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Online Machine Learning Course with Python

Learn machine learning with python Learning concepts like Supervised and Unsupervised Learning, Statistics, Data Analysis, Data Visualization, and Computer Vision.

  • Expert-led interactive sessions.
  • 60+ hours of dedicated learning.
  • Expert-led interactive sessions.
  • 60+ hours of dedicated learning.

Overview

Best Online Machine Learning Course with Python will help you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, Naïve Bayes, and ensemble techniques. Our online Machine learning course with python will also help you understand the concepts of Statistics and machine learning algorithms like supervised and unsupervised algorithms. Throughout our Machine Learning Training, you’ll solve real-life case studies on Media, Healthcare, social media, Aviation, and HR. Our online Machine Learning Course with Python is curated and developed by leading faculty and industry leaders with customized specializations. The system will nurture you into a skilful professional with in-depth knowledge of machine learning techniques and algorithms, like linear and non-linear regression, clustering, classification, supervised and unsupervised learning, and Natural Language Processing.

Prerequisites:

Anyone interested in the Machine learning domain may register and fulfil the prerequisites.

Note: Understanding basic programming concepts will give you the upper hand. However, if you need to catch up, there is a python refresher to get you started.

Key Takeaways

  • Expert-Led Live Interactive Sessions.
  • 60+ hours of dedicated learning.
  • Regular Assignments.
  • Assessment (Quiz/Test).
  • WhatsApp Support Groups.
  • Class Recordings.
  • Internship Grade projects and certification.

Career Opportunities

  • AI Engineer
  • Machine Learning Engineer
  • Data Analyst
  • Data Engineer
  • Python Developer
  • Machine Learning Developer
  • Machine Learning Software Architect

Course Outcomes from Our Online Machine Learning Training :

After completing this Best online Machine Learning Course, you'll have mastered the following abilities:

  • Have a solid grasp of the fundamental problems and difficulties associated with machine learning, such as data, model selection, model complexity, etc..
  • Recognize the benefits and drawbacks of numerous widely used machine learning techniques.
  • Recognize the fundamental mathematical relationships between supervised and unsupervised learning paradigms and within machine learning algorithms.
  • Possess the ability to create and use various machine learning algorithms in various practical applications.
  • Gain an understanding of what goes into learning models from data.
  • Implement object-oriented programming
  • Be familiar with a wide range of learning algorithms
  • Be able to evaluate models generated from data.
  • Apply the algorithms to real-world problems, improve the models you've learnt from them, and describe the expected accuracy you can get using them.
  • Understand the "Roles" that a Machine Learning Engineer plays.
  • Use Python to automate data analysis
  • Description of Machine Learning
  • Explanation of Machine Learning
  • Use real-time data
  • Evaluate machine learning algorithms
  • Develop skills to manage a business in the future while embracing the present

Why learn Machine Learning Course?

  • According to indeed.com, the average compensation for data scientists in the United States is $144481 annually.
  • To ensure that analytical insights drive company goals, positions like chief data scientist and chief analytics officer have been developed, according to Forbes
  • The Economic Times estimates that by 2026, 11.5 million new jobs in data science will be generated globally.
  • According to Glassdoor, a Machine Learning Engineer makes an average annual income of ₹9,00,000 plus an additional ₹2,00,000 in cash benefits in India.
  • Artificial intelligence technology develops each year dramatically. By 2023, it is expected that AI will be valued at $42 billion. This indicates that AI will eventually replace many of our regular tasks and become more prevalent than before. Other reports predict that when AI reaches that point in 2023, it will be the most transformational technology in human history.
Skills Covered
  • Python Programming
  • Data Analysis
  • Data Visualization
  • Statistical Foundations
  • Supervised and Unsupervised Machine Learning
  • Recommender System using Python
  • Optimizing ML Work Flow

    Our Online Machine Learning Course with Python:

    Detail About Our Best Online Machine Learning Course with Python

    The Python-based Machine Learning Course from LI-MAT Soft Solutions is designed to provide a comprehensive understanding of machine learning. This certification program offers an in-depth exploration of machine learning concepts and their practical applications. Through the course, you’ll learn how to implement machine learning techniques using Python, enabling you to apply these skills as a machine learning engineer. You’ll also gain experience with algorithms that automate real-world tasks. Toward the end of the course, real-world applications of machine learning with Python will be explored to enhance your learning. For those aiming to excel in Python and machine learning, our course offers expert-led instruction. Enroll today in the online Machine Learning Certification program with LI-MAT Soft Solutions and advance your skills with industry professionals.

    Who can learn Machine Learning with Python?

    For the following professionals, our Machine Learning with Python course is a suitable fit:

    • College students aiming to become "Machine Learning Engineers."
    • Managers of a group of analysts that use analytics
    • Business Analysts interested in learning about Machine Learning (ML) Techniques
    • Information architects who desire to become knowledgeable about predictive analytics
    • Python experts who want to create prediction models that are automatically generated
    Is a job in machine learning a good choice?

    In computer science, machine learning is a prominent and rapidly expanding field, applicable in various industries such as logistics, healthcare, and many more. Machine learning engineers use artificial intelligence to identify patterns and solve complex problems in these dynamic fields. While it is a highly sought-after career, the competition can be intense. Enrolling in a machine learning with Python course can help aspiring engineers stand out by earning valuable certifications, building code repositories, and gaining practical experience that sets them apart in the industry.

    What is the worth of this certification in machine learning using Python?

    Machine learning is widely adopted due to its numerous applications, offering efficient solutions to various challenges. With the increasing demand for professionals skilled in Python-based machine learning, now is the perfect time for candidates to enroll in an online machine learning certification. This certification opens doors to lucrative career opportunities in top tech companies. Given the abundance of job prospects and the potential for growth, it’s highly recommended to start developing your machine learning skills right away to stay competitive in the evolving job market.

    How do AI and machine learning connect?

    Machine learning is a branch of AI that enables systems to learn autonomously by using data. It falls under the broader scope of artificial intelligence, along with deep learning and other applications. A system can continuously improve its performance through machine learning. Pairing Python with machine learning is one of the most effective ways to enhance your skills and productivity. Earning an online machine learning certification can further validate your expertise and provide the credentials needed to excel in this cutting-edge field.

    Which organizations employ machine learning engineers?
  • Amazon
  • MathWorks
  • Databricks
  • Dataiku
  • RapidMiner
  • Microsoft Azure
  • Quantiphi
  • Accenture
  • TCS
  • SAS
  • Google Cloud
  • IBM
  • Infosys
  • HCL
  • Cognizant
    What is machine learning, and what applications does it serve?

    Machine learning is a key application of artificial intelligence, allowing systems to learn from past experiences and improve without explicit programming. This ability to identify trends, extract relevant information, and make informed decisions is essential for shaping the future of any business. By analyzing vast amounts of data more efficiently, machine learning can deliver faster and more accurate results, leading to new opportunities and advantages. Today, it is applied across industries such as weather forecasting, facial recognition, and medical diagnostics, proving especially useful in areas where pattern recognition and prediction are crucial.

    In emerging markets and specialized fields, machine learning can be highly disruptive, with the potential to automate and optimize processes. Machine learning engineers can harness the power of this technology to develop innovative applications that enhance efficiency. With the right data, machine learning can uncover complex patterns and produce precise predictions. Completing an online machine learning certification will provide a comprehensive understanding of this evolving technology and enable professionals to leverage it for real-world applications. The topics covered in this Machine Learning with Python Course will be explored in depth.

    Which sectors employ machine learning?

    Healthcare, transportation, banking, retail, agriculture, and customer service are the main industries that use machine learning. One may easily obtain jobs in these industries by enrolling in the correct Machine Learning certification course, and you can look forward to a career that is highly gratifying.

    Course Curriculum

    What will you learn in the upcoming months?

    1. Python Refresher
      1. Basic Syntax
      2. Lists
      3. Tuples
      4. Dictionaries
      5. Lambda Functions
      6. Map, Filter Reduce
    2. NumPy and Array Concepts:
      1. Introduction to NumPy arrays
      2. Concepts of 1D 2D multi-D arrays
      3. Vectors and matrices
      4. Processing with NumPy arrays
      5. NumPy over lists
      6. NumPy functions
      7. NumPy Array Operations
    3. Pandas:
      1. Introduction to Pandas
      2. Pandas Series Vs. Data Frames
      3. Loading Data with Pandas (CSV, XLSX, JSON, etc.)
      4. Creating series and data frames
      5. Data pre-processing techniques with pandas
    4. Seaborn & Matplotlib:
      1. Introduction to Data Visualization
      2. Line Plots
      3. Dist plots
      4. Join plot
      5. Scatterplot
      6. Count plot
      7. Heatmap
      8. 3d plotting
      9. Label title and grid
    5. Web Scraping
      1. What is web scraping
      2. Accessing Web Data
      3. Introduction to Beautiful Soup
      4. Using Beautiful Soup, Requests, etc. to Scrape data
      5. Converting Scraped Data to a Dataset
    6. Tuples
      1. Introduction To Tuples
      2. Single and Multi-Dimensional Tuples
      3. Tuple Methods
      4. Tuple Operations
      5. Tuple Practice Problems
    7. Machine Learning
      1. Supervised Learning
        1. Regression (To predict recurring values)
          1. Simple Linear Regression
          2. Multiple Linear Regression
          3. Polynomial and Non-Linear Regression
          4. Random forest regressor/ Decision Tree Regressor/ SVM Regressor
        2. Classification (To predict class labels)
          1. Logistic Regression with Log loss
          2. Naïve Bayes theorem and Classifier
          3. Support Vector Machines and Support Vector Classifiers
          4. K-Nearest Neighbours Algorithm with Euclidean Distance
          5. Decision Tree and Decision Tree Classifiers (Both in Gini and Entropy)
          6. Bagging or Random Forest Classifiers
          7. Boosting
            1. AdaBoost
            2. Gradient Boosting
            3. XGBOOST
      2. Unsupervised Learning
        1. Clustering (To identify the similar type of data)
          1. K-means Clustering (With mean, find the centroids)
          2. K-medoid Clustering
          3. Hierarchical/Agglomerative Clustering
          4. Density-Based Clustering (DBSCAN)
        2. PCA (Principal Component Analysis)
      3. Recommendation Systems
        1. Collaborative Recommendation Systems
        2. Content-Based Recommendation Systems
    8. Optimizing Machine Learning Work Flow
      1. Introduction to ML Workflow
      2. Model Selection
      3. Overfitting
      4. Underfitting
      5. Bias-Variance Trade-off
      6. Optimization
      7. Hyperparameter Optimization Using RandomizedSearchCV
    9. Pipelines
      1. Introduction To Pipelining
      2. Setting Up Machine Learning Pipelines
      3. Implementing Pipeline
    10. Evaluation Metrics
      1. R Squared
      2. RMSE
      3. Accuracy Score
      4. K Fold Cross Validation
    11. Projects
      1. Capstone Projects
      2. Recommendation System Project
      3. Open CV Project
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