Machine Learning with Python
Machine Learning with Python, available at $19.99, has an average rating of 3.88, with 72 lectures, based on 4 reviews, and has 1220 subscribers.
You will learn about 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 This course is ideal for individuals who are Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who want to create added value to their business by using powerful Machine Learning tools It is particularly useful for Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who want to create added value to their business by using powerful Machine Learning tools.
Enroll now: Machine Learning with Python
Summary
Title: Machine Learning with Python
Price: $19.99
Average Rating: 3.88
Number of Lectures: 72
Number of Published Lectures: 72
Number of Curriculum Items: 72
Number of Published Curriculum Objects: 72
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
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
Calculation using weather dataset
Entropy Calculation using weather dataset
Trees Entropy and Gini Maths Introduction
Pipeline with SimpleImputer and SVC
Pipeline with feature selection and SVC
Dropping Missing Data
Regression with categorical features using ridge algorithm
processing Categorical Features part2
processing Categorical Features
processing of machine learning data Delete Outliers
processing of machine learning data Outliers
Course Curriculum
Chapter 1: Machine Learning Introduction
Lecture 1: ML01_01_Machine Learning Introduction and Defination
Lecture 2: Ml02_01_ETP_Defimation
Lecture 3: ML03_01_Applications of ML
Lecture 4: ML04_01_Types of Machine Learning and Supervised Learning Introduction
Lecture 5: ML05_01_UnSupervised Learning Introduction
Lecture 6: ML06_01_reading _sklearn_ml_package_help_document part 1
Lecture 7: ML07_01_reading _sklearn_ml_package_help_document part 2
Lecture 8: ML08_01_Test Your Understanding
Chapter 2: Working with Datasets
Lecture 1: ML09_02_Explore Toy-Datasets
Lecture 2: ML10_02_Explore iris Dataset
Lecture 3: ML11_02_Similarly explore remaining toy datasets
Lecture 4: ML12_02_Create DataFrame from sklearn Bunch
Lecture 5: ML13_02_Create a Bunch with our own data
Lecture 6: ML14_02_Create a Bunch with our own data part 2
Chapter 3: k nearest neighbor algorithm
Lecture 1: ML15_03_k nearest neighbor algorithm Maths
Lecture 2: ML16_03_Find unknown sample quality based on known samples
Lecture 3: ML17_03_Find unknown flower name based on known flower names using MS excel
Lecture 4: ML18_03_Importance of n_neighbors
Lecture 5: ML19_03_Hamming distance
Chapter 4: KNN Estimator from Scratch
Lecture 1: ML20_04_KNN Estimator from Scratch
Lecture 2: ML21_04_Write code to Locate the most similar neighbors
Lecture 3: ML22_04_Write code to Make a classification prediction with neighbors
Lecture 4: ML23_04_High level End to End ML project Steps
Lecture 5: ML24_04_Load csv file and Understand X and y Data
Lecture 6: ML25_04_Split Data for training and testing
Lecture 7: ML26_04_Train or fit the model
Lecture 8: ML27_04_Predict labels of test data
Lecture 9: ML28_04_Accuracy_of_the_Clasification_model
Lecture 10: ML29_04_Hyper_Parameter_tunning
Lecture 11: ML30_04_k means cross validation
Lecture 12: ML31_04_GridSearchCV Hyper Parameter Tunning
Lecture 13: ML32_04_RandomizedSearchCV Hyper Parameter Tunning
Lecture 14: ML33_04_Save The model
Lecture 15: ML34_04_Load The model
Lecture 16: ML35_04_Home_Work
Chapter 5: Linear Regression
Lecture 1: ML36_05_Linear Regression Maths
Lecture 2: ML37_05_Find weight of the baby based on age data understanding
Lecture 3: ML38_05_Ordinary Least Squares
Lecture 4: ML39_05_Find parameters using Ordinary Least Squares Function
Lecture 5: ML40_05_Find parameters using sklearn
Lecture 6: ML42_05_Find parameters using covar and var
Lecture 7: ML43_05_Multivariate Linear Regression
Lecture 8: ML44_05_Linear_regression_to find life span based on number of fertilities part
Lecture 9: ML45_05_Linear_regression_to find life span based on number of fertilities part
Lecture 10: ML46_05_Supervised_Regression_Metric_R2_score
Lecture 11: ML47_05_Supervised_Regression_Metrics_RMSE
Lecture 12: ML48_05_Life Span Predication
Lecture 13: ML49_05_Linear Regression with Cross Validation or K-Fold
Lecture 14: ML50_05_Linear Regression with Boston dataset
Lecture 15: ML52_06_Logistic Regression Binary Clasification
Chapter 6: ML51_06_Logistic Regression Maths
Lecture 1: ML51_06_Logistic Regression Maths
Lecture 2: ML52_06_Logistic Regression Binary Clasification
Lecture 3: ML_53_06_Confusion Matrix
Lecture 4: ML_54_06_Classification Report
Lecture 5: ML_55_06_ROC Curve
Lecture 6: ML_56_06_AUC Computation
Chapter 7: Support Vector Machines Introduction
Lecture 1: ML_57_07_Support Vector Machines Introduction
Lecture 2: ML_58_07_Support Vectors and Maximizing the Margin
Lecture 3: ML_59_07_Non_linear_Support Vectors and Maximizing the Margin
Lecture 4: ML_60_07_upport Vector Machines Using Iris Toy Data set
Lecture 5: ML_61_07_Support_Vector_Machines_for_Face_Recognition
Chapter 8: Pre-processing of machine learning data Outliers
Lecture 1: ML_62_08_Pre-processing of machine learning data Outliers
Lecture 2: ML_63_08_Pre-processing of machine learning data Delete Outliers
Lecture 3: ML_64_08_Pre-processing Categorical Features
Lecture 4: ML_65_08_Pre-processing Categorical Features part2
Lecture 5: ML_66_08_Regression with categorical features using ridge algorithm
Lecture 6: ML_67_08_Dropping Missing Data
Chapter 9: ML_Pipeline with feature_selection and SVC
Lecture 1: ML_68_09_ML_Pipeline with feature_selection and SVC
Lecture 2: ML_69_09_ML_Pipeline with SimpleImputer and SVC
Chapter 10: Trees Entropy and Gini Maths Introduction
Lecture 1: ML_70_10_Trees Entropy and Gini Maths Introduction
Lecture 2: ML_73_10_Entropy Calculation using weather dataset part 3
Lecture 3: ML_74_10_Entropy Calculation using weather dataset part 4
Instructors
-
Ram Reddy
Data Scientist
Rating Distribution
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- 2 stars: 1 votes
- 3 stars: 0 votes
- 4 stars: 2 votes
- 5 stars: 1 votes
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