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Machine Learning Training with Python, Practical Projects and Certification

Machine Learning is a course that will teach you how computers can learn from patterns in data and predict outcomes. Study machine learning algorithms, data preprocessing, model evaluation, and even Python programming.

4.8 (485 reviews)
715 Enrolled
180 Hrs of Content
Live Online Class with Recording, Online One-to-One Class, Weekend Class
  • 22 modules · 198 topics · 180h of content
  • Industry-recognized certificate on completion
  • Live doubt-clearing sessions with expert mentors
  • Hands-on projects & real-world case studies
Expert Trainer Industry Certificate Real-live Projects Trainer Support Interview Preparation Resume Building Support Career Guidance Lifetime Membership Access

Duration

180Hrs

Modules

22

Topics

198

Enrolled

715

Certificate

Included

Access

Lifetime

About Machine Learning Course

Machine learning algorithms allow the computer to discover patterns in data, which can then be used for making predictions and decisions. Machine learning can assist a company in predicting its future revenues, finding abnormalities in transactions, and understanding customers. A Machine Learning Training will give you insight into how such processes work and how they can be applied in practice.

Learn about the principles of training and using machine learning algorithms to make predictions.
Get to know the reasons for preprocessing the data before the modeling process starts.
Understand the difference between classification, regression, and clustering algorithms and their practical applications.
Learn about feature engineering that allows preparing good data for modeling.
Acquire basic knowledge about training and evaluating machine learning models.
Learn the role of Python programming in data analysis.
About Machine Learning Course

Machine Learning Course

What Will You Learn in Machine Learning Training?

Your understanding will develop progressively, beginning with the workings of machine learning all the way through to the processes of training and validation of models. What is taught in detail in Machine Learning Course Training depends on the syllabus eventually developed, but it would be advisable to make sure that the syllabus includes Python, deep learning, neural networks, and predictive modeling.

Study How Machine Learning Algorithms Work

Study how patterns in data are identified by machine learning algorithms and used to predict future events. Get acquainted with classification, regression, and clustering and their applicability in certain cases.

Become Familiar with Data Preprocessing and Feature Engineering

Find out why data preprocessing is required and what it consists of. Study how feature engineering can help select or transform data to make working with it more efficient for the model.

Investigate the Process of Training and Evaluating the Model

Understand how a machine learning model is trained using data and evaluated. Find out why testing is important and what role model evaluation plays in assessing whether predictions made by the model are relevant.

Study How Machine Learning Is Supported By Python

Investigate how Python programming is used for various machine learning tasks from data preprocessing to working with algorithms. Learn the basics of related Python libraries and machine learning frameworks according to the topics covered in the syllabus.

Explore the Concept of Predictive Modeling and AI Applications

Study the process of making predictions on the basis of the historical data. Explore examples such as sales forecasting, customer grouping, and spam detection, and learn how machine learning supports applications in artificial intelligence.

Why Enroll in Corporates Academy for Machine Learning Training?

Selecting a course becomes simpler once you are aware of the structure of the courses, the things that you practice and the reward that you get upon course completion. The present page offers details on live classes, recorded videos, practical projects, and certification upon completion. Before selecting Machine Learning Training, it is recommended to enquire about the current syllabus, project list, course fees, batch schedule, and certification requirements from the institute.

  1. Examine the Syllabus: Verify whether the course offers a syllabus related to machine learning algorithms, data preprocessing, training and evaluation of the model.
  2. Enquire About Practical Projects: Request examples of the practical projects and inquire if you have to do coding and training of the model yourself.
  3. Confirm Machine Learning Classes Format: Verify whether live classes, one-to-one classes, and weekend batches are offered presently.
  4. Clarify About Certification: Inquire about the requirements needed to earn the Machine Learning Certification and whether any assessments are included in the same.
  5. Verify Learning Help: Learn how you can approach questions and review the lessons and the assistance you may get for problems in code or model creation in the Machine Learning Course.

Key Benefits of Machine Learning Course

Machine learning can provide you with the means of using data for predictions instead of relying only on assumptions. Also, you will obtain the foundation needed to conduct research into AI-related topics.

  1. Knowledge of Predictive Modelling: Obtain an understanding of how data from the past can be used to make predictions about future events, such as demand or user behavior.
  2. Improve your Data Analysis Skills: Learn how to conduct data analysis, find patterns and choose a proper algorithm.
  3. Familiarize Yourself With Various Models: Obtain knowledge about classification, regression, clustering and others and the differences between models.
  4. Obtain Programming Skills: Utilize Python for data exploration and interaction with algorithms when it is necessary according to the Machine Learning Training syllabus.
  5. Foundation for Further Study: Obtain a sufficient base to learn deep learning and neural networks in the future along with Machine Learning Certification.

Career Opportunities Available After Machine Learning

Learning machine learning can be useful in jobs that require programming, data analysis, making predictions, or anything related to AI technology. Your choice of which job you can apply to depends on how good you are at programming, mathematics, statistics, and any other technical skills that you might have.

Job RoleWhat the Work Involves
Junior Machine Learning EngineerHelps prepare data, train models, test results, and support machine learning applications.
Machine Learning AnalystStudies datasets and model results to find patterns and useful insights.
Data AnalystCleans and examines data, prepares reports, and helps answer business questions.
Junior Data ScientistWorks with data, statistical methods, and predictive models to investigate problems.
AI/ML DeveloperHelps build applications that use machine learning models, often with additional programming and AI skills.

Salary Expectations for Machine Learning Engineers

Salaries of machine learning engineers in India may differ greatly according to the level of experience, company, location, and technical expertise of an engineer. The data provided below is approximate because various salary surveys and job postings demonstrate different numbers.

Experience LevelApproximate Annual Salary in India
Entry Level (0–2 years)₹5-10 lakh
Mid-Level (3–5 years)₹10-25 lakh
Experienced (5+ years)₹20-40 lakh or more

Who Is This Course For?

Intermediate Learners

Intermediate Learners

Designed for learners with basic knowledge who want to strengthen their practical skills and expertise

Career Growth Seekers

Career Growth Seekers

Learners looking to upgrade existing skills, gain hands-on expertise, and qualify for better job opportunities.

Curious Learners

Curious Learners

Anyone eager to explore this exciting field

What You'll Learn

Machine Learning Fundamentals: What is machine learning? What is the difference between machine learning and regular programming? What is learning based on the example? Applications of machine learning.
Data Preprocessing and Feature Engineering: Why do you need to preprocess and organize the data? What do feature selection and feature engineering contribute to the machine learning process?
Machine Learning Algorithms: What is the purpose of classification, regression and clustering algorithms? How do they solve different real-life problems such as segmentation, predicting values or classifying data?
Model Training and Testing: How does the machine learning algorithm learn from the training set? Why is testing important? What will we learn about the model from the evaluation?
Python Programming and ML Libraries: What is the role of the Python programming language and specific libraries in conducting data analysis and applying machine learning? Relevant libraries should be included in the Machine Learning Course syllabus.
Projects in Machine Learning: What is the process of machine learning project implementation, from defining the problem and analyzing the dataset to creating the model and its output? Practice explaining model predictions and model evaluation in the context of projects.

Technologies Covered in This Course

Power BI LlamaIndex Ollama

Career & Placement Support

Real professionals who accelerated their career growth and secured new opportunities with our Career & Placement Support.

Mock Session

Mock Interview Session

Practice Machine Learning Training with Python, Practical Projects and Certification interview questions, case studies, and technical scenarios with expert feedback.

LMS Learning

LMS Learning

Access Machine Learning Training with Python, Practical Projects and Certification recordings, Machine Learning Course exercises, datasets, assignments, and study materials anytime.

Career Support

Career Support

Get resume preparation, LinkedIn optimization, interview guidance, and job-search assistance.

Certified Expert

Trainer Experience & Expertise

An ideal trainer will help a learner to understand machine learning algorithms better through clear illustrations of concepts via examples and application in programming. In addition to learning the workings of an algorithm, it is also important to know why it can be chosen and how its performance can be evaluated. Before enrolling in any class, inquire about the expertise of the trainer in implementing machine learning algorithms.

  • Simplifies machine learning algorithms with relevant examples.
  • Assists learners with understanding data preprocessing and feature engineering.
  • Shows how classification, regression, and clustering algorithms are implemented in various tasks.
  • Illustrates model training and model evaluation processes, where relevant.
  • Facilitates the learner's completion of coding exercises and projects in Machine Learning Training.
  • Assists with understanding errors and investigating model results.
Your Learning Journey

Training Roadmap

A complete learning journey designed to build practical skills and prepare you for real-world opportunities.

01

Industry Expert Trainer

Learn Machine Learning Training with Python, Practical Projects and Certification from an experienced trainer through practical explanations and real-world examples. Build your skills through 180 hours of training.

02

Updated Learning Resources

Access updated presentation decks, training notes, practice files, and supporting resources used during live sessions, so you can revise every topic at your own pace and stay prepared for interviews.

03

Digital Study Material

Support your Machine Learning Training with Python, Practical Projects and Certification learning with structured digital materials, reference guides, and revision notes. Study materials are organized across 22 modules.

04

Practice & Assessments

Test your knowledge through module-wise assignments, practice exercises, and assessments. Reinforce your understanding of 198 topics covered in the curriculum.

05

Recorded Sessions

Revisit important concepts and catch up on missed classes with recorded sessions, where available. Choose from 3 batches scheduled each month.

06

Hands-On Projects

Apply your Machine Learning Training with Python, Practical Projects and Certification knowledge through practical projects and scenarios relevant to the course. 3 Live projects included.

07

Professional Resume Support

Create a professional resume that highlights your skills, practical work, and career goals with expert guidance with 200

08

Interview Preparation

Prepare for roles related to Machine Learning Training with Python, Practical Projects and Certification with mock interviews, relevant questions, practical scenarios, and trainer feedback.

Our Hiring Partners

Top companies actively hire from our alumni network.

tata-group
Cisco
Deloitte
Accenture
Wipro
Ganpact
TCS
Infosys
sap
Motherson
Tech Mahindra
Samsung
120+
Hiring Partners
85%
Placement Rate
₹12 LPA
Avg. Salary Offered

Course Curriculum

22 modules · 198 topics · 180h total

1 Introduction to Machine Learning 9 topics
Definition and scope of Machine Learning
AI vs ML vs Deep Learning
History, evolution and real-world applications
Supervised, unsupervised, semi-supervised learning
Reinforcement learning overview
Batch vs online learning; instance-based vs model-based learning
Problem framing and data understanding
Training, validation, testing and prediction
Common challenges, limitations and responsible AI
2 Python Programming for Machine Learning 9 topics
Environment setup: Python, Anaconda, Jupyter, Google Colab, VS Code
Variables, data types, operators and type conversion
Strings, lists, tuples, sets and dictionaries
Conditional statements and loops
Functions, lambda, map, filter and comprehensions
Modules, packages, file handling and exception handling
Object-oriented programming fundamentals
Virtual environments and pip package management
Reading and writing CSV, Excel, JSON and text files
3 Python Libraries for Data Science 9 topics
Arrays, dimensions, indexing and slicing
Reshaping, broadcasting, vectorization and aggregations
Linear algebra and random number utilities
Series and DataFrames; importing datasets
Filtering, sorting, grouping and aggregation
Merging, joining, reshaping and handling dates
Matplotlib charts and customization
Seaborn statistical visualizations
SciPy introduction and plotting best practices
4 Mathematics for Machine Learning 9 topics
Scalars, vectors, matrices and tensors
Matrix operations, dot product, transpose and inverse
Rank, determinant, eigenvalues and eigenvectors
Limits, derivatives and partial derivatives
Gradients, chain rule and optimization intuition
Gradient descent and learning-rate concepts
Loss and cost functions
Convexity intuition and local minima
Numerical optimization concepts
5 Statistics and Probability 9 topics
Mean, median, mode, range and percentiles
Variance, standard deviation, quartiles and IQR
Skewness, kurtosis and outlier interpretation
Probability rules and conditional probability
Bayes' theorem
Normal, binomial and Poisson distributions
Sampling and central limit theorem
Confidence intervals and hypothesis testing
p-values, correlation and covariance
6 Data Collection, Cleaning and Preprocessing 9 topics
Data sources, formats and data types
Data quality dimensions and data dictionaries
Duplicates, inconsistent values and missing-data patterns
Missing-value treatment and imputation
Outlier detection and treatment
Type conversion and data validation
Categorical encoding: one-hot, ordinal and target encoding
Normalization and standardization
Train-validation-test split, leakage and imbalanced data
7 Exploratory Data Analysis (EDA) 7 topics
Summary statistics and distribution analysis
Correlation and covariance analysis
Cross-tabulation and group-wise analysis
Feature relationships and target analysis
Histograms, box plots, scatter plots and heatmaps
Anomaly and outlier visualization
Insight storytelling and EDA reporting
8 Feature Engineering and Feature Selection 9 topics
Feature creation, transformation and binning
Date/time, text-derived and interaction features
Polynomial features and domain-driven features
One-hot, ordinal and target encoding
Scaling and transformation choices
Handling rare categories
Filter, wrapper and embedded methods
Variance threshold, correlation and mutual information
Recursive feature elimination and feature importance
9 Supervised Learning – Regression 9 topics
Regression problem framing and assumptions
Simple and multiple linear regression
Ordinary least squares and cost functions
Polynomial regression
Decision tree and random forest regression
K-nearest neighbors and support vector regression
Ridge, Lasso and Elastic Net
Gradient descent for regression
Residual analysis, MAE, MSE, RMSE and R-squared
10 Supervised Learning – Classification 10 topics
Logistic regression
K-nearest neighbors and Naive Bayes
Decision trees and support vector machines
Random forest and SGD classifier
Binary, multiclass and multilabel classification
Probability scores and decision thresholds
Class imbalance and resampling overview
Confusion matrix
Accuracy, precision, recall, specificity and F1-score
ROC curve, ROC-AUC, precision-recall curve
11 Model Evaluation and Validation 9 topics
Overfitting and underfitting
Bias-variance trade-off
Baseline models and generalization
Holdout validation and k-fold cross-validation
Stratified k-fold and time-series split
Learning curves and validation curves
Metric selection based on business objective
Error analysis and calibration basics
Reproducible model comparison
12 Unsupervised Learning 9 topics
K-means clustering and choosing K
Elbow method and silhouette score
Hierarchical clustering and DBSCAN
Gaussian mixture models
Isolation Forest and Local Outlier Factor
Cluster profiling and interpretation
Frequent itemsets and Apriori algorithm
Support, confidence and lift
Market-basket analysis use cases
13 Dimensionality Reduction 9 topics
Curse of dimensionality
Feature selection vs feature extraction
Explained variance and component selection
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
t-SNE and UMAP overview
Scaling requirements and interpretation
2D/3D visualization of high-dimensional data
Limitations and use cases
14 Ensemble Learning and Boosting 9 topics
Ensemble learning principles
Voting and averaging
Bagging and random forests
AdaBoost
Gradient Boosting
XGBoost, LightGBM and CatBoost
Extra Trees
Stacking and blending
Feature importance and overfitting control
15 Hyperparameter Optimization 9 topics
Parameters vs hyperparameters
Manual tuning and search-space design
GridSearchCV and RandomizedSearchCV
Successive halving concepts
Bayesian optimization overview
Optuna introduction
Cross-validation during tuning
Nested cross-validation overview
Experiment tracking and reproducibility
16 Time Series Machine Learning 9 topics
Trend, seasonality, cycles and residuals
Stationarity concepts and decomposition
Time-aware train-test splitting
Lag and rolling-window features
Regression and tree-based forecasting
Forecasting metrics: MAE, RMSE and MAPE
Baseline and naive forecasting
ARIMA and SARIMA overview
Prophet overview and leakage prevention
17 Natural Language Processing for ML 9 topics
Text cleaning and normalization
Tokenization, stop words and n-grams
Stemming and lemmatization
Bag of Words
TF-IDF
Word embeddings overview
Text classification and spam detection
Sentiment analysis
NLTK, spaCy and transformer concepts overview
18 Introduction to Deep Learning 9 topics
Perceptron, neurons and layers
Activation and loss functions
Forward propagation and backpropagation intuition
Optimizers: SGD and Adam
Epochs, batch size and learning rate
Regularization, dropout and normalization
TensorFlow/Keras or PyTorch fundamentals
Building and training a feed-forward network
GPU basics and model saving
19 Explainable AI and Model Interpretation 9 topics
Global vs local explanations
Coefficients and model-based feature importance
Permutation importance and partial dependence
SHAP concepts and interpretation
LIME concepts and use cases
Explaining individual predictions
Error analysis and limitations
Fairness and bias checks
Model cards and documentation
20 ML Pipelines, Deployment and MLOps 13 topics
scikit-learn Pipeline and ColumnTransformer
Reusable preprocessing and inference pipelines
Model serialization with joblib
REST API fundamentals
Deploy with FastAPI or Flask
Interactive demos with Streamlit
Git and GitHub workflow
Docker fundamentals
MLflow experiment tracking and model registry concepts
CI/CD overview, monitoring, data drift and retraining
Cloud ML deployment overview: AWS, Azure and Google Cloud
Model security, access and versioning
Production reliability and governance
21 Responsible Machine Learning 6 topics
Privacy, consent and data minimization
Fairness, bias sources and mitigation overview
Human oversight and responsible use
Explainability and transparency
Security and adversarial robustness overview
Documentation, compliance awareness and model limitations
22 Capstone Projects and Portfolio 9 topics
Choose a business problem and define success metrics
Source data and prepare a data dictionary
Create project plan and baseline
EDA, preprocessing and feature engineering
Model training, validation and hyperparameter tuning
Interpretation, error analysis and deployment
Prepare README, code and technical documentation
Build a demo and presentation
Discuss limitations, impact and future improvements

Salary Range & Job Opportunities

Roles our alumni land after this course

Senior Consultant

₹ 10–22 LPA

Accenture Deloitte TCS

Project Manager

₹ 12–25 LPA

Wipro Infosys IBM

Solution Architect

₹ 18–35 LPA

Capgemini PwC KPMG

87%

Placement Rate

₹ 3.5L

Avg. Salary Hike

45 days

Avg. Time to Hire

Student Reviews

4.8

Course Rating

5
62%
4
25%
3
9%
2
3%
1
1%
485

Total reviews

🏆

Certificate of Completion

Machine Learning Training with Python, Practical Projects and Certification

Corporate Academy

Awarded upon successful completion

Industry-Recognised Certificate

Earn Your Certificate

Finish every module and project to earn the Corporate Academy course completion certificate. Share the Corporate Academy certificate on LinkedIn attach it to your resume and display this Corporate Academy achievement to recruiters, across India and worldwide.

  • Machine Learning Training with Python, Practical Projects and Certification course completion certificate

  • Share your achievement on LinkedIn and your resume

  • Highlight your completed training and newly learned skills

  • Add your certificate to your professional portfolio

FAQs for Machine Learning Course

Do I require previous knowledge of Python and mathematics for enrollment?

It will be easier for you to learn machine learning if you know Python programming and have basic knowledge of statistics. If you are a beginner, enquire whether the Machine Learning Classes begin from scratch or assume that you have some experience in coding.

Which kind of projects should I look for in a machine learning class?

You should look for projects when you develop a dataset, choose an algorithm, develop the model and verify its performance. The projects could be predicting house prices, clustering customers, or classifying messages.

Is a Machine Learning Certification a guarantee of a job?

No, it is not. A certificate means that you passed a Machine Learning Course or met its completion criteria. But the employer can also check your coding skills, your knowledge of algorithms, projects and explanation of your work.

What is the difference between machine learning and deep learning?

Machine learning involves a variety of processes through which computers learn from data. Please make sure that deep learning and neural networks are included in this particular Machine Learning Course before including them.

What should I know before joining the Machine Learning Course Training?

Make sure you have the latest syllabus, the duration of the training, the fee, class timings, the training process, and information on practical projects. Get to know which Python packages will be taught during the training.

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Format

Live Online Class with Recording, Online One-to-One Class, Weekend Class

Duration

180h of content

Access

Lifetime

Certificate

Included on completion

Language

English · Hindi subtitles

Enrolled

715

Upcoming Batches

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