A brief description
Are you looking for a comprehensive Machine Learning and Deep Learning course to help you start a successful career in Data Science and Machine Learning?
You’ve come to the right place for a Machine Learning lesson.
You will be able to do the following after completing this course:
Create prescriptive Machine Learning and Deep Learning models with confidence to solve business challenges and develop business strategies.
Answer interview questions on Machine Learning.
Participate in and score well in online Data Analytics contests, such as those hosted by Kaggle.
To see what Machine Learning and Deep Learning models you’ll read, look at the table of contents below.
How would this course benefit you?
Both students who complete this Machine Learning Basics course will receive a verifiable Certificate of Completion.
If you are a business manager or executive, or a student interested in studying and applying machine learning to real-world business issues, this course will provide you with a strong foundation by showing you the most common machine learning strategies.
Why do you enroll in this program?
This course outlines all of the steps that should be followed by using linear regression to solve a business problem.
Most courses only teach how to run the analysis, but we agree that what happens before and after the analysis is much more significant, i.e., it is critical to provide the correct data and do some pre-processing before running the analysis. Once you’ve completed the study, you should be able to assess how good your model is and analyze the findings so that you can actually assist the business.
What qualifies us to instruct you?
Abhishek and Pukhraj are the instructors for this course. We’ve used our expertise as managers in a Global Analytics Consulting company to help companies address business problems using machine learning tools, and we’ve used that knowledge to incorporate realistic elements of data processing in this course.
With over 600,000 enrollments and thousands of 5-star ratings like these, we’re still the developers of some of the most popular online courses:
This is excellent; I really appreciate the fact that the whole description can be grasped by a layperson. Joshua –
Thank you very much, Author, for this fantastic course. You’re the best, and this course is well worth the investment. Daisy –
Training our understudies is our work and we are focused on it. In the event that you have any inquiries regarding the course content, practice sheet or anything identified with any theme, you can generally post an inquiry in the course or send us an immediate message.
Download Practice records, take Quizzes, and complete Assignments
With each talk, there are class notes appended for you to track. You can likewise take tests to check your comprehension of ideas. Each segment contains a training task for you to basically actualize your learning.
List of chapters
Segment 1 – Python essential
This part kicks you off with Python.
This part will help you set up the python and Jupyter climate on your framework and it’ll show you how to play out some fundamental activities in Python. We will comprehend the significance of various libraries like Numpy, Pandas and Seaborn.
Area 2 – R fundamental
This segment will help you set up the R and R studio on your framework and it’ll show you how to play out some fundamental tasks in R.
Area 3 – Basics of Statistics
This segment is partitioned into five distinct talks beginning from kinds of information at that point sorts of insights then graphical portrayals to depict the information and afterward a talk on proportions of focus like mean middle and mode and finally proportions of scattering like reach and standard deviation
Segment 4 – Introduction to Machine Learning
In this segment we will realize – What machines Learning mean. What are the implications or various terms related with AI? You will see a few models with the goal that you comprehend what AI really is. It additionally contains steps associated with building an AI model, not simply direct models, any AI model.
Segment 5 – Data Preprocessing
In this part you will realize what moves you need to make bit by bit to get the information and afterward set it up for the examination these means are vital. We start with understanding the significance of business information then we will perceive how to do information investigation. We figure out how to do uni-variate investigation and bivariate examination then we cover points like anomaly treatment, missing worth ascription, variable change and relationship.
Area 6 – Regression Model
This part begins with basic straight relapse and afterward covers various direct relapse.
We have covered the fundamental hypothesis behind every idea without getting so numerical so you comprehend where the idea is coming from and how it is significant. Be that as it may, regardless of whether you don’t get it, it will be OK as long as you figure out how to run and decipher the outcome as instructed in the viable talks.
We additionally see how to measure models precision, what is the significance of F measurement, how downright factors in the autonomous factors dataset are deciphered in the outcomes, what are different varieties to the standard least squared strategy and how would we at last decipher the outcome to discover the response to a business issue.
Segment 7 – Classification Models
This part begins with Logistic relapse and afterward covers Linear Discriminant Analysis and K-Nearest Neighbors.
We have covered the fundamental hypothesis behind every idea without getting so numerical so you
comprehend where the idea is coming from and how it is significant. However, regardless of whether you don’t comprehend
it, it will be OK as long as you figure out how to run and decipher the outcome as educated in the commonsense talks.
We likewise see how to measure models execution utilizing disarray network, how all out factors in the free factors dataset are deciphered in the outcomes, test-train part and how would we at long last decipher the outcome to discover the response to a business issue.
Segment 8 – Decision trees
In this part, we will begin with the fundamental hypothesis of choice tree then we will make and plot a basic Regression choice tree. At that point we will grow our insight into relapse Decision tree to characterization trees, we will likewise figure out how to make an order tree in Python and R
Area 9 – Ensemble strategy
In this segment, we will begin our conversation about cutting edge group methods for Decision trees. Gatherings methods are utilized to improve the strength and precision of AI calculations. We will examine Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.
Area 10 – Support Vector Machines
SVM’s are one of a kind models and hang out regarding their idea. In this segment, we will conversation about help vector classifiers and backing vector machines.
Segment 11 – ANN Theoretical Concepts
This part will give you a strong comprehension of ideas engaged with Neural Networks.
In this part you will find out about the single cells or Perceptrons and how Perceptrons are stacked to make an organization design. Whenever engineering is set, we comprehend the Gradient plummet calculation to discover the minima of a capacity and figure out how this is utilized to improve our organization model.
Area 12 – Creating ANN model in Python and R
In this part you will figure out how to make ANN models in Python and R.
We will begin this part by making an ANN model utilizing Sequential API to take care of an order issue. We figure out how to characterize network design, arrange the model and train the model. At that point we assess the exhibition of our prepared model and use it to foresee on new information. In conclusion we figure out how to save and reestablish models.
We additionally comprehend the significance of libraries like Keras and TensorFlow in this part.
Area 13 – CNN Theoretical Concepts
In this part you will find out about convolutional and pooling layers which are the structure squares of CNN models.
In this part, we will begin with the fundamental hypothesis of convolutional layer, step, channels and highlight maps. We likewise clarify how dark scale pictures are not quite the same as hued pictures. Finally we talk about pooling layer which get computational effectiveness our model.
Segment 14 – Creating CNN model in Python and R
In this part you will figure out how to make CNN models in Python and R.
We will take a similar issue of perceiving style protests and apply CNN model to it. We will analyze the presentation of our CNN model with our ANN model and notice that the exactness increments by 9-10% when we use CNN. In any case, this isn’t its finish. We can additionally improve precision by utilizing certain methods which we investigate in the following part.
Area 15 – End-to-End Image Recognition project in Python and R
In this segment we fabricate a total picture acknowledgment project on shaded pictures.
We take a Kaggle picture acknowledgment rivalry and fabricate CNN model to address it. With a straightforward model we accomplish almost 70% precision on test set. At that point we learn ideas like Data Augmentation and Transfer Learning which assist us with improving exactness level from 70% to almost 97% (comparable to the victors of that opposition).
Segment 16 – Pre-handling Time Series Data
In this segment, you will figure out how to imagine time arrangement, perform highlight designing, do re-examining of information, and different apparatuses to dissect and set up the information for models
Segment 17 – Time Series Forecasting
In this part, you will learn normal time arrangement models, for example, Auto-relapse (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.
Before the finish of this course, your trust in making a Machine Learning or Deep Learning model in Python and R will take off. You’ll have an exhaustive comprehension of how to utilize ML/DL models to make prescient models and tackle true business issues.
The following is a rundown of well known FAQs of understudies who need to begin their Machine learning venture
What is Machine Learning?
AI is a field of software engineering which enables the PC to learn without being unequivocally modified. It is a part of man-made consciousness dependent on the possibility that frameworks can gain from information, recognize examples and settle on choices with insignificant human intercession.
Why use Python for Machine Learning?
Understanding Python is one of the important abilities required for a profession in Machine Learning.
In spite of the fact that it hasn’t generally been, Python is the programming language of decision for information science. Here’s a concise history:
In 2016, it overwhelmed R on Kaggle, the chief stage for information science rivalries.
In 2017, it surpassed R on KDNuggets’ yearly survey of information researchers’ most utilized instruments.
In 2018, 66% of information researchers detailed utilizing Python day by day, making it the main instrument for examination experts.
AI specialists anticipate that this trend should proceed with expanding advancement in the Python biological system. And keeping in mind that your excursion to learn Python programming might be simply starting, it’s ideal to realize that work openings are bountiful (and developing) too.
Why use R for Machine Learning?
Understanding R is one of the significant abilities required for a vocation in Machine Learning. The following are a few reasons why you ought to learn Machine learning in R
This course is intended for the following individuals:
Those interested in a career in data science
Working professionals who are only starting out on their data path
Statisticians who want more hands-on training
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