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Machine learning with Python Training Course

Months Icon 3 Months
38 Modules
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Key Features

OSACAD is committed to bringing you the best learning experience with high-standard features including

Key Features
Real-time Practice Labs

Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.

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Physical & Virtual Online Classrooms

Providing the flexibility to learn from our classrooms or anywhere you wish considering these turbulent times.

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24/7 Support On Slack

Technical or Technological, we give you assistance for every challenge you face round-the-clock.

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Job Interview & Assistance

Guiding in & out, until you get placed in your dream job.

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Live projects with our industry partners

An inside look & feel at industry environments by handling real-time projects.

Key Features
Internship after course

Opportunity to prove your talent as an intern at our partner firms and rope for permanent jobs.

Why Machine learning with Python ?

Why Data Science
Continuous Improvement

Machine Learning algorithms are capable of learning from the data we provide. As new data is provided, the model’s accuracy and efficiency to make decisions improve with subsequent training

Why Data Science
Automation for everything

A very powerful utility of Machine Learning is its ability to automate various decision-making tasks. This frees up a lot of time for developers to use their time to more productive use

Why Data Science
Wide range of applications

Machine Learning is used in every industry these days, for example from Defence to Education. Companies generate profits, cut costs, automate, predict the future, analyze trends and patterns from the past data, and many more.

Who is This program for

  • Fresh graduates who wish to make a career in the field of Big Data or Data Science
  • Final year engineering or MBA students who are planning to learn about predictive analytics
  • Business analysts and IT application engineers who want to hone advanced skills for a successful career in the data analytics field.
  • Best suit for people who would like to become professionals such as data analysts, business intelligence engineers
Who is this program


Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.

  • Introduction
  • Creating a Vector
  • Creating a Matrix
  • Creating a Sparse Matrix
  • Selecting Elements
  • Describing a Matrix
  • Applying Operations to Elements
  • Finding the Maximum and Minimum Values
  • Calculating the Average, Variance, and Standard Deviation
  • Reshaping Arrays
  • Transposing a Vector or Matrix
  • Flattening a Matrix
  • Finding the Rank of a Matrix
  • Calculating the Determinant
  • Getting the Diagonal of a Matrix
  • Calculating the Trace of a Matrix
  • Finding Eigenvalues and Eigenvectors
  • Calculating Dot Products
  • Adding and Subtracting Matrices
  • Multiplying Matrices
  • Inverting a Matrix
  • Generating Random Values
  • Introduction
  • Loading a Sample Dataset
  • Creating a Simulated Dataset
  • Loading a CSV File
  • Loading an Excel File
  • Loading a JSON File
  • Querying a SQL Database
  • Introduction
  • Creating a Data Frame
  • Describing the Data
  • Navigating DataFrames
  • Selecting Rows Based on Conditionals
  • Replacing Values
  • Renaming Columns
  • Finding the Minimum, Maximum, Sum, Average, and Count
  • Finding Unique Values
  • Handling Missing Values
  • Deleting a Column
  • Deleting a Row
  • Dropping Duplicate Rows
  • Grouping Rows by Values
  • Grouping Rows by Time
  • Looping Over a Column
  • Applying a Function Over All Elements in a Column
  • Applying a Function to Groups
  • Concatenating DataFrames
  • Merging DataFrames
  • Introduction
  • Rescaling a Feature
  • Standardizing a Feature
  • Normalizing Observations
  • Generating Polynomial and Interaction Features
  • Transforming Features
  • Detecting Outliers
  • Handling Outliers
  • Discretizating Features
  • Grouping Observations Using Clustering
  • Deleting Observations with Missing Values
  • Imputing Missing Values
  • Introduction
  • Encoding Nominal Categorical Features
  • Encoding Ordinal Categorical Features
  • Encoding Dictionaries of Features
  • Imputing Missing Class Values
  • Handling Imbalanced Classes
  • Introduction
  • Cleaning Text
  • Parsing and Cleaning HTML
  • Removing Punctuation
  • Tokenizing Text
  • Removing Stop Words
  • Stemming Words
  • Tagging Parts of Speech
  • Encoding Text as a Bag of Words
  • Weighting Word Importance
  • Introduction
  • Converting Strings to Dates
  • Handling Time Zones
  • Selecting Dates and Times
  • Breaking Up Date Data into Multiple Features
  • Calculating the Difference Between Dates
  • Encoding Days of the Week
  • Creating a Lagged Feature
  • Using Rolling Time Windows
  • Handling Missing Data in Time Series
  • Introduction
  • Loading Images
  • Saving Images
  • Resizing Images
  • Cropping Images
  • Blurring Images
  • Sharpening Images
  • Enhancing Contrast
  • Isolating Colors
  • Binarizing Images
  • Removing Backgrounds
  • Detecting Edges
  • Detecting Corners
  • Creating Features for Machine Learning
  • Encoding Mean Color as a Feature
  • Encoding Color Histograms as Features
  • Introduction
  • Reducing Features Using Principal Components
  • Reducing Features When Data Is Linearly Inseparable
  • Reducing Features by Maximizing Class Separability
  • Reducing Features Using Matrix Factorization
  • Reducing Features on Sparse Data
  • Introduction
  • Thresholding Numerical Feature Variance
  • Thresholding Binary Feature Variance
  • Handling Highly Correlated Features
  • Removing Irrelevant Features for Classification
  • Recursively Eliminating Features
  • Introduction
  • Cross-Validating Models
  • Creating a Baseline Regression Model
  • Creating a Baseline Classification Model
  • Evaluating Binary Classifier Predictions
  • Evaluating Binary Classifier Thresholds
  • Evaluating Multiclass Classifier Predictions
  • Visualizing a Classifier’s Performance
  • Evaluating Regression Models
  • Evaluating Clustering Models
  • Creating a Custom Evaluation Metric
  • Visualizing the Effect of Training Set Size
  • Creating a Text Report of Evaluation Metrics
  • Visualizing the Effect of Hyperparameter Values
  • Introduction
  • Selecting Best Models Using Exhaustive Search
  • Selecting Best Models Using Randomized Search
  • Selecting Best Models from Multiple Learning Algorithms
  • Selecting Best Models When Preprocessing
  • Speeding Up Model Selection with Parallelization
  • Speeding Up Model Selection Using Algorithm-Specific Methods
  • Evaluating Performance After Model Selection
  • Introduction
  • Fitting a Line
  • Handling Interactive Effects
  • Fitting a Nonlinear Relationship
  • Reducing Variance with Regularization
  • Reducing Features with Lasso Regression
  • Introduction
  • Training a Decision Tree Classifier
  • Training a Decision Tree Regressor
  • Visualizing a Decision Tree Model
  • Training a Random Forest Classifier
  • Training a Random Forest Regressor
  • Identifying Important Features in Random Forests
  • Selecting Important Features in Random Forests
  • Handling Imbalanced Classes
  • Controlling Tree Size
  • Improving Performance Through Boosting
  • Evaluating Random Forests with Out-of-Bag Errors
  • Introduction
  • Finding an Observation’s Nearest Neighbors
  • Creating a K-Nearest Neighbor Classifier
  • Identifying the Best Neighborhood Size
  • Creating a Radius-Based Nearest Neighbor Classifier
  • Introduction
  • Training a Binary Classifier
  • Training a Multiclass Classifier
  • Reducing Variance Through Regularization
  • Training a Classifier on Very Large Data
  • Handling Imbalanced Classes
  • Introduction
  • Training a Linear Classifier
  • Handling Linearly Inseparable Classes Using Kernels
  • Creating Predicted Probabilities
  • Identifying Support Vectors
  • Handling Imbalanced Classes
  • Introduction
  • Training a Classifier for Continuous Features
  • Training a Classifier for Discrete and Count Features
  • Training a Naive Bayes Classifier for Binary Features
  • Calibrating Predicted Probabilities
  • Introduction
  • Clustering Using K-Means
  • Speeding Up K-Means Clustering
  • Clustering Using Meanshift
  • Clustering Using DBSCAN
  • Clustering Using Hierarchical Merging
  • Introduction
  • Preprocessing Data for Neural Networks
  • Designing a Neural Network
  • Training a Binary Classifier
  • Training a Multiclass Classifier
  • Training a Regressor
  • Making Predictions
  • Visualize Training History
  • Reducing Overfitting with Weight Regularization
  • Reducing Overfitting with Early Stopping
  • Reducing Overfitting with Dropout
  • Saving Model Training Progress
  • k-Fold Cross-Validating Neural Networks
  • Tuning Neural Networks
  • Visualizing Neural Networks
  • Classifying Images
  • Improving Performance with Image Augmentation
  • 20.17 Classifying Text
  • Introduction
  • Saving and Loading a scikit-learn Model
  • Saving and Loading a Keras Model
Hours of Content
Case Study & Projects
Live Sessions
Coding Assignments
Capstone Projects to Choose From
Tools, Languages & Libraries

Languages and Tools covered

Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered

Hands On Projects

Music Recommendation System Project

In this project, we use the dataset from Asia's leading music streaming service to build a better music recommendation system. We will try to determine which new song or which new artist a listener might like based on their previous choices

Customer Churn Prediction Analysis using Ensemble Techniques

In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques

Machine Learning project for Retail Price Optimization

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model


Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond position

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First-Ever Hybrid Learning System

Enjoy the flexibility of selecting online or offline classes with Osacad first-ever hybrid learning model.
Get the fruitful chance of choosing between the privilege of learning from home or the
advantage of one-on-one knowledge gaining - all in one place.

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Learn from Home

Why leave the comfort and safety of your home when you can learn the eminent non-technical courses right at your fingertips? Gig up to upskill yourself from home with Osacad online courses.

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Exploit the high-tech face-to-face learning experience with esteemed professional educators at Osacad. Our well-equipped, safe, and secure classrooms are waiting to get you on board!

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Artificial Intelligence which is a global company with headquarters in Chicago, USA. Artificial Intelligence has partnered with GamaSec, a leading Cyber Security product company. Artificial Intelligence is focusing on building Cyber Security awareness and skills in India as it has a good demand in consulting and product support areas. The demand for which is predicted to grow exponentially in the next 3 years. The Artificial Intelligence training programs are conducted by individuals who have in depth domain experience. These training sessions will equip you with the fundamentalknowledge and skills required to be a professional cyber security consultant.

All graduates of commerce, law, science and engineering who want to build a career in cyber security can take this training.

There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.

You can get started by enrolling yourself. The enrollment can be initiated from this website by clicking on "ENROLL NOW". If you are having questions or difficulties regarding this, you can talk to our counselors and they can help you with the same.

Once you enroll with us you will receive access to our Learning Center. All online classrooms, recordings, assignments, etc. can be accessed here.

Get in touch with us

What do you benefit from this programs
  • Pick up existing data and build Machine Learning Models to generate a predictive analysis.
  • Leverage applied immersive learning and begin your journey to becoming a confident Machine Learning Engineer
  • Learn how hypothesis testing and inferential statistics help make brilliant technical decisions

I’m Interested

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