Machine Learning Course

Lesson 1: Essential Mathematics (20 hours)

·       Include Linear Algebra which refers to familiarity with integrals, differentiation, differential equations, etc.
·       Statistics including Inferential Statics, Descriptive Statistics, Chi-Squared Tests, Random Variable, Gaussian and Normal Distributions, etc.
·       Probability like Bayes Theorem, Optimization like Convex Optimization, etc.
 

Lesson 2: Introduction to Python (20 hours)

Mastering a programming language is highly necessary to pursue Data Science. Python version 3 is strongly recommended.
·       Python IDE
·       Understanding Operators
·       Variables and Data Types
·       Conditional Statements
·       Looping Constructs
·       Python Control structures
·       Function Modules
·       Functions
·       Data Structure
·       Lists
·       Dictionaries
·       Exception and file Handling
·       Handson Project
 

Lesson 3: Python Libraries for Data Science (10 hours)

·       Understanding Standard Libraries and packages like Matplotlib
·       Numpy
·       Pandas & Scipy
·       Seaborn & Scikit-Learn
·       BeautifulSoup, Bokeh, Urllib, etc.
·       Reading a CSV File in Python
·       Data Frames and basic operations with Data Frames
·       Indexing a Data Frame
·       Anaconda distribution
 

Lesson 4: Common Machine Learning Algorithms and Introduction to AI (30 hours)

·       Supervised, Unsupervised and Reinforcement Learning
·       Linear Regression, Logistic Regression, Decision Tree
·       K-Means, Random Forest
·       Seaborn & Scikit-Learn
·       Dimensionality Reduction Algorithms – PCA
·       Gradient Boosting algorithms - GBM, XGBoost, LightGBM, CatBoost

Lesson 5: Deep Learning (20 hours)

·       Artificial Neural Networks
·       Neurons, ANN & Working
·       Single Layer Perceptron Model
·       Multi-layer Neural Network
·       Cost Function Formation
·       Applying Gradient Descent Algorithm
·       Back-propagation Algorithm & Mathematical Modelling
·       Use Cases of ANN
 

Lesson 6: Introduction to NLP (20 hours)

    Text Preprocessing

·       Noise Removal

·       Lexicon Normalization

·       Lemmatization

·       Stemming

·       Object Standardization

    Text to Features (Feature Engineering on text data)

·       Syntactic Parsing - Dependency Grammar, Part of Speech (POS) Tagging

·       Entity Parsing - Phrase Detection, Named Entity Recognition, Topic Modelling, N-Grams

·       Statistical features - TF - IDF, Frequency / Density Features, Readability Features

·       Word Embeddings

    Important tasks of NLP

·       Text Classification

·       Text Matching - Levenshtein Distance, Phonetic Matching, Flexible String Matching

·       Co reference Resolution - document summarization, question answering, and information extraction

·       Other NLP problems / tasks - Text Summarization, Machine Translation, NLG/NLU, OCR, Document to Information

    Important NLP libraries

·       Scikit-learn: Machine learning in Python

·       Natural Language Toolkit (NLTK): The complete toolkit for all NLP techniques

·       Pattern – A web mining module for the with tools for NLP and machine learning

·       TextBlob – Easy to use NLP tools API, built on top of NLTK and Pattern

·       spaCy – Industrial strength NLP with Python and Cython

·       Gensim – Topic Modelling for Humans

·       Stanford Core NLP – NLP services and packages by Stanford NLP Group

Lesson 7: CNN and RNN (20 hours)

·       Convolutional Neural Networks (CNN)
·       Introduction to CNNs
·       CNNs Application
·       Architecture of a CNN
·       Convolution and Pooling layers in a CNN
·       Understanding and Visualizing a CNN
·       Image classification using Keras deep learning library
·       Recurrent Neural Networks (RNN)
·       Intro to RNN Model
·       Application use cases of RNN
·       Training RNNs with Backpropagation
·       Long Short-Term memory (LSTM)
·       Recurrent Neural Network Model
·       Deep Learning Frameworks
 

Lesson 8: Deep Learning Frameworks and Tensorflow (10 hours)

·       Introducing Tensors
·       Plane Vectors
·       Tensors
·       Installing TensorFlow
·       Getting Started with TensorFlow: Basics
·       To build a neural network and how to train, evaluate and optimize it with TensorFlow
·       TensorFlow Core: The main TensorFlow library which are widely popular for deep learning implementations
·       Keras: Keras apis with TensorFlow backend only
·       TensorFlow Lite: Library for mobile/embedded device based lightweight solutions
·       TFX: TensorFlow Extended, a production scale platform for implementing end to end machine learning solutions. It is available on GitHub via 4 repos
·       TensorFlow Transform, TensorFlow Model Analysis, TensorFlow Serving, TensorFlow Data Validation
 

Lesson 9: Capstone Project (20 hours)

·       Belgian Traffic Signs: Background/ MNIST/CIFAR Dataset
·       Loading and Exploring the Data
·       Traffic Sign Statistics
·       Visualizing the Traffic Signs
·       Feature Extraction
·       Re-scaling Images
·       Deep Learning with TensorFlow
·       Modeling The Neural Network
·       Running the Neural Network
·       Evaluating the Neural Network