Machine Learning with Python
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1,211 students enrolled

Machine Learning with Python

Machine learning
0.0 (0 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.
1,211 students enrolled
Created by Ram Reddy
Last updated 3/2020
English
Price: $19.99
30-Day Money-Back Guarantee
This course includes
  • 9.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Make predictions using linear regression, polynomial regression, and multivariate regression
  • Master Machine Learning on Python
  • Make robust Machine Learning models
  • Have a great intuition of many Machine Learning models
Requirements
  • Python programming Language
Description

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

What is Machine learning

Features of Machine Learning

Difference between regular program and machine learning program

Applications of Machine Learning

Types of Machine Learning

What is Supervised Learning

What is Reinforcement Learning

What is Neighbours algorithm

K Nearest Neighbours classification

K Nearest Neighbours Regression

Detailed Supervised Learning

Supervised Learning Algorithms

Linear Regression

Use Case(with Demo)

Model Fitting

Need for Logistic Regression

What is Logistic Regression?

Ridge and lasso regression

Support vector Machines

Pre process of Machine learning data

ML Pipeline

What is Unsupervised Learning

What is Clustering

Types of Clustering

Tree Based Modeles

What is Decision Tree

What is Random Forest

What is Adaboost

What is Gradient boosting

stochastic gradient boostinng

What is Naïve Bayes


Who this course is for:
  • Any students in college who want to start a career in Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who want to create added value to their business by using powerful Machine Learning tools
Course content
Expand all 59 lectures 09:35:10
+ Machine Learning Introduction
8 lectures 49:38
ML04_01_Types of Machine Learning and Supervised Learning Introduction
16:46
ML05_01_UnSupervised Learning Introduction
03:11
ML06_01_reading _sklearn_ml_package_help_document part 1
01:48
ML07_01_reading _sklearn_ml_package_help_document part 2
07:39
ML08_01_Test Your Understanding
05:35
+ Working with Datasets
6 lectures 41:11
ML09_02_Explore Toy-Datasets
10:55
ML10_02_Explore iris Dataset
10:47
ML11_02_Similarly explore remaining toy datasets
06:06
ML12_02_Create DataFrame from sklearn Bunch
06:31
ML13_02_Create a Bunch with our own data
02:40
ML14_02_Create a Bunch with our own data part 2
04:12
+ k nearest neighbor algorithm
5 lectures 38:15
ML15_03_k nearest neighbor algorithm Maths
11:52
ML16_03_Find unknown sample quality based on known samples
09:40
ML17_03_Find unknown flower name based on known flower names using MS excel
09:12
ML18_03_Importance of n_neighbors
04:51
ML19_03_Hamming distance
02:40
+ KNN Estimator from Scratch
16 lectures 03:10:54
ML20_04_KNN Estimator from Scratch
12:50
ML21_04_Write code to Locate the most similar neighbors
11:06
ML22_04_Write code to Make a classification prediction with neighbors
03:07
ML23_04_High level End to End ML project Steps
07:03
ML24_04_Load csv file and Understand X and y Data
09:36
ML25_04_Split Data for training and testing
23:46
ML26_04_Train or fit the model
14:06
ML27_04_Predict labels of test data
13:52
ML28_04_Accuracy_of_the_Clasification_model
03:59
ML29_04_Hyper_Parameter_tunning
32:41
ML30_04_k means cross validation
11:53
ML31_04_GridSearchCV Hyper Parameter Tunning
20:31
ML32_04_RandomizedSearchCV Hyper Parameter Tunning
01:01
ML33_04_Save The model
09:51
ML34_04_Load The model
10:13
ML35_04_Home_Work
05:19
+ Linear Regression
15 lectures 02:22:16
ML36_05_Linear Regression Maths
07:51
ML37_05_Find weight of the baby based on age data understanding
10:18
ML38_05_Ordinary Least Squares
10:14
ML39_05_Find parameters using Ordinary Least Squares Function
08:21
ML40_05_Find parameters using sklearn
06:01
ML42_05_Find parameters using covar and var
04:44
ML43_05_Multivariate Linear Regression
09:14
ML44_05_Linear_regression_to find life span based on number of fertilities part
05:34
ML45_05_Linear_regression_to find life span based on number of fertilities part
08:38
ML46_05_Supervised_Regression_Metric_R2_score
14:50
ML47_05_Supervised_Regression_Metrics_RMSE
07:47
ML48_05_Life Span Predication
15:57
ML49_05_Linear Regression with Cross Validation or K-Fold
14:53
ML50_05_Linear Regression with Boston dataset
02:12
ML52_06_Logistic Regression Binary Clasification
15:42
+ ML51_06_Logistic Regression Maths
6 lectures 01:21:55
ML51_06_Logistic Regression Maths
13:47
ML52_06_Logistic Regression Binary Clasification
15:42
ML_53_06_Confusion Matrix
19:28
ML_54_06_Classification Report
10:31
ML_55_06_ROC Curve
11:06
ML_56_06_AUC Computation
11:21
+ Support Vector Machines Introduction
1 lecture 12:16
ML_60_07_upport Vector Machines Using Iris Toy Data set
12:16
+ Pre-processing of machine learning data Outliers
2 lectures 18:45
ML_62_08_Pre-processing of machine learning data Outliers
13:28
ML_64_08_Pre-processing Categorical Features
05:17