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Machine Learning: Level-02

Machine Learning Track

Machine Learning:
Level-02

4.8 (1,230 reviews)

Introduction to Machine Learning: Level-02. Master deep learning, NLP, and scalable ML systems. Build real-world AI solutions for domains like healthcare, finance, and IoT.

Created by Avanteia
12,580 Total Enrolled
15 September 2024 Last Updated
Enroll Now
Machine Learning Level-02 Course
3 Months Duration
Certificate On Completion
Level-02 Level
12 Modules Syllabus
3 Months Duration
English Language
Certificate Included
Introduction

Overview

Master deep learning, NLP, and scalable ML systems. Build real-world AI solutions for domains like healthcare, finance, and IoT.

Deep Learning CNNs NLP RNNs Deployment Ethics
Goal

Learning Outcome

Master advanced machine learning techniques, build complex models for NLP and computer vision, and deploy them in real-world applications while considering ethical issues.

Curriculum

Syllabus

Click any module to expand and view topics and hands-on labs included.

  • What is ML? Types (Supervised, Unsupervised, Reinforcement)
  • ML vs AI vs Deep Learning
  • Real-world applications
  • ML workflow & pipeline
Hands-on Lab
Install Python & Jupyter/Colab Run a basic ML pipeline on Iris dataset in Scikit-learn
  • Python essentials (functions, OOP, file handling)
  • NumPy, Pandas for data manipulation
  • Matplotlib, Seaborn for visualization
Hands-on Lab
Load CSV dataset in Pandas Perform summary statistics Plot graphs using Matplotlib/Seaborn
  • Data cleaning (missing values, outliers)
  • Categorical encoding (One-hot, Label Encoding)
  • Scaling (MinMax, StandardScaler)
  • Feature selection & extraction
Hands-on Lab
Handle missing values in Titanic dataset Apply feature scaling on dataset in Scikit-learn
  • Probability basics, Bayes Theorem
  • Distributions (Normal, Bernoulli, Binomial, Poisson)
  • Hypothesis testing (t-test, chi-square)
Hands-on Lab
Simulate coin toss & dice using Python Test significance using SciPy
  • Linear Regression, Multiple Regression
  • Polynomial Regression
  • Regularization (Lasso, Ridge)
Hands-on Lab
Predict house prices using Linear Regression (Kaggle dataset) Compare Ridge vs Lasso regression
  • Logistic Regression
  • k-Nearest Neighbors (k-NN)
  • Decision Trees
  • Support Vector Machines (SVM)
Hands-on Lab
Predict Titanic survival (Logistic Regression) Build k-NN classifier on Iris dataset
  • Random Forest, Gradient Boosting, XGBoost
  • Bagging vs Boosting vs Stacking
  • Hyperparameter tuning (GridSearchCV, RandomSearchCV)
Hands-on Lab
Use Random Forest on loan prediction dataset Perform hyperparameter tuning with GridSearch
  • k-Means Clustering
  • Hierarchical Clustering
  • PCA & Dimensionality Reduction
Hands-on Lab
Perform customer segmentation using K-Means (Retail dataset) Apply PCA on Wine dataset
  • Basics of Neural Networks
  • Forward & Backward Propagation
  • Activation functions
  • Introduction to TensorFlow & PyTorch
Hands-on Lab
Build a simple ANN in TensorFlow/Keras Classify MNIST digits
  • Convolutional Neural Networks (CNNs) for image recognition
  • Recurrent Neural Networks (RNNs), LSTMs for sequences
  • Transfer Learning basics
Hands-on Lab
Build CNN for CIFAR-10 image classification Train RNN for text prediction
  • Text preprocessing (tokenization, stemming, lemmatization)
  • Word embeddings (Word2Vec, GloVe, BERT basics)
  • Sentiment analysis
Hands-on Lab
Build sentiment analysis model using NLTK & Scikit-learn Use pre-trained BERT for text classification in HuggingFace
  • ML model deployment techniques
  • Flask & Streamlit for deployment
  • ML on Cloud (Google Colab, Streamlit Cloud, HuggingFace Spaces)
Hands-on Lab
Deploy a classification model on Streamlit Cloud End-to-End project: Choose dataset → Clean → Train → Deploy
Results

What You Will Learn

Deep Learning Architectures

Build and train CNNs, RNNs, and LSTMs using TensorFlow and PyTorch for image and sequence processing.

Natural Language Processing

Process text data, build word embeddings, and deploy BERT-based models for sentiment analysis and classification.

Model Deployment

Deploy ML models using Flask, Streamlit, and cloud platforms for production-ready AI applications.

Ethics & Bias

Understand ethical considerations, bias detection, and responsible AI practices in machine learning systems.

Testimonials

What Our Students Say

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