We present a cosmography analysis of the local Universe based on the recently released Two-Micron All-Sky Redshift Survey catalogue. Our method is based on a Bayesian Networks Machine Learning algorithm (the Kigen-code) which self-consistently samples the initial density fluctuations compatible with the observed galaxy distribution and a structure formation model given by second-order Lagrangian perturbation theory (2LPT). From the initial conditions we obtain an ensemble of reconstructed density and peculiar velocity fields which characterize the local cosmic structure with high accuracy unveiling non-linear structures like filaments and voids in detail. Coherent redshift-space distortions are consistently corrected within 2LPT. From the ensemble of cross-correlations between the reconstructions and the galaxy field and the variance of the recovered density fields, we find that our method is extremely accurate up to k∼ 1 h Mpc−1 and still yields reliable results down to scales of about 3–4 h−1 Mpc. The motion of the Local Group we obtain within ∼80 h−1 Mpc (vLG = 522 ± 86 km s−1, lLG = 291° ± 16°, bLG = 34° ± 8°) is in good agreement with measurements derived from the cosmic microwave background and from direct observations of peculiar motions and is consistent with the predictions of ΛCDM.