Aspire Journeys

ML Programmer to ML Architect

  • 76 Courses | 71h 49m 43s
  • 4 Labs | 30h
  • Includes Test Prep
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Machine Learning Architects interpret real-time analysis of data to automate and increase efficiency across all business domains, setting the stage for meaningful AI that moves from reactive to predictive. This Skillsoft Aspire journey will guide you in the transition from becoming a DL Programmer to a ML/DL Architect Master through mechanisms such as computational theory.

Track 1: ML Programmer

In this track of the machine learning Skillsoft Aspire journey, the focus is linear regression, computational theory, and training sets.

  • 25 Courses | 21h 55m 15s
  • 1 Lab | 8h

Track 2: DL Programmer

In this track of the machine learning Skillsoft Aspire journey, the focus is neural networks, CNNs, RNNs, and ML algorithms.

  • 22 Courses | 21h 19m 49s
  • 1 Lab | 8h

Track 3: ML Engineer

In this track of the machine learning Skillsoft Aspire journey, the focus is predictive modeling and analytics, ml modeling, and ml architecting.

  • 17 Courses | 16h 13m 15s
  • 1 Lab | 8h

Track 4: ML Architect

In this track of the machine learning Skillsoft Aspire journey, the focus is applied predictive modeling, CNNs and RNNs, and ML algorithms.

  • 12 Courses | 12h 21m 24s
  • 1 Lab | 6h


NLP for ML with Python: NLP Using Python & NLTK
This course explores how natural language processing (NLP) is used for machine learning, and examines the benefits and challenges of NLP when creating an application that can essentially understand human language. In its 13 videos, learners will be shown the essential components of NLP, including parsers, corpus, and corpus linguistic, as well as how to implement regular expressions. This course goes on to examine tokenization, a way to separate a piece of text into smaller units, and then illustrates different tokenization use cases with NLTK (Natural Language Toolkit). You will learn to use stop words using libraries and the NLTK. This course demonstrates how to implement regular expressions in Python to build NLP-powered applications. Learners will examine the list of Python NLP libraries along with their essential capabilities, including NLTK, Gensim, CoreNLP, spaCy and PyNLPl. You will learn to set up and configure an NLTK environment to illustrate how to process raw text. Finally, this course demonstrates the use of filtering stopwords in a tokenized sentence using NLTK.
13 videos | 1h has Assessment available Badge
NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn
This 11-video course explores NLP (natural language processing) by discussing differences between stemming, a process of reducing a word to its word stem, and lemmatization, or returning the base or dictionary form of a word. Key concepts covered here include how to extract synonyms, antonyms, and topic, and how to process and analyze texts for machine learning. You will learn to use Apache's Natural Language Toolkit (NLTK), spaCy, and Scikit-learn to implement text classification and sentiment analysis. This course demonstrates the use of advanced calculus and discrete optimization to implement robust and high-performance machine learning applications. You will learn to use R and Python to implement multivariate calculus for machine learning and data science, then examine the role of probability, variance, and random vectors in implementing machine learning processes and algorithms. Finally, you will examine the role of calculus in deep learning; watch a demonstration of how to apply calculus and differentiation using R and Python libraries; see how to implement calculus, derivatives, and integrals using Python; and learn uses of limits and series expansions in Python.  
11 videos | 44m has Assessment available Badge
Linear Algebra and Probability: Fundamentals of Linear Algebra
Explore the fundamentals of linear algebra, including characteristics and its role in machine learning, in this 13-video course. Learners can examine important concepts associated with linear algebra, such as the class of spaces, types of vector space, vector norms, linear product vector and theorems, and various operations that can be performed on matrix. Key concepts examined in this course include important classes of spaces associated with linear algebra; features of vector spaces and the different types of vector spaces and their application in distribution and Fourier analysis; and inner product spaces and the various theorems that are applied on inner product spaces. Next, you will learn how to implement vector arithmetic by using Python; learn how to implement vector scalar multiplication with Python; and learn the concept and different types of vector norms. Finally, learn how to implement matrix-matrix multiplication, matrix-vector multiplication, and matric-scalar multiplication by using Python; and learn about matrix decomposition and the roles of Eigenvectors and Eigenvalues in machine learning.
13 videos | 1h has Assessment available Badge
Linear Algebra & Probability: Advanced Linear Algebra
Learners will discover how to apply advanced linear algebra and its principles to derive machine learning implementations in this 14-video course. Explore PCA, tensors, decomposition, and singular-value decomposition, as well as how to reconstruct a rectangular matrix from singular-value decomposition. Key concepts covered here include how to use Python libraries to implement principal component analysis with matrix multiplication; sparse matrix and its operations; tensors in linear algebra and arithmetic operations that can be applied; and how to implement Hadamard product on tensors by using Python. Next, learn how to calculate singular-value decomposition and reconstruct a rectangular matrix; learn the characteristics of probability applicable in machine learning; and study probability in linear algebra and its role in machine learning. You will learn types of random variables and functions used to manage random numbers in probability; examine the concept and characteristics of central limit theorem and means and learn common usage scenarios; and examine the concept of parameter estimation and Gaussian distribution. Finally, learn the characteristics of binomial distribution with real-time examples.
14 videos | 1h has Assessment available Badge
Linear Regression Models: Introduction
Machine learning (ML) is everywhere these days, often invisible to most of us. In this course, you will discover one of the fundamental problems in the world of ML: linear regression. Explore how this is solved with classic ML as well as neural networks. Key concepts covered here include how regression can be used to represent a relationship between two variables; applications of regression, and why it is used to make predictions; and how to evaluate the quality of a regression model by measuring its loss. Next, learn techniques used to make predictions with regression models; compare classic ML and deep learning techniques to perform a regression; and observe various components of a neural network and how they fit together. You will learn the two types of functions used in a neuron and their individual roles; how to calculate the optimal weights and biases of a neural network; and how to find the optimal parameters for a neural network.
13 videos | 1h has Assessment available Badge
Linear Regression Models: Building Models with Scikit Learn & Keras
Learn how to use the Scikit Learn and Keras libraries to build a linear regression model to predict a house price. This course reviews the steps needed to prepare data and configure regression models. It shows how to prepare a data set to feed a linear regression model; how to use the Pandas library to load a CSV data set file; and how to configure, train, and validate linear regression models. The course also shows how to visualize metrics with Matplotlib; how to prepare data for a Keras model, how to learn the architecture for a Keras sequential model and initialize it; and finally, how train it to use optimal weights and biases for machine learning solutions.
9 videos | 45m has Assessment available Badge
Linear Regression Models: Multiple & Parsimonious
Several factors usually influence an outcome, and users need to consider all of those by using regression. Regression models help us mathematically evaluate our hunches. This course