Machine Learning (ML): Hands-on Python Course
4.2 (28 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.
168 students enrolled

Machine Learning (ML): Hands-on Python Course

Machine Learning & Data Science in Python with real life based hands-on practice. Source codes included.
4.2 (28 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.
168 students enrolled
Created by Sanjay Singh
Last updated 5/2020
English
English
Current price: $34.99 Original price: $49.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 7.5 hours on-demand video
  • 15 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Hands-on explanation of every major ML techniques in detail: Supervised, Unsupervised, Reinforcement Learning
  • Model Development, Deployment and Monitoring.
  • All the source codes are made available to you for your use.
  • Regression (Simple, Polynomial, and Multinomial)
  • Classification (Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes)
  • Ensemble Modeling (Voting Classifier, Bagging, Boosting, Stacking, Random Forest)
  • Clustering
  • Data Visualization with MatPlotLib and Seaborn
  • Use train, test and Cross Validation to choose and tune data
  • Feature Engineering (Reduce Noise, Outliers) and Data Preprocessing
  • Practical examples of How to trade-off between Bias, Variance, Irreducible errors using Ensemble Learning model and Bagging, Boosting
  • Understand how to implement Machine Learning at massive scale
  • Understand Tensor Flow and Keras
  • Understand math and statistics behind Machine Learning models
Course content
Expand all 71 lectures 07:38:46
+ Introduction
3 lectures 09:31

Welcome to this course. Let's understand what is Machine learning.

You will learn about relations between Algorithm, Features, Data and Classical Programs,  Data Science, Data Mining, and Machine Learning.

Preview 02:13

In this lecture you will learn about some of the important terms and keywords used in machine learning i.e. Observations, Labels, Features, Predictors, Independent Variable, Target Variable, Predictions, Categorical Variable, Numerical Variables, Feature Matrix, Target Vector.

Preview 03:59

In this lecture you will learn about different types of machine learning algorithm.

  1. Supervised

    1. Regression

      1. Linear Regression

        1. Simple Linear Regression

        2. Multiple Linear Regression

        3. Polynomial Linear Regression

    2. Classification

      1. Logistic Regression

      2. Decision Tree

      3. Support Vector Machine (SVM)

      4. Naive Bayes (NB)

      5. K-Nearest Neighbors (KNN)

      6. Random Forest

  2. Unsupervised

    1. Clustering

      1. K-Means

    2. Association

      1. Aprio

  3. Reinforcement Learning

    1. Trial & Error

      1. Markov Decision Process

Preview 03:19

How much you understand Machine Learning?

ML Introduction Quiz
2 questions
+ Basics of Data
1 lecture 07:16

In this lecture you will understand different types of data i.e. Numerical, Categorical, Continuous , Discrete, Binomial, Nominal, Ordinal and distribution they follow i.e. Normal, T-Distribution, Histogram.

Different Types of Data
07:16
+ Prerequisite Tools
1 lecture 02:45

In this lecture you will install Anaconda and Jupyter Notebook.

Anaconda & Jupyter Notebook Installation
02:45
+ Python basics and Libraries
7 lectures 01:31:24

In this lecture you will learn about python library numpy and it's functions like

array, max,argmax,min,argmin,linspace,random.randnt,random.rand,size and other functions.

The focus of this lecture is use of numpy in single dimensional array.

Numpy- Single Dimensional Array (Vector)
17:37

In this lecture you will learn about python library numpy and it's functions like

array, max,argmax,min,argmin,linspace,random.randnt,random.rand,size and other functions.

The focus of this lecture is use of numpy in multi dimensional array.

Numpy-Multidimensional Array (Matrix)
10:25

In this lecture you will learn about python library numpy and it's functions like

std,log,sqrt

The focus of this lecture is use of numpy statistical functions.

Numpy-Statistical Functions
02:21

In this lecture you will explore different features of Jupyter and also explore Python library Pandas.

  1. Create Pandas data frame

  2. Indexing using index method

  3. Slicing and Dicing Data Set using iloc and loc methods

  4. Import data from files using Pandas read_csv and other methods.

Pandas
21:05

In this lecture you will go through essential features of Panda's time series tool.

Pandas- Time Series
11:45

In this lecture you will learn about Matplot and it's features like 2D visualization, 3D visualization, Basemap.

Matplot
13:56

In this lecture your will learn about data exploration using Python library Seaborn. You will understand

  • Data Distribution

  • Data correlation and heat map

  • Scatter plots and linear regression plots

  • Cat Plots

  • and other visualizations


Preview 14:15

Let's test your knowledge of Python basics and Libraries used in data science.

Quiz: Python basics and Libraries
3 questions
+ Data Pre-processing
2 lectures 14:35

In this lecture you will understand data preprocessing.

Introduction
00:20

In this lecture you will learn data pre-processing techniques like Label Encoder, Sklearn library, preprocessing, model-selection methods. Test_train_split method and OneHotEncoder method.

Data Preprocessing
14:15
+ Part 1: Supervised Learning -> Regression
1 lecture 00:13

In this section you will learn about supervised learning regression.

Supervised Learning : Regression
00:13
+ Simple Linear Regression
4 lectures 31:52

In this lecture you will learn what is simple linear regression and how it's coefficients are calculated using least square method and root mean square error (RMSE).

Simple Linear Regression Intuition
03:56

In this lecture you will write Simple Linear Algorithm from scratch using Python 3.

Hans-on: Simple Linear Regression Algorithm
11:00

In this lecture you are going to do a predictive analysis on building height and number of stories data set using machine learning simple linear algorithm. You will use python provided libraries Numpy, Pandas, Matplot and Sklearn.

Preview 08:45

In this lecture you are going to do a predictive analysis on building height and number of stories data set using machine learning simple linear algorithm. You will use python provided libraries Numpy, Pandas, Matplot and Sklearn.

Hands-on: Simple Linear Regression -2
08:11
+ Multiple Linear Regression (MLR)
5 lectures 34:27

In this lecture you will understand what is Multiple Linear Regression.

Multiple Linear Regression Intuition
03:48

This lecture will show you where to get the data from for practicing multiple linear regression.

Multiple Linear Regression Data Set
00:13

In this lecture you will import the mpg data set into pandas dataframe, provide column names, use to_numeric function to change one of the feature values to numeric value and check multcollinearity.

Preview 08:37

This lecture is continuation of multiple linear regression you are building from previous lecture. In this lecture you are going to use Variance Inflation Factor to get rid of multicollinearity. Then you will draw scatter plot of remaining independent variables using sea-born pair plot.


Hands-on: Multiple Linear Regression -2
10:30

This lecture is continuation of multiple linear regression you are building from previous lecture. In this  lecture you are going to compare p value with t, split the data set in training and test data and run multiple linear regression model. Then you will draw distribution plot of y pred and y test.

Hands-on: Multiple Linear Regression -3
11:19
+ Polynomial Linear Regression (PLR)
2 lectures 17:34

In this lecture you will get introduced to Polynomial Linear Regression.

Polynomial Regression Intuition
02:15

In this lecture you will use polynomial linear regression model to predict fuel efficiency of cars.

Hands-on: Polynomial Linear Regression
15:19

Quiz: Linear Regression

Quiz: Linear Regression
3 questions
+ K-Nearest Neighbors (KNN)
3 lectures 20:41

In this lecture you will understand what is difference between Parametric and Non-parametric model and how K-Nearest Neighbor (KNN) model works.

KNN Intution
06:49

In this lecture you will learn about K-Nearest Neighbors (KNN) Regression algorithm. You will calculate r2 score and root mean square error of each regression model to compare the improvement in prediction.

Hand-on: KNN Regression- Step1
09:15

In this lecture you will learn about K-Nearest Neighbors (KNN) Regression algorithm. You will calculate r2 score and root mean square error of each regression model to compare the improvement in prediction.

Hands-on: KNN Regression -Step2
04:37
Requirements
  • To be able to operate computer
  • A lot of curiosity!
  • Some knowledge of Python programming and high school level math will be an asset
Description

Join the most comprehensive Machine Learning Hands-on Course, because now is the time to get started!

From basic concepts about Python Programming, Supervised Machine Learning, Unsupervised Machine Learning to Reinforcement Machine Learning, Natural Language Processing (NLP), this course covers it all you need to know to become a successful Machine Learning Professional!

But that's not all! Along with covering all the steps of Machine Learning functions, this course also has quizzes and projects, which allow you to practice the things learned throughout the course!

You'll not only learn about the concepts, but also practice each of those concepts through hands-on and real life Projects.

And if you do get stuck, you benefit from an extremely fast and friendly support - both via direct messaging or discussion. You have my word!

With more than two decades of IT experience, I have designed this course for students and professionals who wish to master how to develop and support industry standard Machine learning projects.

This course will be kept up-to-date to ensure you don't miss out on any changes once Machine Learning is required in your project!

Why Machine Learning?

In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According available data, Machine Learning Engineer Is The Best Job of 2019 with a 344% growth and an average base salary of $146,085 per year.

If you are looking for a thriving career in Data Analytics, Artificial Intelligence, Robotics, this is the right time to learn Machine Learning.

Get a very deep understanding of Machine Learning!

Who this course is for:
  • Students and professionals who want to become Machine Learning Expert or Data Scientist.
  • IT Professionals, Mathematicians, Statisticians.
  • Machine learning enthusiasts.
  • Project Managers, Data Analytics, and Business Intelligence Professionals.
  • Python developers.