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Machine Learning (Data Science and Deep Learning) with Python Online Training

This Python Machine Learning dives into an approachable and well-known programming language into the fundamental elements of machine learning. This course explains the fundamentals of Python programming and the different packages needed for machine learning. You will learn Supervised versus Unsupervised Learning, look at statistical modeling and make a comparison of each.

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Machine Learning (Data Science and Deep Learning) with Python Online Training Overview

Machine Learning, Data Science, and Deep Learning with Python teach you the techniques used by real data scientists and machine learning practitioners in the tech industry, Easy to learn and simple to use. Python has become the language of choice for most data scientists. This popular language is driving powerful new developments in the field, such as deep learning, and is the perfect choice for data mining, AI, and other analysis techniques prized by businesses and employers. Using the Python code you can experiment with the complete hands-on data science. Online training teaches you techniques used by real data scientists and prepares you for a move into this career path. Our Applied AI/Machine Learning Courses are designed as whole learning experiences to support your journey from the first exercise to a new career.

Machine Learning (Data Science and Deep Learning) with Python Training will:

  • Introduce you to the Machine Learning (Data Science and Deep Learning) with Python programming language and technology.
  • Understand reinforcement learning - and how to build a Pac-Man bot and Implement machine learning at massive scale with Apache Spark's MLLib.
  • Build Deep Learning networks to classify images with Convolutional Neural Networks.
  • Cluster data using K-Means clustering and Support Vector Machines (SVM).
  • Classify medical test results with a wide variety of supervised machine learning classification techniques.
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA.
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Machine Learning (Data Science and Deep Learning) with Python Training Objectives

  • A massive library.
  • Freedom from the platform.
  • There is a lot of community support.
  • Integration of Enterprise Applications
  • Simplicity.
  • TensorFlow and Keras are used to create Deep Learning / Neural Networks.
  • Python data visualization using MatPlotLib and Seaborn.
  • Learning should be transferred.
  • Analyze the emotions.
  • Recognition and classification of images.
  • Analysis of regression.
  • Clustering using K-Means.
  • Principal Component Analysis (PCA)
  • Python data visualization using MatPlotLib and Seaborn.
  • Learning should be transferred.
  • Analyze the emotions.
  • Recognition and classification of images.
  • The average salary for a data scientist is Rs.708,012.
  • For less than a year of experience, an entry-level data scientist will receive about Rs.500,000 per year.
  • Early-career computer scientists with 1 to 4 years of experience earn about Rs.610,811 a year.
  • Machine Learning (Data Science and Deep Learning) with Python teaches you the methods used by real data scientists and machine learning professionals in the tech industry, preparing you for a future in this hot field.
  • Create artificial neural networks using Tensorflow and Keras.
  • Deep learning is used to classify photographs, details, and sentiments.
  • Make forecasts using linear regression, polynomial regression, and multivariate regression.
  • Data visualisation with MatPlotLib and Seaborn.
  • Apache Spark's MLLib can be used to implement machine learning at a large scale.
  • Learn about reinforcement learning and how to make a Pac-Man bot.
  • You'll need a desktop machine (Windows, Mac, or Linux) that can run Anaconda 3 or later.
  • The course will take you through the process of downloading and installing the necessary free software.
  • It is essential to have a previous coding or scripting experience.
  • Math skills equivalent to those used in high school would be expected.
  • Machine Learning is the most important subfield of artificial intelligence.
  • It causes computers to enter a self-learning mode in the absence of explicit programming.
  • When presented with new data, these computers learn, evolve, improve, and build on their own.
  • Machine Learning (Data Science and Deep Learning) with Python focuses on machine learning, Tensorflow, artificial intelligence, and neural networks—all of which are in high demand from the world's leading tech companies.
  • Prototypes in data science should be studied and transformed.
  • Create machine-learning applications.
  • Investigate and incorporate effective machine learning algorithms and tools.
  • Create machine learning software based on the specifications.
  • Choose suitable datasets and data representation approaches.
  • Conduct machine learning trials and evaluations.
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Top Companies Placement

A Machine Learning Engineer is responsible for exploring data to obtain a better understanding of it. Finding the data distribution variations that may impact output when implementing a model in the real world. Understanding business goals and creating templates to help achieve them, as well as metrics to monitor progress and rewarded with substantial pay raises shown below.
  • Designation
  • Annual Salary
    Hiring Companies
  • 5.0L
    Min
  • 9.5L
    Average
  • 17.5L
    Max
  • 4.50L
    Min
  • 8.5L
    Average
  • 16.5L
    Max
  • 4.0L
    Min
  • 7.5L
    Average
  • 13.5L
    Max
  • 3.24L
    Min
  • 6.5L
    Average
  • 12.5L
    Max
Top Skills You Will Gain
  • Getting and cleaning data.
  • Exploratory data analysis.
  • Reproducible research.
  • Supervised learning
  • Binary Classification
  • Core Concepts
  • Description of assignment
  • Unsupervised learning, Jupyter

Online Classroom Batches Preferred

Monday (Mon - Fri)
05-May-2025
08:00 AM (IST)
Wednesday (Mon - Fri)
07-May-2025
08:00 AM (IST)
Saturday (Sat - Sun)
10-May-2025
11:00 AM (IST)
Saturday (Sat - Sun)
11-May-2025
11:00 AM (IST)
Can't find a batch you were looking for?
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No Interest Financing start at ₹ 5000 / month

Corporate Training

  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

Machine Learning with Python Course Curriculam

Trainers Profile

Trainers are certified professionals with 11+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Machine Learning with Python Course are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.

Pre-requisites

  • NumPy for mathematical operations
  • TensorFlow for Deep Learning
  • OpenCV and Dlib for computer vision
  • Syllabus of Machine Learning with Python Online Course Download syllabus

    • 1. Introduction to the Course
    • 2. Course Help and Welcome
    • 3. Course FAQs
    • 1. Python Environment Setup
    • 1. Updates to Notebook Zip
    • 2. Jupyter Notebooks
    • 3. Optional: Virtual Environments
    • 1. Welcome to the Python Crash Course Section!
    • 2. Introduction to Python Crash Course
    • 3. Python Crash Course - Part 1
    • 4. Python Crash Course - Part 2
    • 5. Python Crash Course - Part 3
    • 6. Python Crash Course - Part 4
    • 7. Python Crash Course Exercises - Overview
    • 8. Python Crash Course Exercises - Solutions
    • 1. Welcome to the NumPy Section!
    • 2. Introduction to Numpy
    • 3. Numpy Arrays
    • 4. Quick Note on Array Indexing
    • 5. Numpy Array Indexing
    • 6. Numpy Operations
    • 7. Numpy Exercises Overview
    • 8. Numpy Exercises Solutions
    • 1. Welcome to the Pandas Section!
    • 2. Introduction to Pandas
    • 3. Series
    • 4. DataFrames - Part 1
    • 5. DataFrames - Part 2
    • 6. DataFrames - Part 3
    • 7. Missing Data
    • 8. Groupby
    • 9. Merging Joining and Concatenating
    • 10. Operations
    • 11. Data Input and Output
    • 1. Welcome to the Data Visualization Section!
    • 2. Introduction to Matplotlib
    • 3. Matplotlib Part 1
    • 4. Matplotlib Part 2
    • 5. Matplotlib Part 3
    • 6. Matplotlib Exercises Overview
    • 7. Matplotlib Exercises - Solutions
    • 1. Introduction to Seaborn
    • 2. Distribution Plots
    • 3. Categorical Plots
    • 4. Matrix Plots
    • 5. Grids
    • 6. Regression Plots
    • 7. Style and Color
    • 8. Seaborn Exercise Overview
    • 9. Seaborn Exercise Solutions
    • 1. Welcome to the Data Capstone Projects!
    • 2. 911 Calls Project Overview
    • 3. 911 Calls Solutions - Part 1
    • 4. 911 Calls Solutions - Part 2
    • 5. Bank Data
    • 6. Finance Data Project Overview
    • 7. Finance Project - Solutions Part 1
    • 8. Finance Project - Solutions Part 2
    • 9. Finance Project - Solutions Part 3
    • 1. Welcome to the Machine Learning Section!
    • 2. Supervised Learning Overview
    • 3. Evaluating Performance - Classification Error Metrics
    • 4. Evaluating Performance - Regression Error Metrics
    • 5. Machine Learning with Python
    • 1. Linear Regression Theory
    • 2. Linear Regression with Python - Part 1
    • 3. Linear Regression with Python - Part 2
    • 4. Linear Regression Project Overview
    • 5. Linear Regression Project Solution
    • 1. Logistic Regression Theory
    • 2. Logistic Regression with Python - Part 1
    • 3. Logistic Regression with Python - Part 2
    • 4. Logistic Regression with Python - Part 3
    • 5. Logistic Regression Project Overview
    • 6. Logistic Regression Project Solutions
    • 1. KNN Theory
    • 2. KNN with Python
    • 3. KNN Project Overview
    • 4. KNN Project Solutions
    • 1. Introduction to Tree Methods
    • 2. Decision Trees and Random Forest with Python.
    • 3. Decision Trees and Random Forest Project Overview
    • 4. Decision Trees and Random Forest Solutions Part 1
    • 5. Decision Trees and Random Forest Solutions Part 2
    • 1. Natural Language Processing Theory
    • 2. NLP with Python - Part 1
    • 3. NLP with Python - Part 2
    • 4. NLP with Python - Part 3
    • 5. NLP Project Overview
    • 6. NLP Project Solutions
    • 1. Welcome to the Deep Learning Section!
    • 2. Introduction to Artificial Neural Networks (ANN)
    • 3. Perceptron Model
    • 4. Neural Networks
    • 5. Activation Functions
    • 6. Multi-Class Classification Considerations
    • 7. Cost Functions and Gradient Descent
    • 8. Backpropagation
    • 9. TensorFlow vs Keras
    • 10. TF Syntax Basics - Part One - Preparing the Data
    • 11. TF Syntax Basics - Part Two - Creating and Training the Model
    • 12. TF Syntax Basics - Part Three - Model Evaluation
    • 13. TF Regression Code Along - Exploratory Data Analysis
    • 14. TF Regression Code Along - Exploratory Data Analysis - Continued
    • 15. TF Regression Code Along - Data Preprocessing and Creating a Model
    • 16. TF Regression Code Along - Model Evaluation and Predictions
    • 17. TF Classification Code Along - EDA and Preprocessing
    • 18. TF Classification - Dealing with Overfitting and Evaluation
    • 19. TensorFlow 2.0 Project Options Overview
    • 20. TensorFlow 2.0 Project Notebook Overview
    • 21. Keras Project Solutions - Dealing with Missing Data
    • 22. Keras Project Solutions - Dealing with Missing Data - Part Two
    • 23. Keras Project Solutions - Categorical Data
    • 24. Keras Project Solutions - Data PreProcessing
    • 25. Keras Project Solutions - Data PreProcessing
    • 26. Keras Project Solutions - Creating and Training a Model
    • 27. Keras Project Solutions - Model Evaluation
    • 28. Tensorboard
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