn SQL
  • Hadoop, HDFS and MapReduce
  • Apache Spark and Apache Flink
  • Kafka and Stream Processing
  • Deep Learning and Unstructured Data
  • Setting Up With Google
  • Setting Up Google Colab
  • Google Colab Workspace
  • Uploading Project Data
  • Setting Up Our Data
  • Setting Up Our Data 2
  • Importing TensorFlow 2
  • Optional: TensorFlow 2.0 Default Issue
  • Using A GPU
  • Optional: GPU and Google Colab
  • Optional: Reloading Colab Notebook
  • Loading Our Data Labels
  • Preparing The Images
  • Turning Data Labels Into Numbers
  • Creating Our Own Validation Set
  • Preprocess Images
  • Preprocess Images 2
  • Turning Data Into Batches
  • Turning Data Into Batches 2
  • Visualizing Our Data
  • Preparing Our Inputs and Outputs
  • Optional: How machines learn and what's going on behind the scenes?
  • Building A Deep Learning Model
  • Building A Deep Learning Model 2
  • Building A Deep Learning Model 3
  • Building A Deep Learning Model 4
  • Summarizing Our Model
  • Evaluating Our Model
  • Preventing Overfitting
  • Training Your Deep Neural Network
  • Evaluating Performance With TensorBoard
  • Make And Transform Predictions
  • Transform Predictions To Text
  • Visualizing Model Predictions
  • Visualizing And Evaluate Model Predictions 2
  • Visualizing And Evaluate Model Predictions 3
  • Saving And Loading A Trained Model
  • Training Model On Full Dataset
  • Making Predictions On Test Images
  • Submitting Model to Kaggle
  • Finishing Dog Vision: Where to next?
  • What Is A Programming Language
  • Python Interpreter
  • How To Run Python Code
  • Latest Version Of Python
  • Our First Python Program
  • Python 2 vs Python 3
  • Exercise: How Does Python Work?
  • Learning Python
  • Python Data Types
  • How To Succeed
  • Numbers
  • Math Functions
  • DEVELOPER FUNDAMENTALS: I
  • Operator Precedence
  • Exercise: Operator Precedence
  • Optional: bin() and complex
  • Variables
  • Expressions vs Statements
  • Augmented Assignment Operator
  • Strings
  • String Concatenation
  • Type Conversion
  • Escape Sequences
  • Formatted Strings
  • String Indexes
  • Immutability
  • Built-In Functions + Methods
  • Booleans
  • Exercise: Type Conversion
  • DEVELOPER FUNDAMENTALS: II
  • Exercise: Password Checker
  • Lists
  • List Slicing
  • Matrix
  • List Methods
  • List Methods 2
  • List Methods 3
  • Common List Patterns
  • List Unpacking
  • None
  • Dictionaries
  • DEVELOPER FUNDAMENTALS: III
  • Dictionary Keys
  • Dictionary Methods
  • Dictionary Methods 2
  • Tuples
  • Tuples 2
  • Sets
  • Why Should You Learn Machine Learning AI Training?

    The annual salary of an Machine Learning AI is $125k.

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    What you will get at Zeblearnindia Learning?

    Zeblearnindia Learning is a premier institute offering training in SAP Online Courses, Web Designing, Data Science, Full-Stack Development, Salesforce, Workday, Machine Learning, Software Testing, and Video Editing. With the option to choose between online and offline (classroom) learning, our well-structured courses cater to students, working professionals, business owners, and entrepreneurs. Here’s what you can expect at Zeblearnindia Learning:

    •   Expert-Led Training
    •   Globally Recognized Certifications
    •   100% Job Placement Support
    •   Hands-On Learning
    •   Flexible Learning Options
    •   Affordable Course Fees
    •   Career Growth Opportunities
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    TRAINING FEATURES

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    Instructor-led Sessions

    Engage in live, interactive sessions led by industry experts, ensuring better guidance, monitoring, and flexible learning from any internet-enabled device.

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    Real-life Case Studies

    Gain practical insights through case studies based on real-world industry applications, helping bridge the gap between theory and practice.

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    Assignments

    Enhance your analytical skills and understanding with hands-on assignments designed to reinforce key concepts and practical application.

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    Lifetime Access

    Enjoy unrestricted access to course materials for a lifetime, allowing you to learn and revisit topics at your own pace.

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    24/7 Expert Support

    Get round-the-clock assistance from experienced mentors to resolve your queries and enhance your learning experience.

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    Certification

    Earn industry-recognized certification upon course completion, validating your skills and boosting your career prospects.

    We're Here to Help!

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    +91 7277877778
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    +91 8506888288
    Track Week Days Course Duration Fast Track
    Week Days 40-45 Days 2 Hrs. Per Day Online
    Course Duration 7 Weekends 3 Hrs. Per Day Online
    Fast Track 8 Days 6+ Hrs. Per Day Online

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    Students Reviews

    Explore authentic student feedback on ZebLearn’s expert-led SAP training and foreign language courses. Discover success stories from learners who earned certifications, enhanced their skills, and advanced their careers!

    Siddharth Kapoor
    Data Science Lead
    Zeblearn Indias Machine Learning AI training was an excellent learning experience. The course content was relevant and up-to-date, and the instructors were very knowledgeable. The practical exercises helped me gain a solid grasp of machine learning concepts.
    Arjun Singh
    AI Developer
    The Machine Learning AI course at Zeblearn India was exceptional. The training covered a wide range of topics, and the practical labs were particularly useful in understanding how to implement machine learning algorithms. This course has been a great boost to my career.
    Karan Joshi
    AI Specialist
    The Machine Learning AI course at ZebLearnIndia was transformative. The structured curriculum and practical projects allowed me to deepen my understanding of AI technologies and apply them to solve real-world challenges.
    Kavita Desai
    Project Manager
    The Machine Learning and AI course at Zeblearn India was very informative and practical. The trainers were knowledgeable and provided great insights. This course has been instrumental in my professional growth.
    Karan Desai
    Data Analyst
    The Machine Learning AI course at Zeblearn India was a fantastic experience. The training covered a wide range of topics, and the practical sessions were very beneficial in reinforcing the concepts. The skills I acquired have greatly enhanced my data analysis capabilities.
    Rajesh Kumar
    Software Engineer
    I found the Machine Learning AI course at ZebLearnIndia to be highly comprehensive and engaging. The in-depth coverage of machine learning techniques and real-world applications has equipped me with the skills needed to tackle complex AI problems.
    Aarav Singh
    Data Scientist
    The Machine Learning AI course at ZebLearnIndia was incredibly insightful. The practical approach to learning algorithms and model development has greatly enhanced my ability to build and deploy AI solutions effectively in my projects.
    Aarav Patel
    Machine Learning Engineer
    The Machine Learning AI training at Zeblearn India was phenomenal. The course content was thorough and covered a wide range of topics. The hands-on projects were particularly valuable in applying theoretical knowledge to real-world problems.
    Kritika Gupta
    Analytics Consultant
    The Machine Learning AI training at Zeblearn India was highly effective. The course was detailed and provided a deep dive into machine learning algorithms and techniques. The hands-on projects were especially beneficial in applying what I learned.
    Reena Mehta
    Data Analyst
    The Machine Learning and AI training I received at Zeblearn India was outstanding. The course content was relevant, and the instructors provided clear explanations. I now feel more confident in creating and deploying machine learning models.

    Machine Learning and AI Training Program - Flexible batches for you

    Date Type Schedule Time
    SOLD OUT 12 June 2025 Weekend SAT - SUN (08 Week) 18:00 To 20:00
    Filling Img 17 June 2025 Weekday MON - FRI (08 Week) 08:00 To 10:00
    22 June 2025 Weekend MON - FRI (08 Week) 10:00 To 00:00

    Price  1,20,000

    Now  95,000

    Enroll Now, Pay Later

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