The idea of Machine Learning or teaching a machine to think or process on its own was conceptualized in the late 90s. Machine Learning equips a computer with the proficiency to complete tasks on its own, thereby, eliminating the need to be manually programmed. The motive is to develop solutions that have the capacity to adjust behavior, automatically based on the provided data.

It’s a huge step forward in Artificial Intelligence technology owing to its impact on world business and humankind. Hence, professionals in the field have higher salaries and higher market demand. If you are looking to optimize your machine learning knowledge and its utilization in real life to provide better business solutions for your company and clients, Machine Learning Certification is essential.

Machine Learning Course with Python

Machine learning involves specialized algorithms. Python is a highly compatible and easily readable open-source language. This makes it beginner-friendly and cuts the learning curve shorter. The language also provides extensive libraries. A number of other factors have made Python the most popular programming language, especially among data scientists and other experts in the field of Data Science and Analytics. Machine Learning Course with Python provides the best career prospects.


In order to join the course, you must possess the following:

  1. Basic knowledge of statistics
  2. Knowledge of elementary programming language.


What’s here to Learn?

  1. Statistics: Understand the functioning and behavior of information while constructing models. Beginning from the basics like mean, median, and mode learn about theorems like Baye’s theorem and concepts of statistical analysis. Inclusion of examples from real-life help you learn probability fundamentals better. The objective is to apply statistical knowledge to machine learning.
  2. Python: This module deals with the implementation of Python to work on data. Familiarize yourself with Python. Learn about the terminologies and definitions of commonly used expressions. Understanding the application of Pandas (a Python package) in analyzing data. Utilize Python libraries like ggplot2, Seaborn, and others for data visualization.
  3.  Machine Learning: Know about the relevancy of statistical modeling and treatment of data in Machine Learning. Comprehensive knowledge about types of Machine Learning, and other concepts like bias-variance trade-off, overfitting, and underfitting. Learn about the 7 phases of workflow in machine learning
    1. Gather the Data.
    2. Prepare the Data
    3. Select the Correct Model
    4. Training also called the bulk of machine learning
    5. Evaluate the output upon completion of the training process
    6. After evaluation, it’s time to correct the flaws and develop the model, further.
    7. Machine learning uses data to provide a solution and answers to problems and questions. This final step is the whole reason for building a model in the first place.
  4. Optimization Techniques: Introduction to optimization techniques, such as Stochastic Gradient Descent, Batch Gradient Descent, RMSProp, and ADAM. These techniques will provide expertise in minimizing error in your model. This module also covers aspects like cost function, learning rate, Maxima and Minima, and more.
  5. Algorithms: An in-depth knowledge about algorithms like Decision Tree, SVM, and Clustering to name a few. Another important algorithm is a K-Nearest Neighbor (KNN). It is important because of its simplicity and uncomplicated algorithm. ‘K’ in ‘KNN’ is the nearest possible point ascertained based on the provided and previous data, to classify the newer one. Learn to differentiate between two categories of algorithms, which are:
  1. Supervised Algorithm: These algorithms learn from the old data and its behavior and apply it to new data. Logistic and linear regressions, Support Vector Machines, and other topics are covered.
  2. Unsupervised Algorithm: In case, the information required for the algorithm, or the algorithm that is being applied itself is unclassified or not labeled. Topics like: Clustering approaches, Hierarchical clustering, etc.
  1. Dimensionality Reduction: Understand how to use Feature Selection and Feature Extraction in order to reduce the number of variables.
  2. Neural Network: The computer replicates the neural networks of a human brain to recreate intelligence, artificially. Understand the framework of Machine Learning and apply it to perform sentiment analysis, and classify images, etc.
  3. Ensemble Learning: Learn about the application of a combination of multiple algorithms. This ensures better predictive performance. Know how to implement Association Rules. Learn about the categories of recommendation techniques, filtering (Content-based, as well as Collaborative), Hybrid Recommendation Systems, performance management, etc.


  • Property Pricing Prediction: A regression model to predict property prices by utilization of optimization techniques like gradient descent. This project involves linear regression application.
  • Classifying Customers: The database divides people into two types, good and bad, based on their market credibility. Construct a model by using a logistic regression approach to assist a bank in loan granting decision-making.
  • Classifying Chemicals: The objective of this project is to classify whether a chemical is biodegradable or not by building models to observe the relationship between biodegradation of molecules and the structure of chemicals. 41 attributes make up this dataset. There are a total of 1055 chemicals.
  • Classifying Students: This dataset comprises data that is social media posts of teenagers. Cluster the data into groups using K-Means clustering approach.
  • Quality of Wine: The composition of each wine is different. Using the ingredient composition, the motive of this project is to come up with an effective model that predicts the quality of the wine. This is determined using the Decision Tree.

Machine Learning Certification with Python language is going to help you attract better job opportunity, higher pay-scale, and higher market demand. More and more companies are resorting to machine learning because it is the face of the future, and also because there is an ever-increasing amount of data associated with any business. Hence, organizations in the government sectors to private organizations have a high demand for professionals in machine learning. Therefore, apply for the Machine Learning Certification, now.

Working on projects and the inclusion of examples from real-life scenarios help you in redefining your capabilities. It also will help you absorb everything you require to be a step ahead of your competitors. Become an asset for your organization and a savior for your clients!