Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS

13h 12m 31s
English
Paid

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!

Read more about the course

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.

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# Title Duration
1 Introduction 02:29
2 Introduction to Machine Learning 10:16
3 Statistics 101 09:34
4 Descriptive Statistics 06:37
5 Descriptive Statistics (Part-2) 14:22
6 Measures of Spread 11:11
7 Probability 12:03
8 Conditional Probability 05:56
9 Probability Distribution 13:31
10 Hypothesis Testing 15:31
11 Python Installation 10:07
12 Python IDE 12:46
13 Python_Basics 32:57
14 Python Basics II 41:39
15 Data Structures 57:32
16 Numpy 53:52
17 Pandas 52:40
18 Data Visualisation 46:57
19 Data Transformation 20:04
20 Machine Learning Intro 17:40
21 Linear Regression 01:03:26
22 Logistic Regression 45:54
23 KNN 39:52
24 NaГЇve Bayes 14:09
25 SVM 08:34
26 Decision Tree 28:29
27 K-means 18:07
28 Hierarchical Clustering 08:06
29 DBScan 11:22
30 Bagging 22:06
31 Boosting 17:13
32 PCA 27:35
33 Recommendations System 39:54

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