Udemy

Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS

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  • 24,292 Students
  • Updated 11/2023
4.7
(514 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
13 Hour(s) 12 Minute(s)
Language
English
Taught by
Selfcode Academy
Rating
4.7
(514 Ratings)

Course Overview

Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS

Complete Machine Learning Course with Python for beginners

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!


Machine Learning (Complete course Overview)

Foundations

  • Introduction to Machine Learning

    • Intro

    • Application of machine learning in different fields.

    • Advantage of using Python libraries. (Python for machine learning).

  • Python for AI & ML

  • Python Basics

  • Python functions, packages, and routines.

  • Working with Data structure, arrays, vectors & data frames. (Intro Based with some examples)

  • Jupyter notebook- installation & function

  • Pandas, NumPy, Matplotib, Seaborn

  • Applied Stastistics

    • Descriptive statistics

    • Probability & Conditional Probability

    • Hypothesis Testing

    • Inferential Statistics

    • Probability distributions – Types of distribution – Binomial, Poisson & Normal distribution

Machine Learning

  • Supervised Learning

    • Multiple variable Linear regression

    • Regression

      • Introduction to Regression

      • Simple linear regression

      • Model Evaluation in Regression Models

      • Evaluation Metrics in Regression Models

      • Multiple Linear Regression

      • Non-Linear Regression

    • Naïve bayes classifiers

    • Multiple regression

    • K-NN classification

    • Support vector machines

  • Unsupervised Learning

    • Intro to Clustering

    • K-means clustering

    • High-dimensional clustering

    • Hierarchical clustering

    • Dimension Reduction-PCA

  • Classification

    • Introduction to Classification

    • K-Nearest Neighbours

    • Evaluation Metrics in Classification

    • Introduction to decision tress

    • Building Decision Tress

    • Into Logistic regression

    • Logistic regression vs Linear Regression

    • Logistic Regression training

    • Support vector machine

  • Ensemble Techniques

    • Decision Trees

    • Bagging

    • Random Forests

    • Boosting

  • Featurization, Model selection & Tuning

    • Feature engineering

    • Model performance

    • ML pipeline

    • Grid search CV

    • K fold cross-validation

    • Model selection and tuning

    • Regularising Linear models

    • Bootstrap sampling

    • Randomized search CV

  • Recommendation Systems

    • Introduction to recommendation systems

    • Popularity based model

    • Hybrid models

    • Content based recommendation system

    • Collaborative filtering

Additional Modules

  • EDA

    • Pandas-profiling library

  • Time series forecasting

    • ARIMA Approach

  • Model Deployment

    • Kubernetes

Capstone Project


If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!

Our Learner's Review: Excellent course. Precise and well-organized presentation. The complete course is filled with a lot of learning not only theoretical but also practical examples. Mr. Risabh is kind enough to share his practical experiences and actual problems faced by data scientists/ML engineers. The topic of "The ethics of deep learning" is really a gold nugget that everyone must follow. Thank you, 1stMentor  and SelfCode Academy for this wonderful course.


Course Content

  • 10 section(s)
  • 35 lecture(s)
  • Section 1 Foundation
  • Section 2 Introduction to Machine Learning
  • Section 3 Applied Statistics
  • Section 4 ntroduction to Python
  • Section 5 Let's dig Machine Learning
  • Section 6 Regression
  • Section 7 Classification
  • Section 8 Clustering
  • Section 9 Ensemble ML
  • Section 10 Our Project (Recomendation System)

What You’ll Learn

  • Master Machine Learning on Python
  • Make powerful analysis
  • Make accurate predictions
  • Make robust Machine Learning models
  • Use Machine Learning for personal purpose
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
  • Clean your input data to remove outliers


Reviews

  • k
    karley
    5.0

    All lectures were very easy to understand.

  • A
    Arkam
    5.0

    Wow! This was my first course and I am pretty suprised! This man is a great lecturer, he speaks so understandably! Thanks for everything,

  • R
    Rama swami
    5.0

    All lectures were very easy to understand.

  • J
    Joarn S.
    5.0

    Among the machine learning classes I've taken, this one is particularly noteworthy. The instructor offers first-rate assistance, and the material is thorough. Strongly advised

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