Step by Step Guide to Machine Learning
4.2 (408 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
60,415 students enrolled

Step by Step Guide to Machine Learning

A beginners guide to learn Machine Learning including Hands on from scratch.
4.2 (408 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
60,415 students enrolled
Last updated 4/2020
English
Current price: $13.99 Original price: $19.99 Discount: 30% off
23 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 7 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Learn how to use NumPy to do fast mathematical calculations
  • Learn what is Machine Learning and Data Wrangling
  • Learn how to use scikit-learn for data-preprocessing
  • Learn different model selection and feature selections techniques
  • Learn about cluster analysis and anomaly detection
  • Learn about SVMs for classification, regression and outliers detection.
Course content
Expand all 16 lectures 06:49:24
+ Introduction to Machine Learning & Data Wrangling
3 lectures 01:22:00

Understanding Machine Learning - Supervised, Unsupervised Machine Learning Pipeline Common applications of machine learning

Preview 18:30

NumPy is fundamental to somebody getting started to machine learning or deep learning


Agenda

  • NumPy Creation

  • NumPy Access

  • NumPy hsplit, vsplit

  • NumPy hstack, vstack

  • NumPy Broadcasting

Essential NumPy
22:25

Everything that is required for doing machine learning in pandas

Agenda

  1. Introduction to Pandas

  2. Understanding Series & DataFrames

  3. Loading CSV,JSON

  4. Connecting databases

  5. Descriptive Statistics

  6. Accessing subsets of data - Rows, Columns, Filters

  7. Handling Missing Data

  8. Dropping rows & columns

  9. Handling Duplicates

  10. Function Application - map, apply, groupby, rolling, str

  11. Merge, Join & Concatenate

  12. Pivot-tables

  13. Normalizing JSON


Essential Pandas for Machine Learning
41:05
+ Linear Models, Trees & Preprocessing
3 lectures 02:14:24

Linear Models for Regression & Classification

Agenda

  1. Simple Linear Regression using Ordinary Least Squares

  2. Gradient Descent Algorithm

  3. Regularized Regression Methods - Ridge, Lasso, ElasticNet

  4. Logistic Regression for Classification

  5. OnLine Learning Methods - Stochastic Gradient Descent & Passive Aggressive

  6. Robust Regression - Dealing with outliers & Model errors

  7. Polynomial Regression

  8. Bias-Variance Tradeoff

Linear Models for Regression & Classification
51:35

PreProcessing using scikit-learn

Agenda

  1. Introduction to Preprocessing

  2. StandardScaler

  3. MinMaxScaler

  4. RobustScaler

  5. Normalization

  6. Binarization

  7. Encoding Categorical (Ordinal & Nominal) Features

  8. Imputation

  9. Polynomial Features

  10. Custom Transformer

  11. Text Processing

  12. CountVectorizer

  13. TfIdf

  14. HashingVectorizer

  15. Image using skimage

Pre-Processing Techniques using scikit
48:11

Introduction to Decision Trees -The Decision Tree Algorithms -Decision Tree for Classification -Decision Tree for Regression -Advantages & Limitations of Decision Trees

Agenda

  1. Introduction to Decision Trees

  2. The Decision Tree Algorithms

  3. Decision Tree for Classification

  4. Decision Tree for Regression

  5. Advantages & Limitations of Decision Tree

Decision Trees
34:38
+ Model Evaluation, Feature Selection & Pipelining
3 lectures 01:07:42

Model Selection & Evaluation

Agenda

  1. Cross Validation

  2. Hyperparameter Tuning

  3. Model Evaluation

  4. Model Persistance

  5. Validation Curves

  6. Learning Curves

Model Selection & Evaluation
29:55

Feature Selection Techniques

Agenda

  1. Introduction to Feature Selection

  2. VarianceThreshold

  3. Chi-squared stats

  4. ANOVA using f_classif

  5. Univariate Linear Regression Tests using f_regression

  6. F-score vs Mutual Information

  7. Mutual Information for discrete value

  8. Mutual Information for continues value

  9. SelectKBest

  10. SelectPercentile

  11. SelectFromModel

  12. Recursive Feature Elimination

Feature Selection Techniques
14:49

Composite Estimators using Pipeline & FeatureUnions

Agenda

  1. Introduction to Composite Estimators

  2. Pipelines

  3. TransformedTargetRegressor

  4. FeatureUnions

  5. ColumnTransformer

  6. GridSearch on pipeline

Composite Estimators using Pipelines & FeatureUnions
22:58
+ Bayes, Nearest Neighbours & Clustering
3 lectures 01:00:59

Naive Bayes

Agenda

  1. Introduction Bayes' Theorem

  2. Naive Bayes Classifier

  3. Gaussian Naive Bayes

  4. Multinomial Naive Bayes

  5. Burnolis' Naive Bayes

  6. Naive Bayes for out-of-core

Naive Bayes
20:24

Nearest Neighbors

Agenda

  1. Fundamentals of Nearest Neighbor

  2. Unsupervised Nearest Neighbors

  3. Nearest Neighbors for Classification

  4. Nearest Neighbors for Regression

  5. Nearest Centroid Classifier

Nearest Neighbors
19:55

Cluster Analysis

Agenda

  1. Introduction to Unsupervised Learning

  2. Clustering

  3. Similarity or Distance Calculation

  4. Clustering as an Optimization Function

  5. Types of Clustering Methods

  6. Partitioning Clustering - KMeans & Meanshift

  7. Hierarchical Clustering - Agglomerative

  8. Density-Based Clustering - DBSCAN

  9. Measuring Performance of Clusters

  10. Comparing all clustering methods

Cluster Analysis
20:40
+ SVM, Anomalies, Imbalanced Classes, Ensemble Methods
4 lectures 01:04:19

Anomaly Detection


Agenda

  • What are Outliers?

  • Statistical Methods for Univariate Data

  • Using Gaussian Mixture Models

  • Fitting an elliptic envelope

  • Isolation Forest

  • Local Outlier Factor

  • Using clustering method like DBSCAN

Anomaly Detection
17:41

Dealing with Imbalanced Classes

Agenda

  • What are imbalanced classes & their impact?

  • OverSampling

  • UnderSampling

  • Connecting Sampler to pipelines

  • Making the classification algorithm aware of Imbalance

  • Anomaly Detection


Handling Imbalanced Classes
13:17

Support Vector Machines

Agenda

  1. Introduction to Support Vector Machines

  2. Maximal Margin Classifier

  3. Soft Margin Classifier

  4. SVM Algorithm for Classification

  5. SVM

  6. SVM for Regression

  7. Hyper-parameters in SVM

Support Vector Machine
16:42

Ensemble Methods

Agenda

  • Understanding Ensemble Methods

  • RandomForest

  • AdaBoost

  • Gradient Boosting Tree

  • VotingClassifier

Ensemble Methods
16:39
Requirements
  • Basic knowledge of scripting and programming
  • Basic knowledge of python programming
Description

If you are looking to start your career in machine learning then this is the course for you.

This is a course designed in such a way that you will learn all the concepts of machine learning right from basic to advanced levels.

This course has 5 parts as given below:

  1. Introduction to Machine Learning & Data Wrangling

  2. Linear Models, Trees & Preprocessing

  3. Model Evaluation, Feature Selection & Pipelining

  4. Bayes, Nearest Neighbours & Clustering

  5. SVM, Anomalies, Imbalanced Classes, Ensemble Methods

For the code explained in each lecture, you can find a GitHub link in the resources section.

Who this course is for:
  • Beginners who want to become a data scientist
  • Software developers who want to learn machine learning from scratch
  • Python developers who are interested to learn machine learning
  • Professionals who want to start their career in Machine Leaning