Machine Learning with Python Training

Course Code: 5020



Python as a programming language plays a crucial role in the adoption of Machine Learning (ML) in the business environment. The application of the Python language in AI-based technologies is vast, and is used prominently in data science technologies.

Machine Learning with Python course discusses concepts of the Python language such as file operations, sequences, object-oriented concepts, etc. along with some of the most commonly leveraged Python libraries like Numpy, Pandas, Matplotlib, etc. The course will then move on to introduce learners to the detailed mechanisms of Machine Learning. Learners will understand in detail the significance of the implementation of Machine Learning in the Python programming language, and leverage this knowledge in their role as data scientists.

After completing the course, one would have learnt about tools to train machines based on real-world situations using Machine Learning algorithms, as well as to create complex algorithms based on concepts related to deep learning and neural networks. During the latter stage of the course, learners will be introduced to real-world use cases of Machine Learning with Python for a holistic learning experience which would prepare them to create applications efficiently.

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Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 14 days

Live Virtual Classroom
Duration: 14 days

What You'll learn

  • How to install and import libraries
  • Methods of handling various data types such as categorical, ordinal, and encoding
  • Data visualization
  • Distinguishing between Artificial Intelligence, Machine Learning, and Deep Learning
  • Working with data in real time
  • Implementation of Machine Learning algorithms
  • Implementation of Deep Learning algorithms
  • Types of Time Series data (univariate and multivariate)
  • Performing text and sentiment analysis
  • Business analytics


  • Overview of Python
  • History of Python
  • Python Baiscs – variables, identifiers, indentation
  • Data Structures in Python (list , string, sets, tuples, dictionary)
  • Statements in Python (conditional, iterative, jump)
  • OOPS concepts
  • Exception Handling
  • Regular Expression
  • Numpy,Pandas and Matplotlib
  • Pandas Module
  • Series
  • Data Frames
  • Numpy Module
  • Numpy arrays
  • Numpy operations
  • Matplotlib module
  • Plotting information
  • Bar Charts and Histogram
  • Box and Whisker Plots
  • Heatmap
  • Scatter Plots
  • NumPy – Arrays
  • Data Operations (selection , append , concat , joins)
  • Univariate Analysis
  • Multivariate Analysis
  • Handling Missing Values
  • Handling Outliers
  • What is Machine Learning?
  • Introduction to Machine Learning
  • Types of Machine Learning
  • Basic Probability required for Machine Learning
  • Linear Algebra required for Machine Learning
  • Simple Linear Regression
  • Multiple Linear Regression
  • Assumptions of Linear Regression
  • Polynomial Regression
  • R2 and RMSE
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • SVM
  • Naïve Bayes
  • Confusion Matrix
  • PCA
  • Factor Analysis
  • LDA
  • Types of Clustering
  • K-means Clustering
  • Agglomerative Clustering
  • AUC / ROC
  • Silhouette coefficient
  • Cross Validation
  • Bagging
  • Boosting
  • Bias v/s Variance
  • Need of recommendation engines
  • Types of Recommendation Engines
  • Content Based
  • Collaborative Filtering
  • What are Association Rules?
  • Association Rule Parameters
  • Apriori Algorithm
  • Market Basket Analysis
  • What is Time Series Analysis?
  • Importance of TSA
  • Understanding Time Series Data
  • ARIMA analysis
  • Understanding Reinforcement Learning
  • Algorithms associated with RL
  • Q-Learning Model
  • Introduction to Artificial Intelligence
  • History of Neural Network
  • Perceptron
  • Forward Propagation
  • Introduction to Deep Learning
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  • Basic understanding of computer programming languages is mandatory
  • An understanding of the fundamentals of data analysis would be beneficial

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Once the course is completed, you need to appear for an objective question-based assessment conducted by Cognixia. Based on your performance on different parameters such as attendance in the sessions, assessment scores, etc. you will be awarded a certificate by Cognixia.

Course Decsription

The global machine learning market is expected to grow at a compounded annual growth rate of 42.8%, with predictions to reach about $20.38 billion in 2024. The market is being driven by the proliferation in data generation and technological advancements. A Salesforce Research conducted recently indicates that about 83% of IT leaders say artificial intelligence and machine learning is transforming customer engagement, while 69% say it is transforming business. 79% of them also believe that artificial intelligence and machine learning will help identify external as well as internal security threats for the organization.

If open positions on LinkedIn requiring the use of TensorFlow, which is a very important tool in machine learning, are anything to go by, then there are over 12,172 such open positions listed on LinkedIn worldwide, of which 4,134 are in the US itself. Similarly, open positions requiring machine learning as a skill, listed on LinkedIn range to the tune of about 98,371 worldwide, of which about 44,864 are in the US itself. Today, the three most important use cases for machine learning are – reducing company costs, generating customer insights and intelligence, and improving customer experiences.

At such time, getting certified in machine learning with Python, would not just validate your skills and expertise in the field, it will also set you apart in the job market around you. A machine learning certification could help you shape the future of your career significantly and is definitely worth investing your time and money in.

Yes, the Machine Learning with Python training and certification offered by Cognixia is globally recognized. This certificate is given out by Cognixia itself upon successful completion of the training and clearing the assessments, as well as other parameters. You can add this credential to your resume, your LinkedIn profile, share it on social media, as well as present it along with your resume as a validation of your skills in machine learning.

The machine learning training offered by Cognixia covers:

  • How to install and import libraries
  • Data visualization
  • Working with data in real time
  • Implementation of deep learning algorithms
  • Implementation of machine learning algorithms
  • Conducting text and sentiment analysis
  • Handling different types of data – categorical, ordinal, encoding
  • Differentiating between artificial intelligence, machine learning and deep learning
  • Univariate and multivariate time series data
  • Business analytics

This machine learning training and certification course is ideal for programmers, developers, architects, technical leads, analytics professionals, information architects and Python professionals. The course is immensely useful for data science professionals as well as aspiring machine learning professionals.

This course requires participants to have a fundamental understanding of computer programming languages. IT would also be beneficial to have an understanding of the fundamentals of data analysis.