Machine Learning Course

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Machine Learning Course

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.


Best Machine Learning Courses using Python will help you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, Naïve Bayes, and ensemble techniques. Our Machine learning with python course will also help you understand the concepts of Statistics and machine learning algorithms like supervised and unsupervised algorithms. Throughout the Python Machine Learning Training, you’ll solve real-life case studies on Media, Healthcare, social media, Aviation, and HR. Our Machine Learning course 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.


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 Outcome:

After completing this Best Machine Learning Courses training, 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?

  • According to, 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

    LI-MAT Soft Solutions Machine Learning Training Description

    About Machine Learning training

    The Python-based Best Machine Learning Courses from LI-MAT Soft Solutions is designed to help you understand the fundamentals of machine learning. This certification in Python and machine learning course will provide a thorough understanding of machine learning and its mechanism. You will recognize the importance of machine learning and how to apply it in the Python programming language as an ML engineer. Additionally, you will learn how to use machine learning algorithms to automate real-world events in this Python training on machine learning. To improve your learning experience, near the end of this machine learning online course, we'll talk about various real-world applications of machine learning using Python. For individuals who wish to master Python, we provide the best online machine-learning training. Enrol today in the online Machine Learning Certification program LI-MAT Soft Solutions offers to learn from professionals in the field.

    Who should participate in this online Machine Learning Python training?
    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. You may use it in various businesses, including shipping and fulfilment and the medical sciences, which are constantly changing and growing. Machine learning engineers develop artificial intelligence to identify trends and address issues. Although it is a highly desired career path, it may be extremely competitive. Machine learning engineers interested in certification can set themselves apart from the pack by enrolling in boot camps, submitting code repositories, and gaining practical experience.

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

    Machine learning is widely used because of its many applications; many issues may be resolved more quickly and effectively with a solid understanding of machine learning. The right moment for candidates to enrol and obtain certification is now because there is a growing demand for Machine Learning Python training and a wide range of lucrative work opportunities and positions in tech companies. It's also highly advised to study machine learning skills immediately due to abundant employment opportunities and potential.

    How do AI and machine learning connect?

    One use of AI is in machine learning. Deep learning, machine learning, and other applications fall under the umbrella of AI. A system can learn on its own via machine learning by using data. This grants the system itself an autonomous process. Python and machine learning are among the best pairings to improve your abilities and productivity.

    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?

    Since machine learning enables systems to learn from the past simultaneously and then improve upon it without being explicitly programmed, it can be considered an application of artificial intelligence. To better the future of any firm entails identifying trends in data, obtaining pertinent information, and making decisions with knowledge. Large amounts of data can be analyzed more efficiently, thanks to machine learning. It typically produces quicker and more accurate findings, which might result in beneficial advantages and new chances. Today, machine learning is employed in almost every business, including weather forecasting, facial recognition, and medical diagnostics. When pattern recognition and prediction are crucial, it may be helpful. When used in emerging markets and specialized fields, machine learning can frequently cause disruption. Machine learning engineers can develop new applications for the technology that can automate and optimize current processes. This technique may be used with the correct data to identify incredibly complicated patterns and generate exact predictions. The topics covered in this Machine Learning with Python Course will be covered in great detail.

    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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