Machine Learning
Course Description
Assignments & Case Studies
Real-Time Machine Learning Project
Job Readiness Program
Lifetiime access to study material
Skills covered
Data Analysis
Artificial intelligence
GIT
MLOps
Data Analysis
Data visualization
Prediction algorithms
PySpark
Machine learning, a branch of artificial intelligence (AI), enables computers to learn from data. It employs algorithms and models that adjust themselves through training, allowing systems to make predictions without explicit programming. This technology finds application in fields like healthcare, finance, and recommendation systems, transforming industries with data-driven insights.
Machine learning encompasses various paradigms, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning. These methods empower machines to handle diverse tasks, from sentiment analysis in natural language processing to image recognition in computer vision. As machine learning advances, it promises to revolutionize industries, optimize processes, and reshape technology interactions, unlocking potential in vast datasets for better decision-making.
Course detail
Machine Learning Course Curriculum
Python
- Introduction to Python and IDEs – The basics of the Python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc.
- Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
- Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
- Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding.
Linux
- Introduction to Linux – Establishing the fundamental knowledge of how Linux works and how you can begin with Linux OS.
- Linux Basics – File Handling, data extraction, etc.
- Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux.
SQL Basics –
- Fundamentals of Structured Query Language
- SQL Tables, Joins, Variables
Advanced SQL –
- SQL Functions, Subqueries, Rules, Views
- Nested Queries, string functions, pattern matching
- Mathematical functions, Date-time functions, etc.
Deep Dive into User Defined Functions
- Types of UDFs, Inline table value, multi-statement table.
- Stored procedures, rank function, SQL ROLLUP, etc.
SQL Optimization and Performance
- Record grouping, searching, sorting, etc.
- Clustered indexes, common table expressions.
Hands-on exercise:
Writing comparison data between the past year and the present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).
Extract Transform Load
- Web Scraping, Interacting with APIs
Data Handling with NumPy
- NumPy Arrays, CRUD Operations, etc.
- Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.
Data Manipulation Using Pandas
- Loading the data, data frames, series, CRUD operations, splitting the data, etc.
Data Preprocessing
- Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
- Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.
Data Visualization
- Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
- Regression plots, categorical plots, area plots, etc, with Python seaborn.
Descriptive Statistics –
- Measure of central tendency, the measure of spread, five points summary, etc.
Probability
- Probability Distributions, Bayes’ theorem, central limit theorem.
Inferential Statistics –
- Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.
Introduction to Machine Learning
- Supervised, Unsupervised Learning.
- Introduction to scikit-learn, Keras, etc.
Regression
- Introduction classification problems, Identification of a regression problem, dependent and independent variables.
- How to train the model in a regression problem.
- How to evaluate the model for a regression problem.
- How to optimize the efficiency of the regression model.
Classification
- Introduction to classification problems, Identification of a classification problem, and dependent and independent variables.
- How to train the model in a classification problem.
- How to evaluate the model for a classification problem.
- How to optimize the efficiency of the classification model.
Clustering
- Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
- How to train the model in a clustering problem.
- How to evaluate the model for a clustering problem.
- How to optimize the efficiency of the clustering model.
Supervised Learning
- Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
- Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
- Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
- Random Forest – Creating random forest models for classification problems in a supervised learning approach.
- Support Vector Machine – SVM or support vector machines for regression and classification problems.
- Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding the local minimum and maximum of a given function.
- K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
- Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.
Unsupervised Learning
- K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
- Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
- Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
- Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.
- Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
- Confusion matrix – To evaluate the true positive/negative, and false positive/negative outcomes in the model.
- r2, adjusted r2, mean squared error, etc.
Artificial Intelligence Basics
- Introduction to keras API and TensorFlow
Neural Networks
- Neural networks
- Multi-layered Neural Networks
- Artificial Neural Networks
Deep Learning
- Introduction to Deep Learning (by Academic Faculty)
- Deep neural networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- GPU in deep learning
- Autoencoders, restricted boltzmann machine
The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals:
- Extracting, loading and transforming data into usable format to gather insights.
- Data manipulation and handling to pre-process the data.
- Feature engineering and scaling the data for various problem statements.
- Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
- Assessment and monitoring of the model created using the machine learning models.
- Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
- Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
- Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
- Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
- Object Detection – A much more advanced yet simple case study that will guide you toward making a machine learning model that can detect objects in real-time.
- Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you with some really helpful and actionable insights for specific stocks.
- Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
- AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.
Power BI Basics
- Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
- SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
- Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.
DAX
- Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features
Data Visualization with Analytics
- Slicers, filters, Drill Down Reports
- Power BI Query, Q & A and Data Insights
- Power BI Settings, Administration and Direct Connectivity
- Embedded Power BI API and Power BI Mobile
- Power BI Advance and Power BI Premium
Hands-on Exercise:
Creating a dashboard to depict actionable insights in sales data.
- Job Search Strategy
- Resume Building
- Linkedin Profile Creation
- Interview Preparation Sessions by Industry Experts
- Mock Interviews
- Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.
Excel Fundamentals
- Reading the Data, Referencing in formulae , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
- Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
- VBA Macros, Ranges and Worksheet in VBA
- IF conditions, loops, Debugging, etc.
Excel For Data Analytics
- Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.
Data Visualization with Excel
- Charts, Pie charts, Scatter and bubble charts
- Bar charts, Column charts, Line charts, Maps
- Multiples: A set of charts with the same axes, Matrices, Cards, Tiles
Excel Power Tools
- Power Pivot, Power Query and Power View
Classification Problems using Excel
- Binary Classification Problems, Confusion Matrix, AUC and ROC curve
- Multiple Classification Problems
Information Measure in Excel
- Probability, Entropy, Dependence
- Mutual Information
Regression Problems Using Excel
- Standardization, Normalization, Probability Distributions
- Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
- Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression
Hands-on Exercise:
Classification problem using excel on sales data, and statistical tests on various samples from the population.
NLP:
1. Introduction to NLP
2. Basic concepts and preprocessing
3. Embedding techniques
4. Text classification and model evaluation
5. NLP in deep learning and emebeddings
A machine learning course is suitable for a diverse audience, including data scientists, software engineers, business analysts, students, researchers, entrepreneurs, and anyone interested in AI and data science. These courses cater to various skill levels, making them accessible to beginners as well as those with prior programming and data analysis experience. Whether you want to extract insights from data, build intelligent applications, make data-driven decisions, or explore the innovative possibilities of AI, a machine learning course can empower you with the knowledge and skills to thrive in this rapidly evolving field.
To learn machine learning effectively, one should have a solid grasp of mathematics, including linear algebra, calculus, and probability. Proficiency in Python programming, basic statistics knowledge, and familiarity with data manipulation are also essential prerequisites. These skills provide a strong foundation for understanding and applying machine learning concepts and algorithms.
Taking up a machine learning online course in India offers several advantages. Firstly, it provides accessibility to world-class education from renowned institutions and instructors, overcoming geographical constraints. Online courses are often more affordable than traditional in-person programs, making them cost-effective. They allow for flexible scheduling, enabling individuals to learn at their own pace while balancing other commitments. India's growing tech industry also offers numerous job opportunities in machine learning, making it a valuable skill for career advancement. Moreover, online courses offer the latest industry-relevant content, ensuring learners stay up-to-date in this rapidly evolving field.