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Machine Learning Course

90 Hours
Online Instructor-led Training
USD 2800
Machine Learning Course course and certification
725 Learners

About this Course
Machine Learning focuses on creating algorithms to find patterns and make predictions from experimental data. The vast expanding field of Machine Learning has its used within a vast range of applications & fields such as Intelligent Systems, Computer Vision, Speech Recognition, Natural Language Processing, Robotics, Finance, Information Retrieval, Healthcare, Weather prediction.


This intense 90-hours Machine Learning course by Uplatz helps you develop theoretical and practical fundamentals required to be at the front of progress in the next technical revolution.


Objectives of Machine Learning Course

  • Master the details of Python Programming
  • Clearly understand the Machine Learning concepts and its application to real-world
  • Understand the concept of Deep Learning, Natural Language Processing, Graphical modelling and Reinforcement learning
  • Understand the theory underlying machine learning algorithms
  • Use machine learning to make decisions and predictions
  • Select appropriate statistical and predictive methodologies

Machine Learning Capstone Project
(it is an additional project that you will do as part of this course)

Social Media Sentiment Analysis using Twitter Dataset
Social media platforms like Twitter, Facebook, YouTube, Reddit generate huge amounts of big data that can be mined in various ways to understand trends, public sentiments, and opinions. Social media data today has become relevant for branding, marketing, and business as a whole. A sentiment analyzer learns about various sentiments behind a “content piece” (could be IM, email, tweet or any other social media post) through machine learning and predicts the same using AI.Twitter data is considered as a definitive entry point for beginners to practice sentiment analysis machine learning problems. Using Twitter dataset, one can get a captivating blend of tweet contents and other related metadata such as hashtags, retweets, location, users and more which pave the way for insightful analysis.
Problem Statement: The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.


Machine Learning Course

Course Details & Curriculum

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


Machine Learning Certification

Uplatz's Machine Learning training and certification program gives you a solid understanding of the key topics of Machine Learning. In addition to boosting your income potential, getting certified in Machine Learning will demonstrate your knowledge of the skills necessary to be an effective Machine Learning professional. The certification validates your ability to produce reliable, high-quality results with increased efficiency and consistency. At the end of this machine learning course, you will get a chance to work on a capstone project to gain hands-on capability.


Career Path

1) Data Scientist
2) Senior Data Scientist
3) Machine Learning Professional

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