### 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.

**30 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

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 |