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    Machine Learning Practical Workout | 8 Real-World Projects

    BY
    Udemy

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    Mode

    Online

    Fees

    ₹ 449 3099

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 449  ₹3,099
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    INTRODUCTION TO THE COURSE [ QUICK WIN IN FIRST 10-12 MINS]

    • Updates on Udemy Reviews
    • Course Overview
    • BONUS: Learning Path
    • ML vs. DL vs. AI
    • ML Deep Dive
    • Download Course Materials
    • BONUS: ML vs DL vs AI
    • BONUS: 5 Benefits of Jupyter Notebook

    ANACONDA AND JUPYTER INSTALLATION

    • Download and Set up Anaconda
    • What is Jupyter Notebook
    • Install Tensorflow
    • How to run a Jupyter Notebook

    PROJECT #1: Artificial Neural Networks - CAR SALES PREDICTION

    • Introduction
    • Theory Part 1
    • Theory Part 2
    • Theory Part 3
    • Theory Part 4
    • Theory Part 5
    • Project Overview
    • Import Data
    • Data Visualization Cleaning
    • Model Training 1
    • Model Training 2
    • Model Evaluation

    PROJECT #2: DEEP NEURAL NETWORKS - CIFAR 10 CLASSIFICATION

    • Introduction
    • Theory Part 1
    • Theory Part 2
    • Theory Part 3
    • Theory Part 4
    • Problem Statement
    • Data Vizualization
    • Data Preparation
    • Model Training Part 1
    • Model Training Part 2
    • Model Evaluation
    • Save the Model
    • Image Augmentation Part1
    • Image Augmentation Part 2

    PROJECT #3: PROPHET TIME SERIES - CHICAGO CRIME RATE

    • Introduction
    • Project Overview
    • Import Dataset
    • Data Vizualization
    • Prepare the Data
    • Make Predictions

    PROJECT #4: PROPHET TIME SERIES - AVOCARDO MARKET

    • Introduction
    • Load Avocado Data
    • Explore Dataset
    • Make Predictions Part 1
    • Make Predictions Part 2 (Region Specific)
    • Make Prediction Part 2.1

    PROJECT #5: LE-NET DEEP NETWORK - TRAFFIC SIGN CLASSIFICATION

    • Introduction
    • Project Overview
    • Load Data
    • Data Exploration
    • Data Normalization
    • Model Training
    • Model Evaluation

    PROJECT #6: NATURAL LANGUAGE PROCESSING - EMAIL SPAM FILTER

    • Introduction
    • Naive Bayes Theory Part 1
    • Naive Bayes Theory Part 2
    • Spam Project Overview
    • Visualize Dataset
    • Count Vectorizer
    • Model Training Part 1
    • Model Training Part 2
    • Testing

    PROJECT #7: NATURAL LANGUAGE PROCESSING - YELP REVIEWS

    • Introduction
    • Theory
    • Project Overview
    • Load Dataset
    • Visualize Dataset Part 1
    • Visualize Dataset Part 2
    • Exercise #1
    • Exercise #2
    • Exercise #3
    • Apply NLP to Data
    • Apply Count Vectorizer to Data
    • Model Training Part 1
    • Model Training Part 2
    • Model Evaluation Part 1
    • Model Evaluation Part 2

    PROJECT#8: USER BASED COLLABORATIVE FILTERING - MOVIE RECOMMENDED SYSTEM

    • Introduction
    • Theory
    • Project Overview
    • Import Movie Dataset
    • Visualize Dataset
    • Collaborative Filter One Movie
    • Full Movie Recomendation

    Bonus Lectures

    • ***YOUR SPECIAL BONUS***

    Instructors

    Dr Ryan Ahmed
    Professor
    Udemy

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