Machine Learning & Deep Learning in Python & R course

Machine Learning & Deep Learning in Python & R

Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R

Rating: 4.3 out of 5

4.3  (599 ratings)

63,052 students

Created by : Start-Tech Academy

Last updated 8/2020

English : English [Auto]

What you'll learn

Learn how to solve real life problem using the Machine learning techniques

Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.

Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.

Understanding of basics of statistics and concepts of Machine Learning

How to do basic statistical operations and run ML models in Python

Indepth knowledge of data collection and data preprocessing for Machine Learning problem

How to convert business problem into a Machine learning problem

Course content

42 sections • 279 lectures • 34h 56m total length

This course includes:

35 hours on-demand video

3 articles

1 downloadable resource

Full lifetime access

Access on mobile and TV

Certificate of completion

Enroll now

             

Description

You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?

You've found the right Machine Learning course!

After completing this course you will be able to:

· Confidently build predictive Machine Learning and Deep Learning models to solve business problems and create business strategy

· Answer Machine Learning related interview questions

· Participate and perform in online Data Analytics competitions such as Kaggle competitions

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear regression.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

Table of Contents

Section 1 - Python basic

This section gets you started with Python.

This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

Section 2 - R basic

This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R.

Section 3 - Basics of Statistics

This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation

Section 4 - Introduction to Machine Learning

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

Section 5 - Data Preprocessing

In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.

We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.

Section 6 - Regression Model

This section starts with simple linear regression and then covers multiple linear regression.

We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.

Section 7 - Classification Models

This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.

We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.

Section 8 - Decision trees

In this section, we will start with the basic theory of decision tree then we will create and plot a simple Regression decision tree. Then we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python and R

Section 9 - Ensemble technique

In this section, we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. We will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.

Section 10 - Support Vector Machines

SVM's are unique models and stand out in terms of their concept. In this section, we will discussion about support vector classifiers and support vector machines.

Section 11 - ANN Theoretical Concepts

This part will give you a solid understanding of concepts involved in Neural Networks.

In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

Section 12 - Creating ANN model in Python and R

In this part you will learn how to create ANN models in Python and R.

We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.

We also understand the importance of libraries such as Keras and TensorFlow in this part.

Section 13 - CNN Theoretical Concepts

In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.

In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

Section 14 - Creating CNN model in Python and R

In this part you will learn how to create CNN models in Python and R.

We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.

Section 15 - End-to-End Image Recognition project in Python and R

In this section we build a complete image recognition project on colored images.

We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).

Section 16 - Pre-processing Time Series Data

In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models

Section 17 - Time Series Forecasting

In this section, you will learn common time series models such as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.

By the end of this course, your confidence in creating a Machine Learning or Deep Learning model in Python and R will soar. You'll have a thorough understanding of how to use ML/ DL models to create predictive models and solve real world business problems.

Who this course is for:

People pursuing a career in data science

Working Professionals beginning their Data journey

Statisticians needing more practical experience

Instructor : Start-Tech Academy

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