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Machine learning Course

Online Training Available

Experience the best Machine Learning Course under experienced trainers and an advanced curriculum. The course is designed for beginners so that they can learn from scratch.

JOB ASSISTANCE :

Yes

MINIMUM ELIGIBILITY :

10th pass / 12th pass / Graduate

COURSE DURATION :

6 Months

MODE OF TRAINING :

Online

CERTIFICATION :

Yes

CALL US TODAY :

+91 9163 883 143

COURSE FEES :

For Indian Students : Rs. 65000 /- (Easy Installments available)

For Foreign Students : 1049 USD (Easy Installments available)

Key Highlights of the Machine Learning Course at Webcram Career Academy

Welcome to Webcram Career Academy

Machine Learning Course

Syllabus Details

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Contents

Module 1: Introduction to Machine Learning
1
Module 2: Data Preprocessing
1
Module 3: Supervised Learning Algorithms
2
Module 4: Model Evaluation and Validation
2
Module 5: Unsupervised Learning Algorithms
3
Module 6: Dimensionality Reduction and Feature Selection
3
Module 7: Neural Networks and Deep Learning
4
Module 8: Natural Language Processing (NLP)
4
Module 9: Reinforcement Learning
5
Module 10: Deploying Machine Learning Models
5
1

Introduction to Machine Learning

  • Overview of machine learning: history, applications, and types
  • Machine learning vs. traditional programming
  • Introduction to HTML, CSS, and JavaScript
  • Introduction to Python programming for machine learning (NumPy, Pandas)

Data Preprocessing

  • Exploratory data analysis (EDA) techniques
  • Data cleaning: handling missing values and outliers
  • Feature scaling and normalization
2

Supervised Learning Algorithms

  • Linear regression: simple and multiple regression
  • Logistic regression and classification metrics
  • Support Vector Machines (SVM): classification and regression

Model Evaluation and Validation

  • Training, testing, and validation datasets
  • Evaluation metrics: accuracy, precision, recall, F1 score, ROC curve
  • Hyperparameter tuning and model selection techniques
3

Unsupervised Learning Algorithms

  • K-Means clustering: centroid-based clustering
  • Hierarchical clustering: agglomerative and divisive clustering
  • Principal Component Analysis (PCA): dimensionality reduction

Dimensionality Reduction and Feature Selection

  • Feature selection techniques: filter, wrapper, and embedded methods
  • Principal Component Analysis (PCA) for dimensionality reduction
  • Singular Value Decomposition (SVD) and its applications
4

Neural Networks and Deep Learning

  • Introduction to Artificial Neural Networks (ANN)
  • Training neural networks: forward propagation, backpropagation
  • Deep learning frameworks: TensorFlow and Keras

Natural Language Processing (NLP)

  • Text preprocessing techniques: tokenization, stemming, lemmatization
  • Named Entity Recognition (NER) and sentiment analysis
  • Word embeddings: Word2Vec and GloVe
5

Reinforcement Learning

  • Introduction to reinforcement learning: agents, environments, and rewards
  • Markov Decision Processes (MDP) and Bellman equations
  • Q-Learning: value iteration and policy iteration

Deploying Machine Learning Models

  • Model serialization and deployment considerations
  • Building APIs for machine learning models using Flask or FastAPI
  • Containerizing machine learning models with Docker
6

Ethical and Responsible AI

  • Bias and fairness in machine learning models
  • Ethical considerations in data collection and usage
  • Transparency, interpretability, and accountability in AI

Capstone Project

  • Applying machine learning concepts to solve a real-world problem
  • Project planning, data collection, and preprocessing
  • Model selection, training, and evaluation

Thank you!

We would be glad to assist you further if you have any questions or need guidance as you progress through your Machine Learning course.

Whether it's about understanding concepts, troubleshooting code, or exploring advanced topics, feel free to reach out. Best of luck with your studies, and enjoy the learning journey!

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A Course By Webcram Career Academy

Material Includes

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Talk To Our Counsellor

Mon - Sat : 10am To 7Pm

Phone (For Voice Call) :

+ 91 8017 058 403 (IND)

WhatsApp (For Call & Chat) :

+ 91 9163 883 143 (IND)

Course Objectives

This machine learning course aims to teach students algorithms, techniques, and tools for building predictive models and solving real-world problems.

Pre-requisities

Before enrolling in a machine learning course, proficiency in programming (Python recommended), statistics, and linear algebra fundamentals is essential.

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