Name: Machine Learning And Data Science Using Python For Beginners
Price: 199 USD

About This Course

Who this course is for:

Data Scientists
Data Engineers
Programmers

What you’ll learn:

Introduction to Machine Learning
Basics of Python
NumPy
Pandas
Matplotlib
Data visualization and preparation
Classification and Regression models
And much more

Requirements:

No prior knowledge is required to take this course

In this course, we will focus mainly on Machine Learning. Throughout this course, we will prepare our machine to make it ready for a prediction test, just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Let’s call these sample data of similar problems and their solutions as training input and training output, respectively. And then, the day comes when we have the actual test.

We will be given a new set of problems to solve, but very similar to the problems we learned, and based on the previous practice and learning experiences, we have to solve them. We can call those problems as testing input and our answers as predicted output. Later, our professor will evaluate these answers and compare it with its actual answers, which we can call as test output. Then, a mark will be given on the basis of the correct answers. We call this mark as our accuracy. The life of a machine learning engineer and a data-scientist is dedicated to make this accuracy as good as possible through different techniques and evaluation measures.

Machine Learning and Data Science are the most lucrative jobs in the technology arena nowadays. Learning this course will make you equipped to compete in this area.

Our Promise to You

By the end of this course, you will have learned how Machine Learning works.

10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about Machine Learning.

Course Curriculum

10h 19m

Section 1 - Introduction
	Course Overview And Table Of Contents	00:00:00
	Introduction To Machine Learning Part 1 – Concepts , Definitions And Types	00:00:00
	Introduction To Machine Learning Part 2 – Classifications And Applications	00:00:00
	System And Environment Preparation Part 1	00:00:00
	Important: TensorFlow Incompatibility	00:00:00
	System And Environment Preparation Part 2	00:00:00
	Course Resources	00:00:00
Section 2 - Basics Of Python
	Assignment	00:00:00
	Flow Control	00:00:00
	Functions	00:00:00
	Data Structures	00:00:00
Section 3 - Basics Of NumPy And Matplotlib
	NumPy Array	00:00:00
	NumPy Data	00:00:00
	NumPy Arithmetic	00:00:00
	Matplotlib	00:00:00
Section 4 - Basics Of Pandas
	Basics Of Pandas Part 1	00:00:00
	Basics Of Pandas Part 2	00:00:00
Section 5 - CSV Data File
	Understanding The CSV Data File	00:00:00
	Load And Read CSV Data File Using Python Standard Library	00:00:00
	Load And Read CSV Data File Using NumPy	00:00:00
	Load And Read CSV Data File Using Pandas	00:00:00
Section 6 - Dataset Summary
	Peek, Dimensions And Data Types	00:00:00
	Class Distribution And Data Summary	00:00:00
	Explaining Correlation	00:00:00
	Explaining Skewness – Gaussian And Normal Curve	00:00:00
Section 7 - Dataset Visualization
	Using Histograms	00:00:00
	Using Density Plots	00:00:00
	Box And Whisker Plots	00:00:00
	Multivariate Dataset Visualization – Correlation Plots	00:00:00
	Multivariate Dataset Visualization – Scatter Plots	00:00:00
Section 8 - Data Preparation
	Data Preparation (Pre-Processing) – Introduction	00:00:00
	Re-Scaling Data Part 1	00:00:00
	Re-Scaling Data Part 2	00:00:00
	Standardizing Data Part 1	00:00:00
	Standardizing Data Part 2	00:00:00
	Normalizing Data	00:00:00
	Binarizing Data	00:00:00
Section 9 - Feature Selection
	Introduction	00:00:00
	Uni-Variate Part 1 – Chi-Squared Test	00:00:00
	Uni-Variate Part 2 – Chi-Squared Test	00:00:00
	Recursive Feature Elimination	00:00:00
	Principal Component Analysis (PCA)	00:00:00
	Feature Importance	00:00:00
Section 10 - Algorithm Evaluation Techniques
	Refresher Session – The Mechanism Of Re-Sampling, Training And Testing	00:00:00
	Introduction	00:00:00
	Train And Test Set	00:00:00
	K-Fold Cross Validation	00:00:00
	Leave One Out Cross Validation	00:00:00
	Repeated Random Test-Train Splits	00:00:00
Section 11 - Algorithm Evaluation Metrics
	Introduction	00:00:00
	Classification Accuracy	00:00:00
	Log Loss	00:00:00
	Area Under ROC Curve	00:00:00
	Confusion Matrix	00:00:00
	Classification Report	00:00:00
	Mean Absolute Error – Dataset Introduction	00:00:00
	Mean Absolute Error	00:00:00
	Mean Square Error	00:00:00
	R Squared	00:00:00
Section 12 - Classification Algorithm Spot Check
	Logistic Regression	00:00:00
	Linear Discriminant Analysis	00:00:00
	K-Nearest Neighbors	00:00:00
	Naive Bayes	00:00:00
	Cart	00:00:00
	Support Vector Machines	00:00:00
Section 13 - Regression Algorithm Spot Check
	Linear Regression	00:00:00
	Ridge Regression	00:00:00
	Lasso Linear Regression	00:00:00
	Elastic Net Regression	00:00:00
	K-Nearest Neighbors	00:00:00
	Cart	00:00:00
	Support Vector Machines	00:00:00
Section 14 - Compare Algorithms
	Choosing The Best Machine Learning Model Part 1	00:00:00
	Choosing The Best Machine Learning Model Part 2	00:00:00
Section 15 - Pipelines
	Data Preparation And Data Modelling	00:00:00
	Feature Selection And Data Modelling	00:00:00
Section 16 - Performance Improvement
	Ensembles – Voting	00:00:00
	Ensembles – Bagging	00:00:00
	Ensembles – Boosting	00:00:00
	Parameter Tuning Using Grid Search	00:00:00
	Parameter Tuning Using Random Search	00:00:00
Section 17 - Export, Save And Load Machine Learning Models
	Pickle	00:00:00
	Joblib	00:00:00
Section 18 - Finalizing A Model
	Finalizing A Model – Introduction And Steps	00:00:00
	Finalizing A Classification Model – The Pima Indian Diabetes Dataset	00:00:00
	Quick Session: Imbalanced Data Set – Issue Overview And Steps	00:00:00
	Iris Dataset: Finalizing Multi-Class Dataset	00:00:00
	Finalizing A Regression Model – The Boston Housing Price Dataset	00:00:00
Section 19 - Real-Time Predictions
	Using The Pima Indian Diabetes Classification Model	00:00:00
	Using Iris Flowers Multi-Class Classification Dataset	00:00:00
	Using The Boston Housing Regression Model	00:00:00