Machine Learning with Python

    Machine Learning is a field of study that, enables us to find patterns hiding behind the data with the help of scientific approach, algorithms and extract crucial information and insights in the best ..

    Created by Shankargouda

    Course Overview

    Machine Learning helps in predicting future in terms of human life, Business sales, Stock prices etc. It is a top trending course and demand is growing every day. This course welcomes technical, non-technical background professionals, fresh graduates from any background. Learn Python, Pandas, Numpy, Visualization tools, Statistics, Machine Learning and many more starting from basics to advanced level during live training.

    Training Key Features

    • Instructor Led Live Training.
    • Assignments at regular intervals on every topic.
    • Projects on real world use cases.
    • Lifetime access to student Dashboard.
    • Lifetime access to Recorded sessions.
    • Various resume samples are provided and guidance on resume preparation as well.
    • More than 20 real world projects provided post completion.
    • One-month free mentorship program


    Basic knowledge of computer. Any background graduates are welcome..

    Training Level : Beginner
    Includes :
    • Certificate
    • Projects
    • Online Test
    • Lecture Recordings
    • E-notes

    Instructor-led Live Training

    Nov, 29
    50 Hours (Monday,Tuesday,Wednesday) Timings - 16:00 to 18:00
    Nov, 27
    50 Hours (Saturday,Sunday) Timings - 10:00 to 13:00

    Course Price at

    14000 20000

    Course Content

    Trainer Introduction
    Introduction to Machine Learning
    Introduction of Anaconda, Jupyter
    Python Installation
    Simple Expressions, Variables in Python
    Branching (if else elif)
    List, Tuple, Dictionary and sets
    Loops and Range Function
    Break and Continue
    Input from Keyword
    String Basics and indexing
    Functions, map, reduce and filter
    Iterators and generators
    Object Oriented Programming
    Inheritance and Encapsulation
    Files IO, Modules and Packages
    Working with database, SQL etc
    Read and write operations from CSV and TSV files
    Read and write operations from html
    Read and write operations from web api
    Creating and working with Data frames
    Date time manipulation
    Data Cleaning
    Data manipulation
    Series manipulation
    Data frame manipulation
    Pandas with sqlite3 database
    Numpy from function
    Data types
    Arange and linspace
    Matrix creation
    Random number generation
    Indexing and slicing
    Universal Functions
    Array Math
    Matplotlib and Seaborn
    Plotting bar, barh, histogram graphs
    Plotting box, kde, scatter, pi graphs
    Numerical data plot, Seaborn Styling, Matplotlib styling
    Regression, Categorical and distribution data plots
    Types of Statistics, Descriptive and Inferential
    Types of distribution, Hypothesis, Probability
    Student test and types, ANOVA, Z-statistics
    Ordinary Least Square methods
    Numerical and Categorical data types and examples
    Working on use cases using CDF,PDF,PPF
    Standard Normal distribution, Type I and Type II errors
    Supervised and unsupervised machine learning.
    Machine Learning Applications example
    Machine learning practitioner demand and salary brackets
    Linear Regression with use case
    Logistic Regression with use case
    Decision Tree with use case
    Ensemble techniques. Bagging and Boosting
    Random Forest classifier with use case
    Voting Classifier with use case
    Adaboost with use case
    Gradient Boost with use case
    Xtreme Gradient Boosting (XGBOOST) with use case
    Support Vector Machines (SVM) with use case
    Naive Bayes with use case
    K-Means Clustering with use case
    Hierarchical Clustering with use case
    DBSCAN with use case
    Outlier/ Anomaly detection
    Overfitting, Under fitting
    Lasso, Ridge Regression,ElasticCV
    Confusion Matrix, Accuracy
    Precision-recall and ROC AUC curve
    Regression Evaluation, R2, Adjusted R2
    Correlation, Covariance, Multicollinearity
    Principal Component Analysis (PCA)
    Variance Inflation factor (VIF)
    Grid Search CV
    Randomized Search CV
    Cross Validation techniques
    Handling Outliers
    Working with Categorical Data
    One-Hot Encoder, Label Encoder
    Exploratory Data Analysis
    Handling Missing values, Standard scaler, Min Max scaler
    Working with Pipeline
    Deployment methods like AWS EC2, Sagemaker, Azure
    Resume building Guidance
    Sample Resume Discussion
    Interview preparation tips
    Discussion on free one month mentorship program.

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    Course Reviews

    Any person with basic computer knowledge can enroll to this program.
    Prior programming knowledge is not necessary and in this course, we don't build programming application, so very minimum basic programming skills needed and you will be trained on the skill. Every topic will be trained taking real life example.
    Many start ups looks for freshers. If you dedicate yourself and build good projects, no one can stop hiring you. You will get detailed information during the course.
    Of course Yes. There is no limit for experience. Since you are coming with good amount of domain experience, you will have added advantage. Go for it.
    Yes, we are going to work on projects. You will be provided many projects to work on.
    Mentorship program is to mentor students post completion of course. During this time frame, students are going to work on multiple projects and discussions will be held to monitor their progress and give proper guidance to complete the projects, so that students gain confidence and get ready for the interview.
    Instructor has 14+ years of rich experience in IT in one of the top MNC and have worked on multiple domains such as, Banking, Finance, Telecom, Utilities, Healthcare etc. Trainer has completed many batches for experienced as well as freshers.

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