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Mastering Machine Learning and Deep Learning in Python and R 2023

One can learn a variety of machine learning and deep learning techniques and algorithms, including Regression, Decision Trees, SVM, Neural Networks, CNN, and Time Series Forecasting. These machine learning and deep learning techniques can be implemented using both Python and R programming languages. By gaining proficiency in both languages, individuals will have access to a wider range of tools and libraries, allowing them to effectively address various problem domains and datasets.

Created by- Start-Tech Academy

What you will gain from this course:

  1. Understand how machine learning techniques can be applied to solve real-life problems.
  2. Gain proficiency in using machine learning models such as Linear Regression, Logistic Regression, and K-Nearest Neighbors (KNN).
  3. Explore advanced machine learning models, including Decision Trees, XGBoost, Random Forest, and Support Vector Machines (SVM).
  4. Develop a solid understanding of the basics of statistics and the key concepts of machine learning.
  5. Learn how to perform basic statistical operations and implement machine learning models using Python.
  6. Acquire in-depth knowledge of data collection and data preprocessing techniques for machine learning problems.
  7. Learn how to effectively convert a business problem into a machine learning problem, enabling you to apply machine learning techniques in practical scenarios.
machine learning and deep learning

Course content

  1. Introduction
  • Setting up Python and Jupyter Notebook
  • Setting up R Studio and R Crash Course
  1. Basics of Statistics
  2. Introduction to Machine Learning
  3. Data Preprocessing
  4. Linear Regression
  5. Introduction to Classification Models
  6. Logistic Regression
  7. Linear Discriminant Analysis (LDA)
  8. K-Nearest Neighbors Classifier
  9. Comparing Results from 3 Models
  10. Simple Decision Trees
  11. Simple Classification Tree
  12. Ensemble Technique 1: Bagging
  13. Ensemble Technique 2: Random Forests
  14. Ensemble Technique 3: Boosting
  15. Support Vector Machines
  16. Support Vector Classifier
  17. Creating Support Vector Machine Models in Python
  18. Creating Support Vector Machine Models in R
  19. Introduction to Deep Learning
  20. Build neural networks by stacking cells to form a network.
  21. Artificial Neural Networks (ANN) in Python
  22. Artificial Neural Networks (ANN) in R
  23. Convolutional Neural Networks (CNN) – Basics
  24. Creating CNN Models in Python
  25. Creating CNN Models in R
  26. Project: Implementing a Convolutional Neural Network (CNN) Model from Scratch using Python.
  27. Project: Implementing a Convolutional Neural Network (CNN) Model from Scratch using R.
  28. Project: Data Augmentation for Avoiding Overfitting
  29. Transfer Learning: Basics
  30. Transfer Learning in R
  31. Time Series Analysis and Forecasting
  32. Time Series – Preprocessing in Python
  33. Time Series – Important Concepts
  34. Time Series – Implementation in Python
  35. Time Series – ARIMA Model
  36. Time Series – SARIMA Model
  37. Bonus Lecture


  • Students will be required to install the Anaconda software, which will be covered in a separate lecture dedicated to guiding the installation process.


Are you seeking a comprehensive course in Machine Learning and Deep Learning that can propel your career in Data Science, Machine Learning, Python, R, or Deep Learning to new heights?

Congratulations on finding the perfect Machine Learning course!

By the end of this comprehensive training, you will have the skills to:

  • Develop proficient predictive Machine Learning and Deep Learning models in R and Python, empowering you to tackle intricate business challenges and formulate impactful strategies.
  • Successfully tackle interview questions related to Machine Learning, Deep Learning, R, and Python, showcasing your expertise in these areas.
  • Participate and excel in online Data Analytics and Data Science competitions, including prestigious platforms like Kaggle.

Take a look at the table of contents below to get an overview of the diverse range of Machine Learning and Deep Learning models covered in this course.

What sets this course apart and makes it an excellent choice?

This course is an excellent choice for several reasons. It offers a comprehensive approach to solving business problems using linear regression and focuses on Machine Learning and Deep Learning techniques in both R and Python. Unlike other courses, it emphasizes the critical aspects of data preprocessing and result interpretation, which are crucial for accurate analysis and meaningful insights.

By completing this course, you will gain practical skills in R, Python, and data analysis, enabling you to make a significant impact in the field of Machine Learning and Deep Learning. Additionally, the course provides hands-on experience with real-world examples, allowing you to apply your knowledge to practical business challenges. With experienced instructors guiding you throughout the course, you can benefit from their expertise and industry insights. Overall, this course equips you with the necessary tools and knowledge to excel in the field of Machine Learning and Deep Learning, making it a top choice for your learning journey.

What qualifies us to teach you?

The instructors of this course, Abhishek and Pukhraj, bring a wealth of qualifications and experience to the table. As managers in a reputable Global Analytics Consulting firm, they have successfully helped businesses solve their complex problems using machine learning techniques. This practical experience has been leveraged to incorporate real-world aspects of data analysis into the course content.

Abhishek and Pukhraj possess extensive knowledge of machine learning and deep learning techniques, as well as proficiency in data science and data analysis tools such as R and Python. Their expertise is reflected in the creation of popular online courses with an impressive track record. These courses have garnered over 600,000 enrollments and have received numerous 5-star reviews from satisfied learners.

Here are a couple of testimonials from previous learners:

“This course is very good, and I appreciate how the explanations are accessible even to individuals without a technical background.” – Joshua

“We express our gratitude to the course author for crafting an exceptional and enriching learning experience.. In my opinion, it is truly invaluable, and it surpasses any price. – Daisy”

Table of Contents:

Section 1 – Python Basics:

  • Introduction and setup of Python and Jupyter environment
  • Basic operations in Python
  • Introduction to important libraries: Numpy, Pandas, and Seaborn

Section 2 – R Basics:

  • Introduction and setup of R and R Studio
  • Basic operations in R

Section 3 – Basics of Statistics:

  • Types of data
  • Types of statistics
  • Graphical representations of data
  • Measures of center (mean, median, mode)
  • Measures of dispersion (range, standard deviation)

Section 4 – Introduction to Machine Learning:

  • Understanding the concept of Machine Learning
  • Key terms and definitions
  • Examples illustrating Machine Learning applications
  • Steps involved in building a machine learning model

Section 5 – Data Preprocessing:

  • Importance of business knowledge in data analysis
  • Data exploration techniques
  • Uni-variate and bivariate analysis
  • Outlier treatment and missing value imputation
  • Variable transformation and correlation analysis

Section 6 – Regression Model:

  • Introduction to simple linear regression
  • Multiple linear regression
  • Evaluating model accuracy and interpreting results
  • Advanced variations of ordinary least squared method

Section 7 – Classification Models:

  • Logistic regression
  • Linear Discriminant Analysis (LDA)
  • K-Nearest Neighbors (KNN)
  • Evaluating models using confusion matrix
  • Test-train split and result interpretation

Section 8 – Decision Trees:

  • Theory and concepts of decision trees
  • Creating and plotting regression decision trees
  • Classification trees in Python and R

Section 9 – Ensemble Techniques:

  • Introduction to advanced ensemble techniques
  • Random Forest
  • Bagging
  • Gradient Boosting
  • AdaBoost
  • XGBoost

Section 10 – Support Vector Machines:

  • Support Vector Classifiers
  • Support Vector Machines

Section 11 – ANN Theoretical Concepts:

  • Understanding Neural Networks and Perceptrons
  • Network architecture and Gradient Descent algorithm

Section 12 -Building Artificial Neural Network (ANN) models from scratch using Python and R for various applications and datasets.

  • Creating ANN models using Sequential API
  • Defining network architecture and configuring the model
  • Training, evaluating, and saving the model
  • Importance of libraries like Keras and TensorFlow

Section 13 – CNN Theoretical Concepts:

  • Convolutional and pooling layers in CNN models
  • Understanding grayscale and colored images
  • Pooling layers for computational efficiency

Section 14 – Creating CNN Model in Python and R:

  • Applying CNN models to recognize fashion objects
  • Comparing CNN performance with ANN models
  • Techniques for improving accuracy

Section 15 – Full Image Recognition Project implementation in Python and R, covering data preprocessing, model training, evaluation, and deployment:

  • Building a complete image recognition project
  • Data augmentation and transfer learning
  • Achieving high accuracy levels

Section 16 – Pre-processing Time Series Data:

  • Visualizing time series data
  • Feature engineering and re-sampling techniques

Section 17 – Time Series Forecasting:

  • Auto-regression (AR)
  • Moving Average (MA)

By completing this course, you will gain the skills and knowledge to confidently create Machine Learning and Deep Learning models using Python and R. You will understand how to apply these models to solve real-world business problems. The course also includes frequently asked questions (FAQs) that provide further insights into the field of Machine Learning and its applications.

Python is a valuable language for Machine Learning due to its popularity and extensive ecosystem. It is widely used in data science and offers abundant employment opportunities in the field. Its versatility and continuous development make it a preferred choice for data scientists and analysts.

What is meant by Machine Learning?

Machine Learning is a field of computer science that enables computers to learn from data and make predictions or decisions without explicit programming. It is a branch of artificial intelligence that focuses on algorithms and models that can identify patterns and extract meaningful insights from large datasets. By learning from historical data, Machine Learning algorithms can generalize knowledge and apply it to new, unseen data, allowing for accurate predictions and informed decision-making. This technology has diverse applications and is driving advancements in various industries, revolutionizing the way we analyze data and solve complex problems.

Why python is used in Machine Learning?

Python is widely used for Machine Learning due to several compelling reasons. Firstly, Python has become the preferred language for data science and Machine Learning. It has experienced a significant surge in popularity and adoption within the data science community in recent years.

Python’s rise to prominence can be attributed to its simplicity, readability, and versatility. The language’s clean syntax and extensive library ecosystem, particularly for scientific computing and data analysis, make it well-suited for Machine Learning tasks. Libraries such as NumPy, Pandas, and scikit-learn provide powerful tools for data manipulation, preprocessing, and building ML models.

Furthermore, Python’s popularity in the data science community has resulted in a vast amount of educational resources, tutorials, and community support. This wealth of accessible information makes it easier for beginners to learn and get started with Machine Learning using Python.

Another key advantage of Python is its seamless integration with other technologies and frameworks. Python can be easily combined with popular deep learning frameworks such as TensorFlow and PyTorch, enabling the implementation of complex deep learning models with relative ease.

Additionally, the strong industry adoption of Python for data science and Machine Learning is reflected in the abundance of job opportunities. The demand for professionals proficient in Python for ML is consistently growing, making it a valuable skill for career advancement in the field.

Considering the widespread adoption, rich ecosystem, and promising future developments, learning Python for Machine Learning is a strategic choice that provides both immediate benefits and long-term career prospects.

Why learning R for Machine Learning is beneficial?

  1. Widely used in top tech firms: R is a popular language for Machine Learning at leading tech companies. Many organizations, including Facebook and Google, utilize R for tasks like behavioral analysis, ad effectiveness assessment, and economic forecasting.
  2. Designed for data manipulation and analysis: R was specifically developed with data analysis in mind, making it easier to grasp the basics of data science. Its syntax and functionality are tailored to handle data-related tasks efficiently.
  3. Extensive package ecosystem: R boasts a vast collection of packages and resources that cater to various data science needs. These packages provide convenient and powerful tools for tasks such as data manipulation, visualization, and statistical analysis.
  4. Strong and growing community: The data science community around R is robust and continuously expanding. As the field of data science evolves, R remains at the forefront, with a thriving community of data scientists and statisticians. This community support ensures that you can easily find answers to your questions and access valuable guidance.
  5. Enhances your skill set: Adding R to your skill set gives you another powerful tool for data analysis. It expands your capabilities and makes you a more versatile and marketable professional in the field of data science.

In summary, learning R for Machine Learning opens doors to opportunities at top tech firms, offers a user-friendly environment for data analysis, provides access to a rich package ecosystem, connects you with a vibrant community of data scientists, and strengthens your overall skill set in data science.

How do Data Mining, Machine Learning, and Deep Learning differ from each other?

Data mining, machine learning, and deep learning are distinct but related concepts in the field of data analysis.

Data mining involves the process of discovering hidden patterns and knowledge from large datasets. It employs algorithms and techniques to extract valuable insights and make predictions.

Machine learning, on the other hand, utilizes algorithms and statistical models to enable computers to learn from data and make accurate predictions or decisions. It focuses on reproducing known patterns and knowledge and applying them to new data.

Deep learning is a subset of machine learning that utilizes neural networks with multiple layers to process and understand complex patterns in data. It involves training these networks on large datasets and leveraging advanced computing power to achieve high accuracy in tasks such as image recognition or natural language processing.

In summary, data mining discovers hidden patterns, machine learning reproduces known patterns, and deep learning uses neural networks to understand complex patterns in data.

You can also check other courses as well: Unlocking Real World Data Science Projects with Python

This course is suitable for :

  • Those who are interested in pursuing a career in data science.
  • Working professionals who are starting their journey in the field of data analysis.
  • Statisticians who wish to gain more practical experience in applying their statistical knowledge.

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Frequently Asked Questions (FAQs)

Q:What are the prerequisites for taking this course?

A: This course assumes basic knowledge of programming concepts and statistics. Familiarity with Python and R programming languages is helpful but not mandatory.

Q: Can I take this course if I have no prior experience in data science or machine learning?

A: Absolutely! This course is designed to cater to beginners as well as individuals with some background in data science. It starts with the basics and gradually progresses to more advanced topics.

Q: How long does it take to complete the course?

A: The duration of the course depends on your pace of learning and time commitment. On average, it takes about 8-10 weeks to complete all the modules and assignments.

Q: How can this course benefit my career in data science?

A: This course equips you with essential skills in machine learning and deep learning, which are highly sought after in the data science industry. By gaining proficiency in Python and R programming languages and learning various techniques and models, you can enhance your job prospects and excel in data science roles.

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