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    Fundamentals of Machine Learning with Python Implementation

    Posted By: Sigha
    Fundamentals of Machine Learning with Python Implementation

    Fundamentals of Machine Learning with Python Implementation
    Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 2.7 GB
    Genre: eLearning Video | Duration: 28 lectures (6 hour, 50 mins) | Language: English

    Learn Fundamentals of Machine Learning from scratch to make students well equipped with all basics and math involved

    What you'll learn

    Use Python for Data Science and Machine Learning
    Implement Machine Learning Algorithms
    Handle advanced techniques like Dimensionality Reduction
    Know which Machine Learning model to choose for each type of problem
    Basics of Reinforcement Learning
    Linear Regression
    Logistic Regression
    Clustering
    SVM
    Neural Network Concept
    Random Forest
    PCA and SVD


    Requirements

    Basics of python programming
    Linear algebra (understanding of matrices)
    Differential calculus (optional)

    Description

    Hello there! Welcome to Fundamentals of Machine Learning with Python Implementation. There are many courses available out there for this domain but what makes us different is that the learning in this class is gradual. All the concepts are built from scratch to give students a fair idea of how various algorithms work in addition to live demonstrations

    In this course, students will acquire a good understanding of basic concepts of machine learning. The course also introduces students to deep learning (neural nets) and also artificial intelligence. The concepts are developed from scratch to make students well equipped with all the basics and math involved with all machine learning algorithms

    Some concepts we cover include

    Various types of learning like supervised, unsupervised and reinforcement learning.

    Various supervised learning algorithms like linear and logistic regression.

    Clustering techniques.

    A brief introduction to Neural Nets.

    Parameter tuning, data visualization and accuracy estimation techniques

    Reinforcement learning techniques like Q-learning and SARSA

    Deciding which algorithm fits for a given problem


    Knowing all of these techniques will give an edge to the developer in order to solve many real world problems with high accuracy.

    Who this course is for:

    Anyone interested in Machine Learning.
    Students who want to start learning Machine Learning.
    Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
    Any students in college who want to start a career in Data Science.
    Any computer science student hoping to broaden their skillset
    Anyone interested in Machine Learning Including business leaders, managers, app developers, consumers - you!


    Fundamentals of Machine Learning with Python Implementation


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