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Professional Machine Learning

  • (395 Reviews)

Machine learning course cover algorithms and concepts for enabling computers to learn from data and make decisions without explicit programming.

Durattion: 6 Months
100+ Recruitment Partners
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Skill Level

Beginner

Time to Complete

6 Months

Projects

50+ Projects

Prerequisites

None

About Machine Learning

Our Machine Learning course equips you with the skills to build predictive models using Python, TensorFlow, and Scikit-Learn. Learn data preprocessing, algorithms, deep learning, and model deployment. Gain hands-on experience through real-world projects and become job-ready with expert guidance and career support.

Tools You Will Learn in Machine Learning
COURSE CURRICULUM

Master Skills with Detailed Curriculum

1
Module 1: Introduction to Machine Learning
What is Machine Learning?
Supervised vs. Unsupervised Learning
Real-world applications of Machine Learning
Setting up the development environment (Python, R, Jupyter Notebook, Google Colab)
2
Module 2: Mathematics & Statistics for Machine Learning
Linear Algebra basics (vectors, matrices, eigenvalues)
Probability and statistics
Hypothesis testing and distributions
Gradient descent and optimization techniques
3
Module 3: Data Preprocessing & Feature Engineering
Data cleaning and handling missing values
Feature scaling (Normalization & Standardization)
Encoding categorical variables
Feature selection and dimensionality reduction (PCA, LDA)
4
Module 4: Supervised Learning Algorithms
Linear Regression
Logistic Regression
Decision Trees & Random Forest
Support Vector Machines (SVM)
Naive Bayes
K-Nearest Neighbors (KNN)
5
Module 5: Unsupervised Learning Algorithms
Clustering techniques (K-Means, DBSCAN, Hierarchical Clustering)
Principal Component Analysis (PCA)
Association rule learning (Apriori, Eclat)
6
Module 6: Neural Networks & Deep Learning
Introduction to Artificial Neural Networks (ANN)
Deep Learning with TensorFlow & Keras
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN) & Long Short-Term Memory (LSTM)
7
Module 7: Natural Language Processing (NLP)
Text preprocessing (Tokenization, Lemmatization, Stemming)
Sentiment Analysis
Named Entity Recognition (NER)
Transformers and BERT
8
Module 8: Model Evaluation & Optimization
Performance metrics (Accuracy, Precision, Recall, F1-Score)
Cross-validation techniques
Hyperparameter tuning (Grid Search, Random Search)
Bias-variance tradeoff
9
Module 9: Big Data & Machine Learning
Working with large datasets
Machine Learning with Apache Spark (MLlib)
Cloud-based ML with AWS, Google Cloud, and Azure
10
Module 10: Real-World Projects & Case Studies
Hands-on projects (predictive analytics, recommendation systems, image classification)
Industry case studies
Resume building and job interview preparation

Why Get Our Machine Learning Certification

Doubt Clearing Sessions
Industry Relevant Projects
Assignment Evaluation & Solutions
PW Lab For Your Code Practice
Industry Experts Trainers
Career Guidance Sessions
JOB ASSISTANCE PROGRAM

Fast-Track Your Career with Job Assistance Program

Soft Skills Sessions
Aptitude Training
Resume Building Sessions
LinkedIn Profile Building
Mock Interviews
Job Assistance
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How it Works?

Choose a programme of your interest
Receive personal counselling
Complete application & payment
Get certified & qualify for placement
ELIGIBILITY CRITERIA

Understand the Eligibility Criteria for Course Enrollment

  • B.E./B.Tech - Computer Science, Information Technology,Electronics, Electronics & Telecommunications, Electrical, Electrical & Electronics
  • BSc/MSc - Computer Science, Information Technology
  • BCA/MCA
  • Final Year Students - Expecting to Graduate in 2023
  • Fresh Graduates - Graduated in 2021/2022
  • Working Professionals - Graduated in 2020
FREQUENTLY ASKED QUESTIONS

Most Popular Questions About Our Online Courses

A Machine Learning course teaches you how to build predictive models using algorithms, data analysis, and AI techniques to automate decision-making processes.

Anyone interested in Machine Learning can enroll. It is ideal for students, data analysts, software engineers, and professionals looking to switch to AI and ML roles

Basic knowledge of Python or R is helpful, but beginner-friendly courses start from the fundamentals.

The course primarily uses Python and R for implementing Machine Learning models.

You will learn TensorFlow, PyTorch, Scikit-Learn, Pandas, NumPy, Matplotlib, and cloud platforms like AWS and Google Cloud.

You will learn data preprocessing, model building, supervised and unsupervised learning, deep learning, NLP, and model deployment techniques.

Yes, upon successfully completing the course, you will receive an industry-recognized certification.

Yes, the course includes real-world projects such as fraud detection, recommendation systems, image classification, and NLP applications.

You can enroll through the course provider’s website by filling out the application form and meeting the eligibility requirements.